Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study...Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.展开更多
Effective management of water resources,especially groundwater,is crucial and requires a precise understanding of aquifer characteristics,imposed stresses,and the groundwater balance.Simulation-optimization models pla...Effective management of water resources,especially groundwater,is crucial and requires a precise understanding of aquifer characteristics,imposed stresses,and the groundwater balance.Simulation-optimization models plays a vital role in guiding planners toword sustainable long-term aquifer exploita-tion.This study simulated monthly water table variations in the Kashan Plain over a ten-year period from 2008 to 2019 across 125 stress periods using the GMS model.The model was calibrated for both steady-state and transient conditions for the 2008–2016 period and validated for the 2016–2019 period.Results indicated a 4.4 m decline in groundwater levels over the 10-year study period.Given the plain's location in a arid climatic zone with limited effective precipitation for aquifer recharge,the study focused on ground-water extraction management.A modified two-point hedging policy was employed as a solution to mitigate critical groundwater depletion,reducing the annual drawdown rate from 0.44 m to 0.31 m and conserving 255 million cubic meters(mcm)of water annually.Although this approach slightly decreased reliability(i.e.the number of months meeting full water demands),it effectively minimized the risk of severe droughts and irreparable damages.This policy offers managers a dynamical and intelligent tool for regulating groundwater extraction,balancing aquifer sustainability with agricultural and urban water requirements.展开更多
Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 3...Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.展开更多
Since incorrect site selection has sometimes led to the failure of artificial recharge projects,it is necessary to increase the effectiveness of such projects and minimize their failure by employing new techniques.The...Since incorrect site selection has sometimes led to the failure of artificial recharge projects,it is necessary to increase the effectiveness of such projects and minimize their failure by employing new techniques.Therefore,the present research used a combination of decision-making models,numerical groundwater modeling and clustering technique to determine suitable sites for implementation of an artificial recharge project.This hybrid approach was employed for the Yasouj aquifer located in southwestern Iran.In the first stage,by employing an AHP decision-making model,hydraulic conductivity,specific yield,slope,land use,depth to groundwater,and aquifer thickness were selected from 21 criteria used in previous research.The selected criteria were then entered as input into the classical k-means clustering model.Using the output,aquifer was divided into seven different regions or clusters.These clusters were then matched with the land use map,and some of the abandoned land areas were selected as the final option for implementing the artificial recharge project.Finally,the MODFLOW code in the GMS software was used to simulate the groundwater level and cluster the sites selected,with regards to increase in groundwater level.Results indicated that the most significant increases in groundwater level(43 and 27 cm)were those of Clusters 2 and 6 in the northern and western parts of the aquifer,respectively.Therefore,this approach can be used in other similar aquifers in arid and semi-arid regions to select the best sites for artificial recharge and to prevent loss of floodwaters.展开更多
Pre-oxidation has been reported to be an effective way to remove algal cells in water, but the released algal organic matter (AOM) could be oxidized and lead to the increment in disinfection by-product (DBP) formation...Pre-oxidation has been reported to be an effective way to remove algal cells in water, but the released algal organic matter (AOM) could be oxidized and lead to the increment in disinfection by-product (DBP) formation. The relationship between pre-oxidation and AOM-derived DBP formation needs to be approached more precisely. This study compared the impact of four pre-oxidants, ozone (O), chlorine dioxide (ClO), potassium permanganate(KMnO) and sodium hypochlorite (NaClO), on the formation of nitrogenous (N-) and carbonaceous (C-) DBPs in AOM chlorination. The characterization (fluorescent properties,molecular weight distribution and amino acids concentration) on AOM samples showed that the characterization properties variations after pre-oxidation were highly dependent on the oxidizing ability of oxidants. The disinfection experiments showed that Oincreased DBP formation most significantly, which was consistent with the result of characterization properties variations. Then canonical correspondent analysis (CCA) and Pearson’s correlation analysis were conducted based on the characterization data and DBP formation. CCA indicated that C-DBPs formation was highly dependent on fluorescent data. The formation of haloacetic acids (HAAs) had a positive correlation with aromatic protein-like component while trichloromethane (TCM) had a positive correlation with fulvic acid-like component.Pearson’s correlation analysis showed that low molecular weight fractions were favorable to form N-DBPs. Therefore, characterization data could provide the advantages in the control of DBP formation, which further revealed that KMnOand ClOwere better options for removing algal cells as well as limiting DBP formation.展开更多
In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The c...In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.展开更多
Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A la...Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.展开更多
The slight-alkalization of generator internal cooling water(GICW)is widely used to inhibit the corrosion of hollow copper conductor and thereby ensure the safe operation of the generator.CO_(2) inleakage is increasing...The slight-alkalization of generator internal cooling water(GICW)is widely used to inhibit the corrosion of hollow copper conductor and thereby ensure the safe operation of the generator.CO_(2) inleakage is increasingly identified as a potential security risk for GICW system.In this paper,the influence of CO_(2) inleakage on the slight-alkalization of GICW was theoretically discussed.Based on the equilibriums of the CO_(2)-NaOH-H_(2)O system,CO_(2) inleakage saturation was derived to quantify the amount of the dissolved CO_(2) in GICW.This parameter can be directly calculated with the measured conductivity and the[Na+]of GICW.The influence of CO_(2) inleakage on the slight-alkalization conditioning of GICW and the measurement of its water quality parameters were then analyzed.The more severe the inleakage,the narrower the water quality operation ranges of GICW,resulting in the more difficult the slight-alkalization conditioning of GICW.The temperature calibrations of the conductivity and the pH value of GICW show nonlinear correlations with the amount of CO_(2) inleakage and the NaOH dosage.This study provides insights into the influence of CO_(2) inleakage on the slight-alkalization of GICW,which can serve as the theoretical basis for the actual slight-alkalization when CO_(2) inleakage occurs.展开更多
Macro rainwater harvesting techniques (Macro RWH) are getting more popular to overcome the problem of water scarcity in arid and semi-arid areas. Iraq is experiencing serious water shortage problem now despite of the ...Macro rainwater harvesting techniques (Macro RWH) are getting more popular to overcome the problem of water scarcity in arid and semi-arid areas. Iraq is experiencing serious water shortage problem now despite of the presence of Tigris and Euphrates Rivers. RWH can help to overcome this problem. In this research, RWH was applied in Koya City in its districts, North West Iraq. Twenty-two basins were identified as the catchment area for the application of RWH technique. Watershed modeling system (WMS), based on Soil Conservation Service-curve number (SCS-CN) method, was applied to calculate direct runoff from individual daily rain storm using average annual rainfall records of the area. Two consecutive adjustments for the curve number were considered. The first was for the antecedent moisture condition (AMC) and the second was for the slope. These adjustments increased the total resultant harvested runoff up to 79.402 × 106 m3. The average percentage of increase of harvested runoff volume reached 9.28%. This implies that water allocation is of the order of 2000 cubic meter per capita per year. This quantity of water will definitely help to develop the area.展开更多
Water scarcity is the most significant barrier to agricultural development in arid and semi-arid regions.Deficit irrigation is an effective solution for managing agricultural water in these regions.The use of additive...Water scarcity is the most significant barrier to agricultural development in arid and semi-arid regions.Deficit irrigation is an effective solution for managing agricultural water in these regions.The use of additives such as vermicompost(VC)to improve soil characteristics and increase yield is a popular practice.Despite this,there is still a lack of understanding of the interaction between irrigation water and VC on various crops.This study aimed to investigate the interaction effect of irrigation water and VC on greenhouse cucumber yield,yield components,quality,and irrigation water use efficiency(IWUE).The trials were done in a split-plot design in three replicates in a semi-arid region of southeastern Iran in 2018 and 2019.Three levels of VC in the experiments,i.e.,10(V_(1)),15(V_(2)),and 20 t/hm^(2)(V_(3)),and three levels of irrigation water,i.e.,50%(I_(1)),75%(I_(2)),and 100%(I_(3))of crop water requirement were used.The results showed that the amount of irrigation water,VC,and their interaction significantly affected cucumber yield,yield components,quality,and IWUE in both years.Reducing the amount of irrigation water and VC application rates reduced the weight,diameter,length,and cucumber yield.The maximum yield(175 t/hm^(2))was recorded in full irrigation using 20 t/hm^(2)of VC,while the minimum yield(98 t/hm^(2))was found in I_(1)V_(1)treatment.The maximum and minimum values of IWUE were recorded for I_(1)V_(3)and I_(3)V_(1)treatments as 36.07 and 19.93 kg/(m^(3)•hm^(2)),respectively.Moreover,reducing irrigation amount decreased chlorophyll a and b,but increased vitamin C.However,the maximum carbohydrate and protein contents were obtained in mild water-stressed conditions(I_(2)).Although adding VC positively influenced the value of quality traits,no significant difference was observed between V_(2)and V_(3)treatments.Based on the results,adding VC under full irrigation conditions leads to enhanced yield and IWUE.However,in the case of applying deficit irrigation,adding VC up to a certain level(15 t/hm^(2))increases yield and IWUE,after which the yield begins to decline.Because of the salinity of VC,using a suitable amount of it is a key point to maximize IWUE and yield when applying a deficit irrigation regime.展开更多
Assessment of climate and land use changes impact including extreme events on the sediment yield is vital for water and power stressed countries. Mangla Reservoir is the second-largest reservoir in Pakistan, and its c...Assessment of climate and land use changes impact including extreme events on the sediment yield is vital for water and power stressed countries. Mangla Reservoir is the second-largest reservoir in Pakistan, and its capacity is being reduced due to rapid sedimentation and will be threatened under climate and land use changes. This paper discusses the consequences of climate and land use change on sediment yield at Mangla Dam using General Circulation Models(GCMs), Land Change Modeler(LCM), Soil and Water Assessment Tool(SWAT) model after calibration and validation.Results show that over the historical period temperature is observed to increase by 0.10 o C/decade and forest cover is observed to reduce to the level of only 16% in 2007. Nevertheless, owing to the forest conservation policy, the forest cover raised back to 27% in 2012. Anticipated land use maps by using LCM of 2025, 2050 and 2100 showed that the forest cover will be 33%, 39.2%, and, 53.7%, respectively. All seven GCMs projected the increase in temperature and five GCMs projected an increase in precipitation,however, two GCMs projected a decrease in precipitation. Owing to climate change, land use change and combined impact of climate and land use change on annual sediment yield(2011-2100) may vary from-42.9% to 39.4%, 0% to-27.3% and,-73%to 39.4%, respectively. Under climate change scenarios projected sediment yield is mainly linked with extreme events and is expected to increase with the increase in extreme events. Under land use change scenarios projected sediment yield is mainly linked with the forest cover and is expected to decrease with the increase in forest cover. The results of this study are beneficial for planners, watershed managers and policymakers to mitigate the impacts of climate and land use changes to enhance reservoir life by reducing the sediment yield.展开更多
Agriculture faces risks due to increasing stress from climate change,particularly in semi-arid regions.Lack of understanding of crop water requirement(CWR)and irrigation water requirement(IWR)in a changing climate may...Agriculture faces risks due to increasing stress from climate change,particularly in semi-arid regions.Lack of understanding of crop water requirement(CWR)and irrigation water requirement(IWR)in a changing climate may result in crop failure and socioeconomic problems that can become detrimental to agriculture-based economies in emerging nations worldwide.Previous research in CWR and IWR has largely focused on large river basins and scenarios from the Coupled Model Intercomparison Project Phase 3(CMIP3)and Coupled Model Intercomparison Project Phase 5(CMIP5)to account for the impacts of climate change on crops.Smaller basins,however,are more susceptible to regional climate change,with more significant impacts on crops.This study estimates CWRs and IWRs for five crops(sugarcane,wheat,cotton,sorghum,and soybean)in the Pravara River Basin(area of 6537 km^(2))of India using outputs from the most recent Coupled Model Intercomparison Project Phase 6(CMIP6)General Circulation Models(GCMs)under Shared Socio-economic Pathway(SSP)245 and SSP585 scenarios.An increase in mean annual rainfall is projected under both scenarios in the 2050s and 2080s using ten selected CMIP6 GCMs.CWRs for all crops may decline in almost all of the CMIP6 GCMs in the 2050s and 2080s(with the exceptions of ACCESS-CM-2 and ACCESS-ESM-1.5)under SSP245 and SSP585 scenarios.The availability of increasing soil moisture in the root zone due to increasing rainfall and a decrease in the projected maximum temperature may be responsible for this decline in CWR.Similarly,except for soybean and cotton,the projected IWRs for all other three crops under SSP245 and SSP585 scenarios show a decrease or a small increase in the 2050s and 2080s in most CMIP6 GCMs.These findings are important for agricultural researchers and water resource managers to implement long-term crop planning techniques and to reduce the negative impacts of climate change and associated rainfall variability to avert crop failure and agricultural losses.展开更多
This research proposes a novel nature-based design of a new concrete armour unit for the cover layer of a rubblemoundbreakwater. Armour units are versatile with respect to shape, orientation, surface condition details...This research proposes a novel nature-based design of a new concrete armour unit for the cover layer of a rubblemoundbreakwater. Armour units are versatile with respect to shape, orientation, surface condition details, and porosity.Therefore, a detailed analysis is required to investigate the exact state of their hydraulic interactions and structuralresponses. In this regard, the performance results of several traditional armour units, including the Antifer cube,Tetrapod, X-block and natural stone, are considered for the first step of this study. Then, the related observed resultsare compared with those obtained for a newly designed (artificial coral) armour unit. The research methodology utilizesthe common wave flume test procedure. Furthermore, several verified numerical models in OpenFOAM code areused to gain the extra required data. The proposed armour is configured to provide an effective shore protection as anenvironmental-friendly coastal structure. Thus it is designed with a main trunk including deep grooves to imitate thetypical geometry of a coral type configuration, so as to attain desirable performance. The observed results and ananalytic hierarchy process (AHP) concept are used to compare the hydraulic performance of the studied traditionaland newly proposed (artificial coral) armour units. The results indicate that the artificial coral armour unit demonstratesacceptable performance. The widely used traditional armour units might be replaced by newer designs for betterwave energy dissipation, and more importantly, for fewer adverse effects on the marine environment.展开更多
Movement of sediment load and its pattern of transportation along nearshore coastal water is a very important phenomenon to be assessed for different sector of coastal Engineering. To develop and understand the physic...Movement of sediment load and its pattern of transportation along nearshore coastal water is a very important phenomenon to be assessed for different sector of coastal Engineering. To develop and understand the physical processes responsible for shaping the ongoing evolution of the coast and to develop the management strategies to deal the impact of human activities on the coastal zone and as well as for adapting to the hazards associated with the people living on the coast, knowledge of the mechanism, processes and the pattern of sediment movement in the nearshore zone is of utmost importance. Nearshore zone is a very active area, where a series of dynamic processes occur in response to changing wave climates and sediment budgets. Nowadays mathematical modeling is an attractive alternative and becoming a very viable approach to study the sediment movement pattern with the advanced computational facilities and improved understanding on wave mechanics and sediment transport processes. It is very effective, reliable and also comfortable to study the pattern of sediment transportation including yield, distribution and management of sediment with the help of mathematical model. Validity of forecast in sediment transport depends on both mathematical modeling technique and boundary conditions.展开更多
A simple idealized model to describe the hydraulic resistance caused by vegetation is compared to results from flow experiments conducted in natural waterways. Two field case studies are considered: fixed-point flow m...A simple idealized model to describe the hydraulic resistance caused by vegetation is compared to results from flow experiments conducted in natural waterways. Two field case studies are considered: fixed-point flow measurements in a Green River (case 1) and vessel-borne flow measurements along a cross-section with floodplains in the river Rhine (case 2). Analysis of the two cases shows that the simple flow model is consistent with measured flow velocities and the present vegetation characteristics, and may be used to predict a realistic Manning resistance coefficient. From flow measurements in the river floodplain (case 2) an estimate was made of the equivalent height of the drag dominated vegetation layer, as based on measured flow characteristics. The resulting height corresponds well with the observed height of vegetation in the floodplain. The expected depth-dependency of the associated Manning resistance coefficient for could not be detected due to lack of data for relatively shallow flows. Furthermore, it was shown that topographical variations in the floodplain may have an important impact on the flow field, which should not be mistaken as roughness effects.展开更多
Dump sites pose a significant threat to groundwater resources due to the possibility of leachate leakage into the aquifer.This study investigated the impact of leachate on groundwater quality in the southwest region o...Dump sites pose a significant threat to groundwater resources due to the possibility of leachate leakage into the aquifer.This study investigated the impact of leachate on groundwater quality in the southwest region of Zanjan City,Iran,where groundwater is utilized for drinking,agricultural,and industrial purposes.We analyzed 18 parameters of dump site leachate,including physicochemical,heavy metals,and bacterial properties,alongside 13 groundwater samples.Sampling was conducted twice,in November 2020 and June 2021,within a five-kilometer radius of the Zanjan dump site.We utilized the Leachate Pollution Index(LPI)to evaluate potential groundwater contamination by leachate leakage from nearby dumpsite.Additionally,due to the predominant agricultural activities in the study area,various indices were employed to assess groundwater quality for agricultural purposes,such as Sodium Adsorption Ratio(SAR),Soluble Sodium Index(SSI),Kelly Ratio(KR),and Permeability Index(PI).Our analysis revealed no observed contamination related to leachate in the study area according to the LPI results.However,with the persistent pollution threat,implementing sanitary measures at the dump site is crucial to prevent potential impacts on groundwater quality.Moreover,the assessment of groundwater quality adequacy for irrigation yielded satisfactory results for SAR,KR,and PI indices.However,during both the dry(November 2020)and wet seasons(June 2021),the SSP index indicated that 80%of the samples were not classified as excellent,suggesting groundwater may not be suitable for agriculture.Overal,our qualitative study highlights the significant impact of the dry season on groundwater quality in the study area,attributed to elevated concentration levels of the investigated parameters within groundwater sources during the dry season.展开更多
Minimizing water loss in water supply networks is one of the objectives for protecting water resources. Currently, the large amount of water loss is mainly due to leakage of the pipeline network. The leaking of pipe c...Minimizing water loss in water supply networks is one of the objectives for protecting water resources. Currently, the large amount of water loss is mainly due to leakage of the pipeline network. The leaking of pipe can be caused by incorrect construction, impacted by external forces, or corroded pipe material and aging. Therefore, to control and predict the cracking area on pipe, it is necessary to collect data about pipe conditions, approve the solution of technology improvement and define the ability of pipe capacity from setting up to the first preparing time. This paper will demonstrate how to evaluate corrosion pipe under the age of pipe and the impact level of internal pressure pipe at different times, and will put forward solution of effective leaking management on water supply network.展开更多
Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour ...Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R<sup>2</sup> = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.展开更多
This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite ...This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite mission.The primary objective is to develop accurate predictive models for groundwa-ter level changes by leveraging the unique capabilities of GRACE satellite data in conjunction with advanced machine learning algorithms.Three widely-used machine learning models,namely DT,SVM and RF,were employed to analyze and model the relationship between GRACE satellite data and groundwater fluctuations in South Khorasan Province,Iran.The study utilized 151 months of GRACE data spanning from 2002 to 2017,which were correlated with piezometer well data available in the study area.The JPL 2 model was selected based on its strong correlation(R=0.9368)with the observed data.The machine learn-ing models were trained and validated using a 70/30 split of the data,and their performance was evaluated 2 using various statistical metrics,including RMSE,R and NSE.The results demonstrated the suitability of machine learning approaches for modeling groundwater fluctuations using GRACE satellite data.The DT 2 model exhibited the best performance during the calibration stage,with an R value of 0.95,RMSE of 20.655,and NSE of 0.96.The SVM and RF models achieved R values of 0.79 and 0.65,and NSE values of 0.86 and 0.71,respectively.For the prediction stage,the DT model maintained its high efficiency,with an 2 RMSE of 1.48,R of 0.87,and NSE of 0.90,indicating its robustness in predicting future groundwater fluc-tuations using GRACE data.The study highlights the potential of machine learning techniques,particularly Decision Trees,in conjunction with GRACE satellite data,for accurate prediction and monitoring of groundwater fluctuations in arid and semi-arid regions.The findings demonstrate the effectiveness of the DT model in capturing the complex relationships between GRACE data and groundwater dynamics,provid-ing reliable predictions and insights for sustainable groundwater management strategies.展开更多
基金supported by the Iran National Science Foundation(INSF)the University of Birjand under grant number 4034771.
文摘Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.
文摘Effective management of water resources,especially groundwater,is crucial and requires a precise understanding of aquifer characteristics,imposed stresses,and the groundwater balance.Simulation-optimization models plays a vital role in guiding planners toword sustainable long-term aquifer exploita-tion.This study simulated monthly water table variations in the Kashan Plain over a ten-year period from 2008 to 2019 across 125 stress periods using the GMS model.The model was calibrated for both steady-state and transient conditions for the 2008–2016 period and validated for the 2016–2019 period.Results indicated a 4.4 m decline in groundwater levels over the 10-year study period.Given the plain's location in a arid climatic zone with limited effective precipitation for aquifer recharge,the study focused on ground-water extraction management.A modified two-point hedging policy was employed as a solution to mitigate critical groundwater depletion,reducing the annual drawdown rate from 0.44 m to 0.31 m and conserving 255 million cubic meters(mcm)of water annually.Although this approach slightly decreased reliability(i.e.the number of months meeting full water demands),it effectively minimized the risk of severe droughts and irreparable damages.This policy offers managers a dynamical and intelligent tool for regulating groundwater extraction,balancing aquifer sustainability with agricultural and urban water requirements.
文摘Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.
文摘Since incorrect site selection has sometimes led to the failure of artificial recharge projects,it is necessary to increase the effectiveness of such projects and minimize their failure by employing new techniques.Therefore,the present research used a combination of decision-making models,numerical groundwater modeling and clustering technique to determine suitable sites for implementation of an artificial recharge project.This hybrid approach was employed for the Yasouj aquifer located in southwestern Iran.In the first stage,by employing an AHP decision-making model,hydraulic conductivity,specific yield,slope,land use,depth to groundwater,and aquifer thickness were selected from 21 criteria used in previous research.The selected criteria were then entered as input into the classical k-means clustering model.Using the output,aquifer was divided into seven different regions or clusters.These clusters were then matched with the land use map,and some of the abandoned land areas were selected as the final option for implementing the artificial recharge project.Finally,the MODFLOW code in the GMS software was used to simulate the groundwater level and cluster the sites selected,with regards to increase in groundwater level.Results indicated that the most significant increases in groundwater level(43 and 27 cm)were those of Clusters 2 and 6 in the northern and western parts of the aquifer,respectively.Therefore,this approach can be used in other similar aquifers in arid and semi-arid regions to select the best sites for artificial recharge and to prevent loss of floodwaters.
基金supported by the National Natural Science Foundation (Nos. 51878257, 52100007)the Natural Science Foundation of Hunan Province (No. 2021JJ40066) and the Natural Science Foundation of Hunan Province (No. 2021JJ40106)。
文摘Pre-oxidation has been reported to be an effective way to remove algal cells in water, but the released algal organic matter (AOM) could be oxidized and lead to the increment in disinfection by-product (DBP) formation. The relationship between pre-oxidation and AOM-derived DBP formation needs to be approached more precisely. This study compared the impact of four pre-oxidants, ozone (O), chlorine dioxide (ClO), potassium permanganate(KMnO) and sodium hypochlorite (NaClO), on the formation of nitrogenous (N-) and carbonaceous (C-) DBPs in AOM chlorination. The characterization (fluorescent properties,molecular weight distribution and amino acids concentration) on AOM samples showed that the characterization properties variations after pre-oxidation were highly dependent on the oxidizing ability of oxidants. The disinfection experiments showed that Oincreased DBP formation most significantly, which was consistent with the result of characterization properties variations. Then canonical correspondent analysis (CCA) and Pearson’s correlation analysis were conducted based on the characterization data and DBP formation. CCA indicated that C-DBPs formation was highly dependent on fluorescent data. The formation of haloacetic acids (HAAs) had a positive correlation with aromatic protein-like component while trichloromethane (TCM) had a positive correlation with fulvic acid-like component.Pearson’s correlation analysis showed that low molecular weight fractions were favorable to form N-DBPs. Therefore, characterization data could provide the advantages in the control of DBP formation, which further revealed that KMnOand ClOwere better options for removing algal cells as well as limiting DBP formation.
文摘In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.
基金The authors would like to thank the Laboratory of Water Engineering,Fasa University for providing the facilities to perform this research.
文摘Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.
文摘The slight-alkalization of generator internal cooling water(GICW)is widely used to inhibit the corrosion of hollow copper conductor and thereby ensure the safe operation of the generator.CO_(2) inleakage is increasingly identified as a potential security risk for GICW system.In this paper,the influence of CO_(2) inleakage on the slight-alkalization of GICW was theoretically discussed.Based on the equilibriums of the CO_(2)-NaOH-H_(2)O system,CO_(2) inleakage saturation was derived to quantify the amount of the dissolved CO_(2) in GICW.This parameter can be directly calculated with the measured conductivity and the[Na+]of GICW.The influence of CO_(2) inleakage on the slight-alkalization conditioning of GICW and the measurement of its water quality parameters were then analyzed.The more severe the inleakage,the narrower the water quality operation ranges of GICW,resulting in the more difficult the slight-alkalization conditioning of GICW.The temperature calibrations of the conductivity and the pH value of GICW show nonlinear correlations with the amount of CO_(2) inleakage and the NaOH dosage.This study provides insights into the influence of CO_(2) inleakage on the slight-alkalization of GICW,which can serve as the theoretical basis for the actual slight-alkalization when CO_(2) inleakage occurs.
文摘Macro rainwater harvesting techniques (Macro RWH) are getting more popular to overcome the problem of water scarcity in arid and semi-arid areas. Iraq is experiencing serious water shortage problem now despite of the presence of Tigris and Euphrates Rivers. RWH can help to overcome this problem. In this research, RWH was applied in Koya City in its districts, North West Iraq. Twenty-two basins were identified as the catchment area for the application of RWH technique. Watershed modeling system (WMS), based on Soil Conservation Service-curve number (SCS-CN) method, was applied to calculate direct runoff from individual daily rain storm using average annual rainfall records of the area. Two consecutive adjustments for the curve number were considered. The first was for the antecedent moisture condition (AMC) and the second was for the slope. These adjustments increased the total resultant harvested runoff up to 79.402 × 106 m3. The average percentage of increase of harvested runoff volume reached 9.28%. This implies that water allocation is of the order of 2000 cubic meter per capita per year. This quantity of water will definitely help to develop the area.
文摘Water scarcity is the most significant barrier to agricultural development in arid and semi-arid regions.Deficit irrigation is an effective solution for managing agricultural water in these regions.The use of additives such as vermicompost(VC)to improve soil characteristics and increase yield is a popular practice.Despite this,there is still a lack of understanding of the interaction between irrigation water and VC on various crops.This study aimed to investigate the interaction effect of irrigation water and VC on greenhouse cucumber yield,yield components,quality,and irrigation water use efficiency(IWUE).The trials were done in a split-plot design in three replicates in a semi-arid region of southeastern Iran in 2018 and 2019.Three levels of VC in the experiments,i.e.,10(V_(1)),15(V_(2)),and 20 t/hm^(2)(V_(3)),and three levels of irrigation water,i.e.,50%(I_(1)),75%(I_(2)),and 100%(I_(3))of crop water requirement were used.The results showed that the amount of irrigation water,VC,and their interaction significantly affected cucumber yield,yield components,quality,and IWUE in both years.Reducing the amount of irrigation water and VC application rates reduced the weight,diameter,length,and cucumber yield.The maximum yield(175 t/hm^(2))was recorded in full irrigation using 20 t/hm^(2)of VC,while the minimum yield(98 t/hm^(2))was found in I_(1)V_(1)treatment.The maximum and minimum values of IWUE were recorded for I_(1)V_(3)and I_(3)V_(1)treatments as 36.07 and 19.93 kg/(m^(3)•hm^(2)),respectively.Moreover,reducing irrigation amount decreased chlorophyll a and b,but increased vitamin C.However,the maximum carbohydrate and protein contents were obtained in mild water-stressed conditions(I_(2)).Although adding VC positively influenced the value of quality traits,no significant difference was observed between V_(2)and V_(3)treatments.Based on the results,adding VC under full irrigation conditions leads to enhanced yield and IWUE.However,in the case of applying deficit irrigation,adding VC up to a certain level(15 t/hm^(2))increases yield and IWUE,after which the yield begins to decline.Because of the salinity of VC,using a suitable amount of it is a key point to maximize IWUE and yield when applying a deficit irrigation regime.
文摘Assessment of climate and land use changes impact including extreme events on the sediment yield is vital for water and power stressed countries. Mangla Reservoir is the second-largest reservoir in Pakistan, and its capacity is being reduced due to rapid sedimentation and will be threatened under climate and land use changes. This paper discusses the consequences of climate and land use change on sediment yield at Mangla Dam using General Circulation Models(GCMs), Land Change Modeler(LCM), Soil and Water Assessment Tool(SWAT) model after calibration and validation.Results show that over the historical period temperature is observed to increase by 0.10 o C/decade and forest cover is observed to reduce to the level of only 16% in 2007. Nevertheless, owing to the forest conservation policy, the forest cover raised back to 27% in 2012. Anticipated land use maps by using LCM of 2025, 2050 and 2100 showed that the forest cover will be 33%, 39.2%, and, 53.7%, respectively. All seven GCMs projected the increase in temperature and five GCMs projected an increase in precipitation,however, two GCMs projected a decrease in precipitation. Owing to climate change, land use change and combined impact of climate and land use change on annual sediment yield(2011-2100) may vary from-42.9% to 39.4%, 0% to-27.3% and,-73%to 39.4%, respectively. Under climate change scenarios projected sediment yield is mainly linked with extreme events and is expected to increase with the increase in extreme events. Under land use change scenarios projected sediment yield is mainly linked with the forest cover and is expected to decrease with the increase in forest cover. The results of this study are beneficial for planners, watershed managers and policymakers to mitigate the impacts of climate and land use changes to enhance reservoir life by reducing the sediment yield.
基金supported by the research project Developing Localized Indicators of Climate Change for Impact Risk Assessment in Ahmednagar using CMIP5 Data through University Grant Commission-Basic Science Research(UGC-BSR)Start-Up Grant(No.F.30-525/2020(BSR))University Grant Commission,New Delhi for providing fund。
文摘Agriculture faces risks due to increasing stress from climate change,particularly in semi-arid regions.Lack of understanding of crop water requirement(CWR)and irrigation water requirement(IWR)in a changing climate may result in crop failure and socioeconomic problems that can become detrimental to agriculture-based economies in emerging nations worldwide.Previous research in CWR and IWR has largely focused on large river basins and scenarios from the Coupled Model Intercomparison Project Phase 3(CMIP3)and Coupled Model Intercomparison Project Phase 5(CMIP5)to account for the impacts of climate change on crops.Smaller basins,however,are more susceptible to regional climate change,with more significant impacts on crops.This study estimates CWRs and IWRs for five crops(sugarcane,wheat,cotton,sorghum,and soybean)in the Pravara River Basin(area of 6537 km^(2))of India using outputs from the most recent Coupled Model Intercomparison Project Phase 6(CMIP6)General Circulation Models(GCMs)under Shared Socio-economic Pathway(SSP)245 and SSP585 scenarios.An increase in mean annual rainfall is projected under both scenarios in the 2050s and 2080s using ten selected CMIP6 GCMs.CWRs for all crops may decline in almost all of the CMIP6 GCMs in the 2050s and 2080s(with the exceptions of ACCESS-CM-2 and ACCESS-ESM-1.5)under SSP245 and SSP585 scenarios.The availability of increasing soil moisture in the root zone due to increasing rainfall and a decrease in the projected maximum temperature may be responsible for this decline in CWR.Similarly,except for soybean and cotton,the projected IWRs for all other three crops under SSP245 and SSP585 scenarios show a decrease or a small increase in the 2050s and 2080s in most CMIP6 GCMs.These findings are important for agricultural researchers and water resource managers to implement long-term crop planning techniques and to reduce the negative impacts of climate change and associated rainfall variability to avert crop failure and agricultural losses.
文摘This research proposes a novel nature-based design of a new concrete armour unit for the cover layer of a rubblemoundbreakwater. Armour units are versatile with respect to shape, orientation, surface condition details, and porosity.Therefore, a detailed analysis is required to investigate the exact state of their hydraulic interactions and structuralresponses. In this regard, the performance results of several traditional armour units, including the Antifer cube,Tetrapod, X-block and natural stone, are considered for the first step of this study. Then, the related observed resultsare compared with those obtained for a newly designed (artificial coral) armour unit. The research methodology utilizesthe common wave flume test procedure. Furthermore, several verified numerical models in OpenFOAM code areused to gain the extra required data. The proposed armour is configured to provide an effective shore protection as anenvironmental-friendly coastal structure. Thus it is designed with a main trunk including deep grooves to imitate thetypical geometry of a coral type configuration, so as to attain desirable performance. The observed results and ananalytic hierarchy process (AHP) concept are used to compare the hydraulic performance of the studied traditionaland newly proposed (artificial coral) armour units. The results indicate that the artificial coral armour unit demonstratesacceptable performance. The widely used traditional armour units might be replaced by newer designs for betterwave energy dissipation, and more importantly, for fewer adverse effects on the marine environment.
文摘Movement of sediment load and its pattern of transportation along nearshore coastal water is a very important phenomenon to be assessed for different sector of coastal Engineering. To develop and understand the physical processes responsible for shaping the ongoing evolution of the coast and to develop the management strategies to deal the impact of human activities on the coastal zone and as well as for adapting to the hazards associated with the people living on the coast, knowledge of the mechanism, processes and the pattern of sediment movement in the nearshore zone is of utmost importance. Nearshore zone is a very active area, where a series of dynamic processes occur in response to changing wave climates and sediment budgets. Nowadays mathematical modeling is an attractive alternative and becoming a very viable approach to study the sediment movement pattern with the advanced computational facilities and improved understanding on wave mechanics and sediment transport processes. It is very effective, reliable and also comfortable to study the pattern of sediment transportation including yield, distribution and management of sediment with the help of mathematical model. Validity of forecast in sediment transport depends on both mathematical modeling technique and boundary conditions.
文摘A simple idealized model to describe the hydraulic resistance caused by vegetation is compared to results from flow experiments conducted in natural waterways. Two field case studies are considered: fixed-point flow measurements in a Green River (case 1) and vessel-borne flow measurements along a cross-section with floodplains in the river Rhine (case 2). Analysis of the two cases shows that the simple flow model is consistent with measured flow velocities and the present vegetation characteristics, and may be used to predict a realistic Manning resistance coefficient. From flow measurements in the river floodplain (case 2) an estimate was made of the equivalent height of the drag dominated vegetation layer, as based on measured flow characteristics. The resulting height corresponds well with the observed height of vegetation in the floodplain. The expected depth-dependency of the associated Manning resistance coefficient for could not be detected due to lack of data for relatively shallow flows. Furthermore, it was shown that topographical variations in the floodplain may have an important impact on the flow field, which should not be mistaken as roughness effects.
文摘Dump sites pose a significant threat to groundwater resources due to the possibility of leachate leakage into the aquifer.This study investigated the impact of leachate on groundwater quality in the southwest region of Zanjan City,Iran,where groundwater is utilized for drinking,agricultural,and industrial purposes.We analyzed 18 parameters of dump site leachate,including physicochemical,heavy metals,and bacterial properties,alongside 13 groundwater samples.Sampling was conducted twice,in November 2020 and June 2021,within a five-kilometer radius of the Zanjan dump site.We utilized the Leachate Pollution Index(LPI)to evaluate potential groundwater contamination by leachate leakage from nearby dumpsite.Additionally,due to the predominant agricultural activities in the study area,various indices were employed to assess groundwater quality for agricultural purposes,such as Sodium Adsorption Ratio(SAR),Soluble Sodium Index(SSI),Kelly Ratio(KR),and Permeability Index(PI).Our analysis revealed no observed contamination related to leachate in the study area according to the LPI results.However,with the persistent pollution threat,implementing sanitary measures at the dump site is crucial to prevent potential impacts on groundwater quality.Moreover,the assessment of groundwater quality adequacy for irrigation yielded satisfactory results for SAR,KR,and PI indices.However,during both the dry(November 2020)and wet seasons(June 2021),the SSP index indicated that 80%of the samples were not classified as excellent,suggesting groundwater may not be suitable for agriculture.Overal,our qualitative study highlights the significant impact of the dry season on groundwater quality in the study area,attributed to elevated concentration levels of the investigated parameters within groundwater sources during the dry season.
文摘Minimizing water loss in water supply networks is one of the objectives for protecting water resources. Currently, the large amount of water loss is mainly due to leakage of the pipeline network. The leaking of pipe can be caused by incorrect construction, impacted by external forces, or corroded pipe material and aging. Therefore, to control and predict the cracking area on pipe, it is necessary to collect data about pipe conditions, approve the solution of technology improvement and define the ability of pipe capacity from setting up to the first preparing time. This paper will demonstrate how to evaluate corrosion pipe under the age of pipe and the impact level of internal pressure pipe at different times, and will put forward solution of effective leaking management on water supply network.
文摘Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R<sup>2</sup> = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.
文摘This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite mission.The primary objective is to develop accurate predictive models for groundwa-ter level changes by leveraging the unique capabilities of GRACE satellite data in conjunction with advanced machine learning algorithms.Three widely-used machine learning models,namely DT,SVM and RF,were employed to analyze and model the relationship between GRACE satellite data and groundwater fluctuations in South Khorasan Province,Iran.The study utilized 151 months of GRACE data spanning from 2002 to 2017,which were correlated with piezometer well data available in the study area.The JPL 2 model was selected based on its strong correlation(R=0.9368)with the observed data.The machine learn-ing models were trained and validated using a 70/30 split of the data,and their performance was evaluated 2 using various statistical metrics,including RMSE,R and NSE.The results demonstrated the suitability of machine learning approaches for modeling groundwater fluctuations using GRACE satellite data.The DT 2 model exhibited the best performance during the calibration stage,with an R value of 0.95,RMSE of 20.655,and NSE of 0.96.The SVM and RF models achieved R values of 0.79 and 0.65,and NSE values of 0.86 and 0.71,respectively.For the prediction stage,the DT model maintained its high efficiency,with an 2 RMSE of 1.48,R of 0.87,and NSE of 0.90,indicating its robustness in predicting future groundwater fluc-tuations using GRACE data.The study highlights the potential of machine learning techniques,particularly Decision Trees,in conjunction with GRACE satellite data,for accurate prediction and monitoring of groundwater fluctuations in arid and semi-arid regions.The findings demonstrate the effectiveness of the DT model in capturing the complex relationships between GRACE data and groundwater dynamics,provid-ing reliable predictions and insights for sustainable groundwater management strategies.
