As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.H...As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.展开更多
-In this paper,by using ISODATA of fuzzy cluster,the water masses classification of the upper layer in the E-quatorial Western Pacific is carried out. On the basis of the degree of the membership in the obtained optim...-In this paper,by using ISODATA of fuzzy cluster,the water masses classification of the upper layer in the E-quatorial Western Pacific is carried out. On the basis of the degree of the membership in the obtained optima) classification matrix, the solid distribution of the detailed structure of water masses is made. The water of the upper layer,consisting of six water masses,may be divided into three layers,i, e. ,the surface,subsurface and intermediate layer. Besides analyzing the features of various water masses,a discussion on their distribution structure and formation mechanism is also made.展开更多
In order to clarify the characteristics of non-uniform water invasion in water-bearing gas reservoirs,it is necessary to introduce the nonuniformity coefficient(A)and water invasion constant(B)to characterize the non-...In order to clarify the characteristics of non-uniform water invasion in water-bearing gas reservoirs,it is necessary to introduce the nonuniformity coefficient(A)and water invasion constant(B)to characterize the non-uniformity degree of reservoir physical properties and the activity degree of peripheral water,respectively,based on the dual mechanism of water invasion to recharge the formation energy and seal off the gas in the reservoir.Then,the material balance method considering the phenomenon of water sealed gas was established.On this basis,the water invasion characteristic curve chart of water-bearing gas reservoirs was plotted,and the non-uniform water invasion mode was classified based on the example gas reservoir.And the following research results were obtained.First,in the water invasion characteristic curve chart of waterbearing gas reservoirs which is plotted based on the material balance method considering the influence of water sealed gas,the upper right area and the lower left area are defined as recharge area and seal area,respectively.By taking A=0 and B=2 as the boundary,the recharge area is divided into strong recharge area and weak recharge area.By taking A=2 and B=2 as the boundary,the seal area is divided into strong seal area and weak seal area.And correspondingly there are four water invasion modes,i.e.,strong recharge,weak recharge,weak seal and strong seal.Second,for fractured gas reservoirs,the non-uniformity degree of reservoir physical properties is high,and water sealed gas can be formed easily after water invasion.The dimensionless relative pseudo-pressure data of this type of gas reservoir is located in the seal area of the water invasion characteristic curve chart.Third,for the gas reservoirs whose reservoir physical properties are relatively uniform,the dimensionless relative pseudo-pressure data is located in the recharge area of the water invasion characteristic curve chart,and the recharge effect of water invasion on formation energy is greater than the weakening effect of water sealed gas on formation energy.Fourth,with the increase of A,the non-uniformity degree of reservoir physical properties increases,the water invasion characteristic curve shifts from the upper right to the lower left,and the recovery factor of gas reservoir decreases continuously.With the increase of B,the recharge effect of water invasion on formation energy and the weakening effect of water sealed gas on formation energy are both weakened,the distribution range of water invasion characteristic curve narrows to the recharge/seal boundary,and the corresponding range of gas reservoir recovery factor also narrows.展开更多
It would be very helpful for making countermeasures against complex water scarcity by analysis on systematic water scarcity.Based on the previous researches on water scarcity classification,a classification system of ...It would be very helpful for making countermeasures against complex water scarcity by analysis on systematic water scarcity.Based on the previous researches on water scarcity classification,a classification system of water scarcity was established according to contributing factors,which comprises three water scarcity categories caused by anthropic factors,natural factors and mixed factors respectively.Accordingly,the concept of systematic water scarcity was proposed,which can be defined as one type of water scarcity category caused by the discordance between water demand pattern determined by anthropic factors and water supply pattern controlled by natural factors in an evaluation region during a period.Systematic water scarcity has four features,namely space-time characteristic,scale property,externality and integrity,and can be divided into four developing phases including critical phase,early phase,middle phase and late phase according to various degrees of water scarcity.展开更多
An integrated method that implements multivariate statistical analysis and ML methods to evaluate groundwater quality of the shallow aquifers of the Djerid and Kebili district,Southern Tunisia,was adopted.An evaluatio...An integrated method that implements multivariate statistical analysis and ML methods to evaluate groundwater quality of the shallow aquifers of the Djerid and Kebili district,Southern Tunisia,was adopted.An evaluation of their suitability for irrigation and/or drinking purposes is necessary.A comprehensive hydrochemical assessment of 52 samples with entropy weighted water quality index(EWQI)was also proposed.Eleven water parameters were calculated to ascertain the potential use of those resources in irrigation and drinking.Multivariate analysis showed two main components with Dim1(variance=62.3%)and Dim.2(variance=22%),due to the bicarbonate,dissolution,and evaporation and the intrusion of drainage water.The evaluation of water quality has been carried out using EWQI model.The calculated EWQI for the Djerid and Kebili waters(i.e.,52 samples)varied between 7.5 and 152.62,indicating a range of 145.12.A mean of 79.12 was lower than the median(88.47).From the calculation of EWQI,only 14 samples are not suitable for irrigation because of their poor to extremely poor quality(26.92%).The bivariate plot showed high correlation for EWQI~TH(r=0.93),EWQI~SAR(r=0.87),indicating that water quality depended on those parameters.Diff erent ML algorithms were successfully applied for the water quality classifi cation.Our results indicated high prediction accuracy(SVM>LDA>ANN>kNN)and perfect classifi cation for kNN,LDA and Naive Bayes.For the purposes of developing the prediction models,the dataset was divided into two groups:training(80%)and testing(20%).To evaluate the models’performance,RMSE,MSE,MAE and R^(2) metrics were used.kNN(R^(2)=0.9359,MAE=6.49,MSE=79.00)and LDA(accuracy=97.56%;kappa=96.21%)achieved high accuracy.Moreover,linear regression indicated high correlation for both training(R^(2)=0.9727)and testing data(0.9890).This well confi rmed the validity of LDA algorithm in predicting water quality.Cross validation showed a high accuracy(92.31%),high sensitivity(89.47%)and high specifi city(95%).These fi ndings are fundamentally important for an integrated water resource management in a larger context of sustainable development of the Kebili district.展开更多
The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de...The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de- veloped. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration （LC50） of a pollutant. The data were used to develop a method to evaluate water quality, so as 6＋ 2＋ to give an early indication of toxicity. Four kinds of metal ions （Cu2~, Hg2~, Cr , and Cd ） were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm （GA） was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.展开更多
A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as poll...A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets.展开更多
The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the wester...The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.展开更多
The presence of fecal coliforms is one of the determinants for classification of the quality of water bodies. The aim of this study was to evaluate the relationship between the water quality and surrounding land use i...The presence of fecal coliforms is one of the determinants for classification of the quality of water bodies. The aim of this study was to evaluate the relationship between the water quality and surrounding land use in the area known as the Mantiqueira Ecological Corridor, which straddles the borders of the states of Sao Paulo, Rio de Janeiro and Minas Gerais, in Brazil. More particularly, we studied ten municipalities in Minas Gerais located in the region surrounding Serra do Papagaio State Park and Ibitipoca State Park. We established a classification of water bodies in the area surrounding the collection points in drainage basins based on the principles of sustainability. Using TM/Landsat 5 images, SPOTMap mosaics and the SRTM digital elevation model, we correlated land use classes with the environmental contamination index and topographic characteristics of the area studied. The presence of agriculture and urban areas heightened the differences in water quality classification in the comparison between the dry and rainy seasons, while in forested areas there was a greater equilibrium, with the same classification between the two seasons.展开更多
Long-term and large-scale lake statistics are meaningful for the study of environment change,but many of the existing methods are labourintensive and time-consuming.To overcome this problem,a novel method for long-ter...Long-term and large-scale lake statistics are meaningful for the study of environment change,but many of the existing methods are labourintensive and time-consuming.To overcome this problem,a novel method for long-term and large-scale lake extraction by shape-factorsand machine-learning-based water body classification is proposed.An experiment was conducted to extract the lakes in the Yangtze River basin(YRB)from 2008 to 2018 with the Joint Research Centre’s Global Surface Water Dataset(JRC GSW)data and OSM data.The results show:1)The proposed method is automatically and successfully executed.2)The number of river–lake complexes is between 3008 and 4697,representing 3.56%–5.70%of the total water bodies.3)The areas of the lakes and rivers in the YRB were obtained,and the accuracy of water classification in each year was stable between 90.2%and 93.6%.Comparing the back propagation neural network,random forest,and support vector machine models,we found that the three machine learning models have similar classification accuracy for the scenario.4)Fragmented and incomplete small rivers in the JRC GSW data,unchecked training samples,and overlapped shape factors are the three error sources.Future work will focus on addressing these three error sources.展开更多
基金Under the auspices of Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28020503,XDA23100102)National Key Research and Development Program of China(No.2019YFA0607101)+1 种基金Project of China Geological Survey(No.DD20230505)Excellent Scientific Research and Innovation Team of Universities in Anhui Province(No.2023AH010071)。
文摘As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.
文摘-In this paper,by using ISODATA of fuzzy cluster,the water masses classification of the upper layer in the E-quatorial Western Pacific is carried out. On the basis of the degree of the membership in the obtained optima) classification matrix, the solid distribution of the detailed structure of water masses is made. The water of the upper layer,consisting of six water masses,may be divided into three layers,i, e. ,the surface,subsurface and intermediate layer. Besides analyzing the features of various water masses,a discussion on their distribution structure and formation mechanism is also made.
基金Project supported by the PetroChina-SWPU Innovation Alliance's technological cooperation project(No.2020CX010402).
文摘In order to clarify the characteristics of non-uniform water invasion in water-bearing gas reservoirs,it is necessary to introduce the nonuniformity coefficient(A)and water invasion constant(B)to characterize the non-uniformity degree of reservoir physical properties and the activity degree of peripheral water,respectively,based on the dual mechanism of water invasion to recharge the formation energy and seal off the gas in the reservoir.Then,the material balance method considering the phenomenon of water sealed gas was established.On this basis,the water invasion characteristic curve chart of water-bearing gas reservoirs was plotted,and the non-uniform water invasion mode was classified based on the example gas reservoir.And the following research results were obtained.First,in the water invasion characteristic curve chart of waterbearing gas reservoirs which is plotted based on the material balance method considering the influence of water sealed gas,the upper right area and the lower left area are defined as recharge area and seal area,respectively.By taking A=0 and B=2 as the boundary,the recharge area is divided into strong recharge area and weak recharge area.By taking A=2 and B=2 as the boundary,the seal area is divided into strong seal area and weak seal area.And correspondingly there are four water invasion modes,i.e.,strong recharge,weak recharge,weak seal and strong seal.Second,for fractured gas reservoirs,the non-uniformity degree of reservoir physical properties is high,and water sealed gas can be formed easily after water invasion.The dimensionless relative pseudo-pressure data of this type of gas reservoir is located in the seal area of the water invasion characteristic curve chart.Third,for the gas reservoirs whose reservoir physical properties are relatively uniform,the dimensionless relative pseudo-pressure data is located in the recharge area of the water invasion characteristic curve chart,and the recharge effect of water invasion on formation energy is greater than the weakening effect of water sealed gas on formation energy.Fourth,with the increase of A,the non-uniformity degree of reservoir physical properties increases,the water invasion characteristic curve shifts from the upper right to the lower left,and the recovery factor of gas reservoir decreases continuously.With the increase of B,the recharge effect of water invasion on formation energy and the weakening effect of water sealed gas on formation energy are both weakened,the distribution range of water invasion characteristic curve narrows to the recharge/seal boundary,and the corresponding range of gas reservoir recovery factor also narrows.
基金Supported by the CAS/SAFEA International Partnership Program for Creative Research Teams(KZCX2-YW-T08)Innovation Foundation for Young Scitech Talents of Fujiang Province(2006F3115)
文摘It would be very helpful for making countermeasures against complex water scarcity by analysis on systematic water scarcity.Based on the previous researches on water scarcity classification,a classification system of water scarcity was established according to contributing factors,which comprises three water scarcity categories caused by anthropic factors,natural factors and mixed factors respectively.Accordingly,the concept of systematic water scarcity was proposed,which can be defined as one type of water scarcity category caused by the discordance between water demand pattern determined by anthropic factors and water supply pattern controlled by natural factors in an evaluation region during a period.Systematic water scarcity has four features,namely space-time characteristic,scale property,externality and integrity,and can be divided into four developing phases including critical phase,early phase,middle phase and late phase according to various degrees of water scarcity.
文摘An integrated method that implements multivariate statistical analysis and ML methods to evaluate groundwater quality of the shallow aquifers of the Djerid and Kebili district,Southern Tunisia,was adopted.An evaluation of their suitability for irrigation and/or drinking purposes is necessary.A comprehensive hydrochemical assessment of 52 samples with entropy weighted water quality index(EWQI)was also proposed.Eleven water parameters were calculated to ascertain the potential use of those resources in irrigation and drinking.Multivariate analysis showed two main components with Dim1(variance=62.3%)and Dim.2(variance=22%),due to the bicarbonate,dissolution,and evaporation and the intrusion of drainage water.The evaluation of water quality has been carried out using EWQI model.The calculated EWQI for the Djerid and Kebili waters(i.e.,52 samples)varied between 7.5 and 152.62,indicating a range of 145.12.A mean of 79.12 was lower than the median(88.47).From the calculation of EWQI,only 14 samples are not suitable for irrigation because of their poor to extremely poor quality(26.92%).The bivariate plot showed high correlation for EWQI~TH(r=0.93),EWQI~SAR(r=0.87),indicating that water quality depended on those parameters.Diff erent ML algorithms were successfully applied for the water quality classifi cation.Our results indicated high prediction accuracy(SVM>LDA>ANN>kNN)and perfect classifi cation for kNN,LDA and Naive Bayes.For the purposes of developing the prediction models,the dataset was divided into two groups:training(80%)and testing(20%).To evaluate the models’performance,RMSE,MSE,MAE and R^(2) metrics were used.kNN(R^(2)=0.9359,MAE=6.49,MSE=79.00)and LDA(accuracy=97.56%;kappa=96.21%)achieved high accuracy.Moreover,linear regression indicated high correlation for both training(R^(2)=0.9727)and testing data(0.9890).This well confi rmed the validity of LDA algorithm in predicting water quality.Cross validation showed a high accuracy(92.31%),high sensitivity(89.47%)and high specifi city(95%).These fi ndings are fundamentally important for an integrated water resource management in a larger context of sustainable development of the Kebili district.
基金Project supported by the Natural Science Foundation of Ningbo City (No.2010A610005)the Key Science and Technology Program of Zhejiang Province (No.2011C11049),China
文摘The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de- veloped. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration （LC50） of a pollutant. The data were used to develop a method to evaluate water quality, so as 6＋ 2＋ to give an early indication of toxicity. Four kinds of metal ions （Cu2~, Hg2~, Cr , and Cd ） were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm （GA） was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.
基金primarily supported by the Ministry of Higher Education through MRUN Young Researchers Grant Scheme(MY-RGS),MR001-2019,entitled“Climate Change Mitigation:Artificial Intelligence-Based Integrated Environmental System for Mangrove Forest Conservation,”received by K.H.,S.A.R.,H.F.H.,M.I.M.,and M.M.Asecondarily funded by the UM-RU Grant,ST065-2021,entitled Climate Smart Mitigation and Adaptation:Integrated Climate Resilience Strategy for Tropical Marine Ecosystem.
文摘A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets.
基金The National Natural Science Foundation of China under contract Nos 42106005,91958203,41676131,41876155.
文摘The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.
基金the Brazilian Agricultural Research Corporation,the National Council for Scientific and Technological Development and the Agency of Minas Gerais Research Foundation,for supporting this study.
文摘The presence of fecal coliforms is one of the determinants for classification of the quality of water bodies. The aim of this study was to evaluate the relationship between the water quality and surrounding land use in the area known as the Mantiqueira Ecological Corridor, which straddles the borders of the states of Sao Paulo, Rio de Janeiro and Minas Gerais, in Brazil. More particularly, we studied ten municipalities in Minas Gerais located in the region surrounding Serra do Papagaio State Park and Ibitipoca State Park. We established a classification of water bodies in the area surrounding the collection points in drainage basins based on the principles of sustainability. Using TM/Landsat 5 images, SPOTMap mosaics and the SRTM digital elevation model, we correlated land use classes with the environmental contamination index and topographic characteristics of the area studied. The presence of agriculture and urban areas heightened the differences in water quality classification in the comparison between the dry and rainy seasons, while in forested areas there was a greater equilibrium, with the same classification between the two seasons.
基金supported by the National Nature Science Foundation of China(nos.41971351,41771422,41890822).
文摘Long-term and large-scale lake statistics are meaningful for the study of environment change,but many of the existing methods are labourintensive and time-consuming.To overcome this problem,a novel method for long-term and large-scale lake extraction by shape-factorsand machine-learning-based water body classification is proposed.An experiment was conducted to extract the lakes in the Yangtze River basin(YRB)from 2008 to 2018 with the Joint Research Centre’s Global Surface Water Dataset(JRC GSW)data and OSM data.The results show:1)The proposed method is automatically and successfully executed.2)The number of river–lake complexes is between 3008 and 4697,representing 3.56%–5.70%of the total water bodies.3)The areas of the lakes and rivers in the YRB were obtained,and the accuracy of water classification in each year was stable between 90.2%and 93.6%.Comparing the back propagation neural network,random forest,and support vector machine models,we found that the three machine learning models have similar classification accuracy for the scenario.4)Fragmented and incomplete small rivers in the JRC GSW data,unchecked training samples,and overlapped shape factors are the three error sources.Future work will focus on addressing these three error sources.
