Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction...Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.展开更多
Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the...Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the reservoir water level is an essential physical indicator for the reservoirs.Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies.In recent years,deep learning models have been widely applied to solve forecasting problems.In this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water levels.It utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir images.Adam is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during training.We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam.To assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same dataset.The model performances were validated using k-fold cross-validation and ANOVA analysis.The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models.It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.展开更多
The Lower Mekong Delta in Vietnam experiences widespread flooding annually. About 17 million people live in the Delta with agriculture as the major economic activity. The suspended sediment load in the Mekong River pl...The Lower Mekong Delta in Vietnam experiences widespread flooding annually. About 17 million people live in the Delta with agriculture as the major economic activity. The suspended sediment load in the Mekong River plays an important role in carrying contaminants and nutrients to the delta and changing the geomorphology of the delta river system. In recent decades, it is generally perceived that the flow and sediment transport in the Mekong River have changed due to climate change and development activities, but observed sediment data are lacking. Moreover, after natural floodplains, the sediment deposition has replaced by dense river systems as resulting in floodplain compartments protected by embankments. This study is aimed to investigate impacts of changing water flow on erosion/deposition in the Lower Mekong Delta. We used Mike 11 hydrodynamic model and sediment transport model for simulating the flow and sediment transport. Various scenarios were simulated based on anticipated upstream discharges. Our findings provide the positive and negative impacts to the changes in sediment transport on agriculture cultivation in the Lower Mekong Delta.展开更多
This paper evaluated the probability of landslide susceptibilities through the applica-tion of the Transient Rainfall Infiltration and Grid-Based Region Slope-Stability model in Cau river basin (Vietnam) using the sce...This paper evaluated the probability of landslide susceptibilities through the applica-tion of the Transient Rainfall Infiltration and Grid-Based Region Slope-Stability model in Cau river basin (Vietnam) using the scenarios-based approach under the influence of the warming climate. The tested cases were developed based on various options including rainfall amount and distribution, soil depth determination, and land-cover conditions. Input data for extreme rain events included historical rainstorm in 2013, the Probable Maximum Precipitation (PMP) with the durations of 24 hours and 48 hours. The results illustrated the reduction of slope stability when the land cover changed from land-use data in 2007 (Ha12) to land-use data in 2015 (Ha22). When the whole region was assumed to be replaced by soil (Ha02), the factor of safety (Fs) decreased to lower magnitude when compared to Fs value regarding to changes in land cover condition (Ha12 & Ha22) and changes in soil-depth (Ha33). The model simulations demonstrated the agreement with the slope-failure hazard association with the destabilizing factor such as slope-cutting activities at historical landslide events. Under the same land-cover and soil depth condition, the average value of factor of safety regarding to the historical rainstorm in 2013 (Ha32) declined by 0.069 and 0.189 when compared to Fs of the 24-hour PMP with the storm distribution type 3 (1332) and Fs of the 48-hour PMP with the storm distribution type 3 (2332), respectively. The results reveal that in a warming climate, changes in extreme precipitation in terms of rain-total, rain-duration, and rain-distribution would result in the expansion of slope instability in the hilly region. This application is considered as a prevailing method for landslide susceptibility analysis and would provide important information for authorities in developing adequate land-management in the river basin.展开更多
<span style="font-family:Verdana;">The Near-equatorial orbit (NEqO) satellite represent</span><span style="font-family:Verdana;">s</span><span style="font-family:Ver...<span style="font-family:Verdana;">The Near-equatorial orbit (NEqO) satellite represent</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> a new generation of optical satellite images characterized by nonlinear distortion when captured. Conventional modeling techniques are insufficient to overcome the geometric distortion in these satellite images. This study proposes a new methodology for overcom</span><span style="font-family:Verdana;">ing</span><span style="font-family:Verdana;"> the geometric distortion of the NEqO images. The data used are obtained from RazakSAT and SPOT-5 satellite images in Malaysia. The method starts with applying the RI-SIFT algorithm to extract control points (CPs) automatically. These CPs are used to solve for the transformation parameters of the geometric correction model by applying spline transformations. The result </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">verified through statistical comparison: 1) geometric correction on the RazakSAT image is performed with Spot satellite image with using first-order polynomial trans-formation. 2) Then calculate the root mean square error (RMSE)</span><span style="font-family:Verdana;">. </span><span style="font-family:;" "=""><span style="font-family:Verdana;">3) Compare the calculated RMSE with that obtained from the first step with that of the proposed method. The RMSE value of the geometric corrections using the proposed method was 7.08 × 10</span><sup><span style="font-family:Verdana;"><span style="white-space:nowrap;">−</span>9</span></sup><span style="font-family:Verdana;"> m. The proposed method provides promising results.</span></span>展开更多
The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generat...The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs,their matching has high false alarm.This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference(SAD)in different manner for the new optical satellite generation called near-equatorial orbit satellite(NEqO)and multi-sensor images.The proposed method leads to improving CPs matching with a significantly higher rate of correct matches.The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area.The proposed method consists of three parts:(1)applying the SIFT to extract CPs automatically,(2)refining CPs matching by SAD algorithm with empirical threshold,and(3)evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method.The result indicates an accurate and precise performance of the model,which showed the effectiveness and robustness of the proposed approach.展开更多
Researchers in the remote sensing field use different types of images from satellite systems and simulator devices, such as goniometers. However, no device can simulate the new generation of optical satellite system c...Researchers in the remote sensing field use different types of images from satellite systems and simulator devices, such as goniometers. However, no device can simulate the new generation of optical satellite system called near-equatorial satellite system to perform different kinds of remote sensing applications in equatorial regions. This study proposed a newly invented laboratory and fieldwork goniometer designed to simulate and capture intensity variation and measure the bidirectional spectral reflectance of earth surface. The proposed goniometer is a multi-purpose and multi-field device. It is able to simulate different satellite systems and measure the intensity variation and spectral reflectance of earth’s surface features with freely azimuth and zenith angles of sensors and illumination source in fieldwork and/or laboratory. However, the system of invention is focusing on specific satellite orbital to work with the parameters and properties of NEqO satellite system in order to obtain NEqO system imagery for performing different applications such as geometric correction, relative radiometric normalization and change detection for future work. The significant of this invention is that most of the invented goniometers of remote sensing are able to work just in field or just in laboratory and use, carry just optical sensor or hyperspectral sensor. Specifically, our invention can do all these functions that are not available in existing goniometers. The proposed device offers several advantages, namely, high measurement speed, flexibility, low cost, efficiency, and possible measurement depending on the free zenith/azimuth angles of sensors and illumination sources. The proposed goniometer includes ten parts, and two different sensors (optical and hyperspectral).展开更多
An-Najaf province is situated in south-western part of Iraq. It is 70 meters above the sea level in the dry desert environment. The city is considered as one of the most important cities in Iraq, facing a fast populat...An-Najaf province is situated in south-western part of Iraq. It is 70 meters above the sea level in the dry desert environment. The city is considered as one of the most important cities in Iraq, facing a fast population growth and continuous development in constructions such as residential complexes, hotels, bridges and shopping malls. Soil investigation data for An-Najaf Province (An-Najaf and Kufa cities) from 464 boreholes drilled by the Na-tional Centre for Construction Laboratories & Researches (NCCLR)/Babylon laboratory were used in this research. The data were analysed and possessed using Excel program then represented on the Geographical Information System (GIS) program by Inverse Distance Weighted (IDW) tool to create an allowable bearing capacity map for the soil at depths 0 - 2 meters. The allowable bearing capacity is one of the most important soil characteristics to be considered when making a database for An-Najaf city soil. Geographical Information System GIS program enables to create reliable database for any characteristic and it is one of the best programs to produce an accurate map and allow ease in dealing with it. Those maps cover all the studied areas and by using contour lines, approximate values for no-data areas can be obtained. The results show that the allowable bearing capacity range is 5 - 20 Ton/m2 for both An-Najaf and Kufa cities. Kufa city has the range 5 - 9 Ton/m2. An-Najaf city has the range 7 - 18 at the center, 8 - 10 Ton/m2 at the north eastern part, 7 - 14 Ton/m2 for the north western part, 6 - 12 Ton/m2 at the south eastern and 12 - 19 Ton/m2 at the south western.展开更多
Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs.This paper presents a new hybrid hydrological model based on the combination of the ...Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs.This paper presents a new hybrid hydrological model based on the combination of the Hydrologic Engineering Center-Hydrologic Modeling System(HEC-HMS)hydrological model and an Encoder-Decoder-Long Short-Term Memory network to enhance the accuracy of real-time flood forecasting.The proposed hybrid model has been applied to the Krong H'nang hydropower reservoir.The observed data from 33 floods monitored between 2016 and 2021 are used to calibrate,validate,and test the hybrid model.Results show that the HEC-HMS-artificial neural network hybrid model significantly improves the forecast quality,especially for results at a longer forecasting time.In detail,the Kling-Gupta efficiency(KGE)index,for example,increased from ΔKGE=16%at time t+1h to ΔKGE=69%at time t+6 h.Similar results were obtained for other indicators including peak error and volume error.The computer program developed for this study is being used in practice at the Krong H'nang hydropower to aid in reservoir planning,flood control,and water resource efficiency.展开更多
基金Scientific Research and Technology Development Project。
文摘Model accuracy and runtime are two key issues for flood warnings in rivers.Traditional hydrodynamic models,which have a rigorous physical mechanism for flood routine,have been widely adopted for water level prediction in river,lake,and urban areas.However,these models require various types of data,in-depth domain knowledge,experience with modeling,and intensive computational time,which hinders short-term or real-time prediction.In this paper,we propose a new framework based on machine learning methods to alleviate the aforementioned limitation.We develop a wide range of machine learning models such as linear regression(LR),support vector regression(SVR),random forest regression(RFR),multilayer perceptron regression(MLPR),and light gradient boosting machine regression(LGBMR)to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010,2012,and 2020.Four evaluation metrics,that is,R^(2),Nash-Sutcliffe efficiency,mean absolute error,and root mean square error,are employed to examine the reliability of the proposed models.The results show that the LR model outperforms the SVR,RFR,MLPR,and LGBMR models.
基金funded by International School,Vietnam National University,Hanoi(VNU-IS)under project number CS.2023-10.
文摘Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the reservoir water level is an essential physical indicator for the reservoirs.Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies.In recent years,deep learning models have been widely applied to solve forecasting problems.In this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water levels.It utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir images.Adam is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during training.We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam.To assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same dataset.The model performances were validated using k-fold cross-validation and ANOVA analysis.The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models.It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.
文摘The Lower Mekong Delta in Vietnam experiences widespread flooding annually. About 17 million people live in the Delta with agriculture as the major economic activity. The suspended sediment load in the Mekong River plays an important role in carrying contaminants and nutrients to the delta and changing the geomorphology of the delta river system. In recent decades, it is generally perceived that the flow and sediment transport in the Mekong River have changed due to climate change and development activities, but observed sediment data are lacking. Moreover, after natural floodplains, the sediment deposition has replaced by dense river systems as resulting in floodplain compartments protected by embankments. This study is aimed to investigate impacts of changing water flow on erosion/deposition in the Lower Mekong Delta. We used Mike 11 hydrodynamic model and sediment transport model for simulating the flow and sediment transport. Various scenarios were simulated based on anticipated upstream discharges. Our findings provide the positive and negative impacts to the changes in sediment transport on agriculture cultivation in the Lower Mekong Delta.
文摘This paper evaluated the probability of landslide susceptibilities through the applica-tion of the Transient Rainfall Infiltration and Grid-Based Region Slope-Stability model in Cau river basin (Vietnam) using the scenarios-based approach under the influence of the warming climate. The tested cases were developed based on various options including rainfall amount and distribution, soil depth determination, and land-cover conditions. Input data for extreme rain events included historical rainstorm in 2013, the Probable Maximum Precipitation (PMP) with the durations of 24 hours and 48 hours. The results illustrated the reduction of slope stability when the land cover changed from land-use data in 2007 (Ha12) to land-use data in 2015 (Ha22). When the whole region was assumed to be replaced by soil (Ha02), the factor of safety (Fs) decreased to lower magnitude when compared to Fs value regarding to changes in land cover condition (Ha12 & Ha22) and changes in soil-depth (Ha33). The model simulations demonstrated the agreement with the slope-failure hazard association with the destabilizing factor such as slope-cutting activities at historical landslide events. Under the same land-cover and soil depth condition, the average value of factor of safety regarding to the historical rainstorm in 2013 (Ha32) declined by 0.069 and 0.189 when compared to Fs of the 24-hour PMP with the storm distribution type 3 (1332) and Fs of the 48-hour PMP with the storm distribution type 3 (2332), respectively. The results reveal that in a warming climate, changes in extreme precipitation in terms of rain-total, rain-duration, and rain-distribution would result in the expansion of slope instability in the hilly region. This application is considered as a prevailing method for landslide susceptibility analysis and would provide important information for authorities in developing adequate land-management in the river basin.
文摘<span style="font-family:Verdana;">The Near-equatorial orbit (NEqO) satellite represent</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> a new generation of optical satellite images characterized by nonlinear distortion when captured. Conventional modeling techniques are insufficient to overcome the geometric distortion in these satellite images. This study proposes a new methodology for overcom</span><span style="font-family:Verdana;">ing</span><span style="font-family:Verdana;"> the geometric distortion of the NEqO images. The data used are obtained from RazakSAT and SPOT-5 satellite images in Malaysia. The method starts with applying the RI-SIFT algorithm to extract control points (CPs) automatically. These CPs are used to solve for the transformation parameters of the geometric correction model by applying spline transformations. The result </span><span style="font-family:Verdana;">is </span><span style="font-family:Verdana;">verified through statistical comparison: 1) geometric correction on the RazakSAT image is performed with Spot satellite image with using first-order polynomial trans-formation. 2) Then calculate the root mean square error (RMSE)</span><span style="font-family:Verdana;">. </span><span style="font-family:;" "=""><span style="font-family:Verdana;">3) Compare the calculated RMSE with that obtained from the first step with that of the proposed method. The RMSE value of the geometric corrections using the proposed method was 7.08 × 10</span><sup><span style="font-family:Verdana;"><span style="white-space:nowrap;">−</span>9</span></sup><span style="font-family:Verdana;"> m. The proposed method provides promising results.</span></span>
文摘The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs,their matching has high false alarm.This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference(SAD)in different manner for the new optical satellite generation called near-equatorial orbit satellite(NEqO)and multi-sensor images.The proposed method leads to improving CPs matching with a significantly higher rate of correct matches.The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area.The proposed method consists of three parts:(1)applying the SIFT to extract CPs automatically,(2)refining CPs matching by SAD algorithm with empirical threshold,and(3)evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method.The result indicates an accurate and precise performance of the model,which showed the effectiveness and robustness of the proposed approach.
文摘Researchers in the remote sensing field use different types of images from satellite systems and simulator devices, such as goniometers. However, no device can simulate the new generation of optical satellite system called near-equatorial satellite system to perform different kinds of remote sensing applications in equatorial regions. This study proposed a newly invented laboratory and fieldwork goniometer designed to simulate and capture intensity variation and measure the bidirectional spectral reflectance of earth surface. The proposed goniometer is a multi-purpose and multi-field device. It is able to simulate different satellite systems and measure the intensity variation and spectral reflectance of earth’s surface features with freely azimuth and zenith angles of sensors and illumination source in fieldwork and/or laboratory. However, the system of invention is focusing on specific satellite orbital to work with the parameters and properties of NEqO satellite system in order to obtain NEqO system imagery for performing different applications such as geometric correction, relative radiometric normalization and change detection for future work. The significant of this invention is that most of the invented goniometers of remote sensing are able to work just in field or just in laboratory and use, carry just optical sensor or hyperspectral sensor. Specifically, our invention can do all these functions that are not available in existing goniometers. The proposed device offers several advantages, namely, high measurement speed, flexibility, low cost, efficiency, and possible measurement depending on the free zenith/azimuth angles of sensors and illumination sources. The proposed goniometer includes ten parts, and two different sensors (optical and hyperspectral).
文摘An-Najaf province is situated in south-western part of Iraq. It is 70 meters above the sea level in the dry desert environment. The city is considered as one of the most important cities in Iraq, facing a fast population growth and continuous development in constructions such as residential complexes, hotels, bridges and shopping malls. Soil investigation data for An-Najaf Province (An-Najaf and Kufa cities) from 464 boreholes drilled by the Na-tional Centre for Construction Laboratories & Researches (NCCLR)/Babylon laboratory were used in this research. The data were analysed and possessed using Excel program then represented on the Geographical Information System (GIS) program by Inverse Distance Weighted (IDW) tool to create an allowable bearing capacity map for the soil at depths 0 - 2 meters. The allowable bearing capacity is one of the most important soil characteristics to be considered when making a database for An-Najaf city soil. Geographical Information System GIS program enables to create reliable database for any characteristic and it is one of the best programs to produce an accurate map and allow ease in dealing with it. Those maps cover all the studied areas and by using contour lines, approximate values for no-data areas can be obtained. The results show that the allowable bearing capacity range is 5 - 20 Ton/m2 for both An-Najaf and Kufa cities. Kufa city has the range 5 - 9 Ton/m2. An-Najaf city has the range 7 - 18 at the center, 8 - 10 Ton/m2 at the north eastern part, 7 - 14 Ton/m2 for the north western part, 6 - 12 Ton/m2 at the south eastern and 12 - 19 Ton/m2 at the south western.
基金Vingroup JSCMaster,PhD Scholarship Program of Vingroup Innovation Foundation,Grant/Award Number:VINIF.2021.ThS.97。
文摘Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs.This paper presents a new hybrid hydrological model based on the combination of the Hydrologic Engineering Center-Hydrologic Modeling System(HEC-HMS)hydrological model and an Encoder-Decoder-Long Short-Term Memory network to enhance the accuracy of real-time flood forecasting.The proposed hybrid model has been applied to the Krong H'nang hydropower reservoir.The observed data from 33 floods monitored between 2016 and 2021 are used to calibrate,validate,and test the hybrid model.Results show that the HEC-HMS-artificial neural network hybrid model significantly improves the forecast quality,especially for results at a longer forecasting time.In detail,the Kling-Gupta efficiency(KGE)index,for example,increased from ΔKGE=16%at time t+1h to ΔKGE=69%at time t+6 h.Similar results were obtained for other indicators including peak error and volume error.The computer program developed for this study is being used in practice at the Krong H'nang hydropower to aid in reservoir planning,flood control,and water resource efficiency.
