The Langat River Basin in Malaysia is vulnerable to soil erosion risks because of its exposure to intensive land use activities and its topography,which primarily consists of steep slopes and mountainous areas.Further...The Langat River Basin in Malaysia is vulnerable to soil erosion risks because of its exposure to intensive land use activities and its topography,which primarily consists of steep slopes and mountainous areas.Furthermore,climate change frequently exposes this basin to drought,which negatively affects soil and water conservation.However,recent studies have rarely shown how soil reacts to drought,such as soil erosion.Therefore,the purpose of this study is to evaluate the relationship between drought and soil erosion in the Langat River Basin.We analyzed drought indices using Landsat 8 satellite images in November 2021,and created the normalized differential water index(NDWI)via Landsat 8 data to produce a drought map.We used the revised universal soil loss equation(RUSLE)model to predict soil erosion.We verified an association between the NDWI and soil erosion data using a correlation analysis.The results revealed that the southern and northern regions of the study area experienced drought events.We predicted an average annual soil erosion of approximately 58.11 t/(hm^(2)·a).Analysis of the association between the NDWI and soil erosion revealed a strong positive correlation,with a Pearson correlation coefficient of 0.86.We assumed that the slope length and steepness factor was the primary contributor to soil erosion in the study area.As a result,these findings can help authorities plan effective measures to reduce the impacts of drought and soil erosion in the future.展开更多
Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural ...Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.展开更多
The Langat River in Malaysia has been experiencing anthropogenic input from urban, rural and industrial activities for many years. Sewage contamination, possibly originating from the greater than three million inhabit...The Langat River in Malaysia has been experiencing anthropogenic input from urban, rural and industrial activities for many years. Sewage contamination, possibly originating from the greater than three million inhabitants of the Langat River Basin, were examined. Sediment samples from 22 stations （SL01-SL22） along the Langat River were collected, extracted and analysed by GC-MS. Six different sterols were identified and quantified. The highest sterol concentration was found at station SL02 （618.29 ng/g dry weight）, which situated in the Balak River whereas the other sediment samples ranged between 11.60 and 446.52 ng/g dry weight. Sterol ratios were used to identify sources, occurrence and partitioning of faecal matter in sediments and majority of the ratios clearly demonstrated that sewage contamination was occurring at most stations in the Langat River. A multivariate statistical analysis was used in conjunction with a combination of biomarkers to better understand the data that clearly separated the compounds. Most sediments of the Langat River were found to contain low to mid-range sewage contamination with some containing ＇significant＇ levels of contamination. This is the first report on sewage pollution in the Langat River based on a combination of biomarker and multivariate statistical approaches that will establish a new standard for sewage detection using faecal sterols.展开更多
文摘The Langat River Basin in Malaysia is vulnerable to soil erosion risks because of its exposure to intensive land use activities and its topography,which primarily consists of steep slopes and mountainous areas.Furthermore,climate change frequently exposes this basin to drought,which negatively affects soil and water conservation.However,recent studies have rarely shown how soil reacts to drought,such as soil erosion.Therefore,the purpose of this study is to evaluate the relationship between drought and soil erosion in the Langat River Basin.We analyzed drought indices using Landsat 8 satellite images in November 2021,and created the normalized differential water index(NDWI)via Landsat 8 data to produce a drought map.We used the revised universal soil loss equation(RUSLE)model to predict soil erosion.We verified an association between the NDWI and soil erosion data using a correlation analysis.The results revealed that the southern and northern regions of the study area experienced drought events.We predicted an average annual soil erosion of approximately 58.11 t/(hm^(2)·a).Analysis of the association between the NDWI and soil erosion revealed a strong positive correlation,with a Pearson correlation coefficient of 0.86.We assumed that the slope length and steepness factor was the primary contributor to soil erosion in the study area.As a result,these findings can help authorities plan effective measures to reduce the impacts of drought and soil erosion in the future.
文摘Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.
基金the Universiti Kebangsaan Malaysia for the OUP Fund(OUP-UKM-FST-2011)
文摘The Langat River in Malaysia has been experiencing anthropogenic input from urban, rural and industrial activities for many years. Sewage contamination, possibly originating from the greater than three million inhabitants of the Langat River Basin, were examined. Sediment samples from 22 stations （SL01-SL22） along the Langat River were collected, extracted and analysed by GC-MS. Six different sterols were identified and quantified. The highest sterol concentration was found at station SL02 （618.29 ng/g dry weight）, which situated in the Balak River whereas the other sediment samples ranged between 11.60 and 446.52 ng/g dry weight. Sterol ratios were used to identify sources, occurrence and partitioning of faecal matter in sediments and majority of the ratios clearly demonstrated that sewage contamination was occurring at most stations in the Langat River. A multivariate statistical analysis was used in conjunction with a combination of biomarkers to better understand the data that clearly separated the compounds. Most sediments of the Langat River were found to contain low to mid-range sewage contamination with some containing ＇significant＇ levels of contamination. This is the first report on sewage pollution in the Langat River based on a combination of biomarker and multivariate statistical approaches that will establish a new standard for sewage detection using faecal sterols.
