Water quality in surface bodies remains a pressing issue worldwide.While some regions have rich water quality data,less attention is given to areas that lack sufficient data.Therefore,it is crucial to explore novel wa...Water quality in surface bodies remains a pressing issue worldwide.While some regions have rich water quality data,less attention is given to areas that lack sufficient data.Therefore,it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly.Here we showed sparse-dataset-based prediction of water pollution using machine learning.We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory(LSTM)models,integrated with the Load Estimator(LOADEST).The research was conducted at a river-lake confluence,an area with intricate hydrological patterns.We found that the Self-Attentive LSTM(SA-LSTM)model outperformed the other three machine learning models in predicting water quality,achieving Nash-Sutcliffe Efficiency(NSE)scores of 0.71 for COD_(Mn)and 0.57 for NH_(3)N when utilizing LOADEST-augmented water quality data(referred to as the SA-LSTMLOADEST model).The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error(RMSE)by 24.6%for COD_(Mn)and 21.3%for NH_(3)N.Furthermore,the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly.Additionally,the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance.This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23040502)National Natural Science Foundation of China(41890823)Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences(No.WL2019003).
文摘Water quality in surface bodies remains a pressing issue worldwide.While some regions have rich water quality data,less attention is given to areas that lack sufficient data.Therefore,it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly.Here we showed sparse-dataset-based prediction of water pollution using machine learning.We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory(LSTM)models,integrated with the Load Estimator(LOADEST).The research was conducted at a river-lake confluence,an area with intricate hydrological patterns.We found that the Self-Attentive LSTM(SA-LSTM)model outperformed the other three machine learning models in predicting water quality,achieving Nash-Sutcliffe Efficiency(NSE)scores of 0.71 for COD_(Mn)and 0.57 for NH_(3)N when utilizing LOADEST-augmented water quality data(referred to as the SA-LSTMLOADEST model).The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error(RMSE)by 24.6%for COD_(Mn)and 21.3%for NH_(3)N.Furthermore,the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly.Additionally,the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance.This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.
