Water quality prediction based on sparse dataset using enhanced machine learning  

作　　者：Sheng Huang Jun Xia Yueling Wang Jiarui Lei Gangsheng Wang 

机构地区：[1]State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,China [2]Institute for Water-Carbon Cycles and Carbon Neutrality,Wuhan University,Wuhan 430072,China [3]Department of Civil and Environmental Engineering,National University of Singapore,117578 Singapore [4]Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China

出　　处：《Environmental Science and Ecotechnology》2024年第4期218-228,共11页环境科学与生态技术（英文）

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).

摘　　要：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.

关 键 词：Water quality modeling Sparse measurement River-lake confluence Long short-term memory Load estimator Machine learning 

分 类 号：X703[环境科学与工程—环境工程]