Machine-learning-based water quality management of river with serial impoundments in the Republic of Korea

Abstract

Euiam Lake in the Republic of Korea This study establishes a framework to prioritize total phosphorus (TP) management strategies based on machine learning (ML). A comparative analysis is conducted to evaluate the performance of four ML methods: random forest (RF), extreme gradient boosting (XGBoost), deep neural network (DNN), and long short-term memory (LSTM). The LSTM-based model is selected as the optimal predictive model of TP concentration in Euiam Lake (E_TP) on seasons (May to October) with high rainfall and inflow from two upstream dams (Chuncheon Dam and Soyanggang Dam). We also perform a gradient-based analysis to figure out the most influential factors on E_TP using the LSTM model. The top four priority factors are TP concentrations and suspended solids concentrations in the upstream dams. This application of the gradient-based analysis enables the predictive model to discuss quantitative reductions in the priorities. Based on these numerical results, we anticipate that the proposed framework can enhance the feasibility of management practices for achieving the water quality management goal of the study region. This study demonstrates that a robust predictive model can be developed for a serial impoundment system with distinct seasonal characteristics of rainfall, temperature, and water quality, thereby facilitating the selection of management priorities. Based on the predictive model results, we conclude that it is the key for managing the target TP concentration to prioritize the incoming TP concentrations and determine the quantitative • Water quality management strategy is established using RF, XGBoost, DNN, and LSTM. • The LSTM model outperforms the other ML methods when predicting the TP concentration in Euiam Lake. • The LSTM gradient-based analysis determines TP management priorities and their quantitative reduction.