Prediction of pollution loads in agricultural reservoirs using LSTM algorithm: case study of reservoirs in Nonsan City

Heesung  Lim1   Hyunuk  An1   Gyeongsuk  Choi2   Jaenam  Lee3   Jongwon  Do4,*   

1Department of Agricultural and Rural Engineering, Chungnam National University, Daejeon 34134, Korea
2Department of Agricultural Civil Engineering, Institute of Agricultural Science & Technology, Kyungpook National University, Daegu 41566, Korea
3Department of Rural Research Institute, Korea Rural Community Corporation, Naju 58327, Korea
4Department of Rural Research Institute, Korea Rural Community Corporation, Sejong 30130, Korea


The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [T-P]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R2 that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

Figures & Tables

Fig. 1. Structure of recurrent neural network (RNN) and long-short term memory (LSTM). h, hidden state; X, input gate; tanh, hyper tangent; σ, sigmoid.