Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake - Deng, Bin; Liu, Pan; Chin, Ren Jie; Kumar, Pavitra; Jiang, Changbo; Xiang, Yifei; Liu, Yizhuang; Lai, Sai Hin; Luo, Hongmei (Frontiers in Earth Science, 2022)
Dongting Lake is the second-largest freshwater lake in China. An accurate and reliable water level prediction in such a lake system is crucial for water resources management, lake wetland protection, flood control, etc. Therefore, this paper takes the hourly water level, flow rate, rainfall, temperature data from the upstream stations (Xiangjiang, Zishui, Yuanjiang, Jianghe and mainstream of Yangtze River), as well as the rainfall data of the representative stations from the West, South and East Dongting Lake respectively, with the data duration from year 2004 to 2021, as the input features to establish hybrid metaheuristic machine learning-based model for water level prediction in Dongting Lake areas. In this study, multilayer perceptron neural network (MLP-NN), Elman neural network (ENN), and integration of particle swarm optimisation algorithm to Elman neural network (PSO-ENN) were selected as the model development techniques. The developed models were compared using a series of statistical analyses. The PSO-ENN model appears as the model which can provide better accuracy in terms of water level prediction in Dongting Lake. It records NSE of 0.929 to 0.988, RMSE of 0.129 to 0.322 and MAE of 0.151 to 0.359 at the representative stations of the West, South and East Dongting Lake. It also shows its ability to provide better performance for the prediction of 36 hours in advance. In terms of input variables sensitivity, the developed model is most sensitive to flow rate, followed by rainfall.