Long Short-Term Memory-based simulation study of river happiness evaluation – A case study of Jiangsu section of Huaihe River Basin in China

Abstract

Real-time prediction of the state of the river itself and the degree of its benefit to the people is the leading way to achieve human-water harmony. Using the indicator scoring method as the evaluation method, we used the river evaluation data and results with time series characteristics as features and labels and applied the concept of transfer learning to Long Short-Term Memory to establish six subsystems, including water safety, water quality, economic contribution, water ecology, water management and water culture, to conduct a real-time rolling evaluation simulation study on the degree of river happiness in the Jiangsu section of the Huaihe River Basin in China. The empirical results show that the maximum Root Mean Square Error (RMSE) of the training set and test set of each system is 0.0226, and the lowest coefficient of determination R2 is 0.9011, which proves that the model fits well, according to which the relevant data of the watershed in June 2022 are brought in, and the evaluation result is obtained as 89.77 points. The overall trend is good, but a certain tendency to fall back at the level of economic contribution can be found, and the reasons are analyzed objectively.