Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation

Accurate runoff prediction is one of the important tasks in various fields such as agriculture, hydrology, and environmental studies. Recently, with massive improvements computational system hardware, deep learning-based approach has recently been applied for more accurate prediction. In this study, long short-term memory model sequence-to-sequence structure was hourly predictions from 2015 to 2019 Russian River basin, California, USA. The proposed used predict lead time 1–6 h using data observed at upstream stations. evaluated terms event-based performance statistical metrics including root mean square error, Nash-Sutcliffe Efficiency, peak error. results show that outperforms support vector machine conventional models. addition, best predictive ability events, which means it can be effective developing flood forecasting warning systems. study demonstrate high power method improve results.