Mountain Flood Level Forecasting in Small Watersheds Based on Recurrent Neural Networks and Multi-Dimensional Data

Due to the many disaster-causing factors of mountain torrents, rapid changes in time and space, and rapid disasters, the traditional hydrological models and general data-driven mountainous flood forecasting models in small watershed areas have low accuracy and timeliness. Therefore, this paper proposed the time series Recurrent Neural Networks (RNN) and data processing for small mountain flood level forecasting, it includes base RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model. We use the Tiaoxi catchment’s mountain flood data including hydrology, meteorology, geography and human activities at Zhejiang province of China. We construct multi-dimensional time series data processing and the model test system, and develop multi-dimensional disaster factor data on an hourly scale to suit the training and prediction of Deep Learning (DL)’s structure. The experiment shows that time series base RNN, LSTM and GRU models can be considered as the efficient hydrology modeling approach. The LSTM model demonstrated the best accuracy and real-time in the study area.