Flood Prediction Using Deep Neural Networks Across a Large and Complex River System

Accurately predicting streamflow poses a considerable challenge particularly for intense storm events occurring across complex river systems. To tackle this issue, we developed multiple deep neural network models including a Long Short-term Memory (LSTM) and Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) to predict short duration (1-hour) flood hydrographs. LSTM excels in preserving prolonged dependencies in structured time series data, while N-HiTS introduces an innovative deep neural architecture characterized by backward and forward residual links and a deep stack of fully connected layers. In addition, N-HiTS employs a combination of multi-rate sampling and multi-scale synthesis of predictions, resulting in a hierarchical forecasting structure that reduces computational requirements and enhances accuracy. Our goal was to evaluate the robustness and effectiveness of these advanced algorithms by comparing them with the National Water Model (NWM) forecast, across a large and complex river system i.e., the Wateree River Basin in South Carolina, USA. The models were trained and tested using precipitation, temperature, humidity, and solar radiation data during the periods 01/01/2009 to 09/30/2022 and 10/1/2022 to 01/01/2024, respectively.  Analysis suggests that N-HiTS showcased state-of-the-art performance and enhanced hourly flood forecasting accuracy by approximately 10% compared to LSTM and NWM with a negligible difference in computational costs. N-HiTS was able to more accurately forecast time to peak and peak rate values of hourly flood hydrographs compared to the LSTM and NWM. Our extensive experiments revealed the importance of multi-rate input sampling and hierarchical interpolation approaches designed within the N-HiTS model that drastically improved the flood forecasting and interpretability of the predictions.