Enhancing Flood Forecasting with BiLSTM Networks

Abstract

Accurate flood forecasting is essential for effective disaster management and mitigation. Traditional models often struggle with the complex temporal dependencies present in flood-related time-series data. This study investigates the use of Bidirectional Long Short-Term Memory (BiLSTM) networks for flood forecasting, leveraging their ability to capture dependencies and context from both past and future data points. We utilize the Historical Daily Weather dataset from the Australian Commonwealth Office of Meteorology, focusing on variables such as precipitation and river discharge to test our model. The BiLSTM model is compared against traditional models and other deep learning algorithms, including simple LSTM, GRU, and RNN networks. Our findings reveal that the BiLSTM model consistently achieves lower root mean square error (RMSE) and mean absolute error (MAE) compared to the other models. Specifically, the BiLSTM model's bidirectional architecture allows it to capture intricate temporal dynamics, resulting in more accurate and reliable flood forecasts. These results underscore the potential of BiLSTM networks as a powerful tool for enhancing flood forecasting and improving disaster preparedness and response.