Evaluation of deep learning models for flood forecasting in Bangladesh - Rumee, Asif Rahman (Journal of Computer Sciences Institute, 2025)
Flooding is a recurrent and devastating issue in Bangladesh, largely due to its geographical and climatic conditions. This study examined the performance of four deep learning architectures Feed-forward Neural Network (FNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) in predicting floods in Bangladesh. Utilizing a binary classification dataset of historical meteorological and hydrological data, the findings revealed that GRU outperformed the other models, achieving an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1-score of 99%. In contrast, LSTM attained an accuracy of 96%, a precision of 99%, a recall of 95%, and an F1-score of 97%. These results underscored the effectiveness of GRU for operational flood forecasting, which was critical for enhancing disaster preparedness in the region.