Evaluation of deep learning models for flood forecasting in Bangladesh

Evaluation of deep learning models for flood forecasting in Bangladesh

Blog Article

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 keychron m4 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 diamond painting strand en zee flood forecasting, which was critical for enhancing disaster preparedness in the region.