A Tabular Transformer-Based Deep Learning Framework for Flood Prediction Using Long-Term Meteorological Data from Bangladesh

This work suggests a transformer-based SAINT system to predict floods for Bangladesh which outperforms traditional machine learning and deep learning models with an accuracy of 97.83%. It also tackles the issue of class imbalance through the use of the SMOTE method and strengthens the interpretability of the models with the help of explainable artificial intelligence (XAI) using the SHAP approach, which will enable to interpret the contribution of meteorological variables to flood forecasting, giving the model more transparency.