Machine learning-driven rainfall forecasting model for sustainable and adaptive infrastructure planning

Climate variability and extreme rainfall events pose significant challenges to infrastructure development in Bangladesh, where standard statistical models often fail to account for nonlinear anomalies and low-frequency extremes. This study employs two machine learning approaches, Prophet and Long Short-Term Memory (LSTM) networks, to predict long-term yearly rainfall using historical data from 1980 to 2024 across two climatically sensitive locations. Prophet, an additive decomposition model, and LSTM, a recurrent neural network with memory-based learning, were benchmarked using Root Mean Squared Error (RMSE) and the Coefficient of Determination (R2). Results demonstrate that LSTM consistently outperformed…