Machine Learning-Driven Rainfall Forecasting Model for Sustainable and Adaptive Infrastructure Planning

Climate variability and extreme rainfall events pose important problems to infrastructure development in Bangladesh, where standard statistical models generally fail to incorporate nonlinear anomalies and low-frequency extremes. This study utilizes two machine learning approaches, Prophet and Long Short-Term Memory (LSTM) networks, to anticipate long-term yearly rainfall using historical data from 1980 to 2024 across two climatically susceptible 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 Prophet in both Rajshahi and Ishwardi, obtaining lower RMSE values (102.4 mm and 118.7 mm, respectively) and higher R² scores (0.88 and 0.85). While Prophet generated smoother forecasts, it underfitted severe years, whereas LSTM correctly captured interannual volatility and identified a greater number of high-risk years above the 90th percentile threshold. The forecasts were turned into practical adaptation techniques, including elevated foundation design, decentralized rainwater collecting, and the use of water-resistant materials, therefore integrating predictive analytics into civil engineering applications. This integration provides a framework for climate-resilient infrastructure planning aligned with national adaptation plans. Future studies should expand toward multivariate forecasting that incorporates exogenous climate drivers such as ENSO, soil moisture, and vegetation indices, as well as examine scalable integration with GIS and BIM platforms for real-time urban resilience design.