Rainfall frequency and uncertainty analysis in arid regions using machine learning and statistical distribution models based on a 91-year rainfall record.

Accurate estimation of rainfall depths associated with standard return periods is fundamental for hydraulic design, flood risk assessment, and water resources management, particularly in arid regions. Statistical extreme value distributions have long been the standard approach for rainfall frequency analysis, whereas machine learning (ML) has recently emerged as a potential alternative. This study presents a comprehensive comparison between twenty-five statistical distribution models and three supervised ML regressors-Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Regressor (XGBR)-for Annual Maximum Daily Rainfall (AMDR) frequency analysis using a 91-year record (1931-2021) from the Suez meteorological station, Egypt. Prior to frequency analysis, the AMDR series was verified to satisfy the assumptions of stationarity and serial independence using the Mann-Kendall test, Sen's slope estimator, and Lag-1 autocorrelation analysis. Statistical distribution models were implemented in HEC-SSP using Weibull plotting positions, whereas the ML models were developed in Python using exceedance probability as the sole predictor to enable a direct and consistent comparison with statistical frequency models. Model performance was evaluated using the coefficient of determination (R