Development of intensity-duration-frequency curves using machine learning and satellite-derived precipitation data

Intensity-Duration-Frequency (IDF) curves describe the relationship between rainfall intensity, the duration of rainfall events, and the frequency with which these events occur at a specific location. IDF curves relate rainfall intensity, duration, and frequency using one of various statistical methods to support flood risk management and infrastructure design. However, these traditional statistical methods struggle to capture the growing variability and uncertainty in extreme precipitation. This study leverages satellite-based precipitation datasets and advanced machine learning techniques as an alternative to these statistical methods to develop more accurate and robust IDF curves, thereby lowering uncertainty and improving the reliability of construction under non-stationary rainfall trends. For this, daily precipitation data are collected from Global Precipitation Measurement (GPM) satellite observations over Beirut, Lebanon which are then subsequently disaggregated into multiple finer temporal resolutions. Maximum rainfall values are derived from these data points to develop machine learning and deep learning architectures, including Support Vector Regression (SVR), Artificial Neural Networks (ANN), novel Temporal Convolutional Networks (TCN), and a TCN variant enhanced with sparse self-attention mechanisms (TCAN), which learn distributions used to generate IDF curves. TCAN was able to explain almost all the variance, followed, respectively, by TCN, ANN, SVM then the Gumbel statistical method. The findings highlight how adaptive ML-based models can improve explained variance under variable precipitation patterns, delivering more reliable and robust IDF curves.