[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124459-en":3,"doc-seo-124459-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124459,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Development of intensity-duration-frequency curves using machine learning and satellite-derived precipitation data","Intensity-Duration-Frequency (IDF) curves link rainfall intensity, event duration, and occurrence frequency for flood-risk management and infrastructure design. Traditional statistical approaches struggle to represent increasing variability and uncertainty in extreme precipitation under non-stationary rainfall trends. Using GPM satellite precipitation over Beirut, Lebanon, daily data are disaggregated to finer resolutions and maximum values are learned via SVR, ANN, TCN, and a sparse self-attention TCN variant (TCAN). TCAN captures nearly all variance, improving the explained variance and robustness of generated IDF curves.","OPEN ACCESS  \nEDITED BY  \nDagang Wang,  \nSun Yat-sen University, China  \nREVIEWED BY  \nJingyu Wang,  \nNanyang Technological University, Singapore Milan Stojkovic,  \nInstitute for Artificial Intelligence R&D Serbia, Serbia  \nAthanasios Serafeim,  \nUniversity of Peloponnese, Greece Ziaul Haq Doost,  \nKing Fahd University of Petroleum and Minerals, Saudi Arabia  \n*CORRESPONDENCE  \nCynthia Andraos  \n [cynthia.andraos2@usj.edu.lb](cynthia.andraos2@usj.edu.lb)  \nRECEIVED 17 October 2025  \nREVISED 30 December 2025  \nACCEPTED 02 January 2026  \nPUBLISHED 29 January 2026  \nCITATION  \nDargham E and Andraos C (2026)  \nDevelopment of intensity-duration-frequency curves using machine learning and satellite-derived precipitation data.  \nFront. Water 8:1727182 .  \ndoi: 10.3389/frwa.2026.1727182  \nCOPYRIGHT  \n© 2026 Dargham and Andraos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 29 January 2026 DOI 10.3389/frwa.2026.1727182  \nDevelopment of  \nintensity-duration-frequency curves using machine learning and satellite-derived precipitation data  \nElias Dargham and Cynthia Andraos *  \nRegional Center for Water and Environment, Faculty of Engineering, Saint Joseph University of Beirut, Beirut, Lebanon  \nIntensity-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 selfattention 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.  \nKEYWORDS  \nextreme precipitation, intensity-duration-frequency, machine learning, satellite-based rainfall, uncertainty  \n1 Introduction  \nRainfall intensity-duration-frequency (IDF) curves form a fundamental procedure in hydrological engineering for analyzing precipitation patterns and designing water infrastructure (Collalti et al., 2024; Sherman, 1931) . This process establishes critical relationships between the intensity of rainfall events, their duration, and frequency of occurrence, serving as the central component for modern water resource management and infrastructure design (Soto-Escobar et al., 2025). IDF curves have evolved from early empirica","cbCaitkYsyDnL1dq","https://ap.wps.com/l/cbCaitkYsyDnL1dq","pdf",1784221,1,14,"English","en",105,"# Introduction\n## IDF curves in hydrological engineering\n## Limitations of traditional approaches and data scarcity\n# Methodology and modeling approach\n## Satellite precipitation data and temporal disaggregation\n## Model architectures for learning IDF relationships\n# Results and findings\n## Variance explanation across ML and statistical methods\n## Robustness under variable precipitation patterns","[{\"question\":\"What do intensity-duration-frequency (IDF) curves represent in this study?\",\"answer\":\"IDF curves describe the relationship between rainfall intensity, rainfall duration, and the frequency of occurrence at a given location to support flood-risk management and infrastructure design.\"},{\"question\":\"How is satellite precipitation used to build the IDF curves?\",\"answer\":\"Daily precipitation from Global Precipitation Measurement (GPM) satellite observations over Beirut is disaggregated to finer temporal resolutions, and maximum rainfall values are extracted to train models that generate IDF curves.\"},{\"question\":\"Which machine learning model performed best and what was the outcome?\",\"answer\":\"The TCAN model with sparse self-attention explained almost all the variance, outperforming TCN, ANN, SVM, and the Gumbel statistical method, leading to more reliable and robust IDF curves.\"}]","Development of intensity-duration-frequency curves using machine learning and satellite-derived precipitation data | 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do intensity-duration-frequency (IDF) curves represent in this study?","Question",{"text":75,"@type":76},"IDF curves describe the relationship between rainfall intensity, rainfall duration, and the frequency of occurrence at a given location to support flood-risk management and infrastructure design.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is satellite precipitation used to build the IDF curves?",{"text":80,"@type":76},"Daily precipitation from Global Precipitation Measurement (GPM) satellite observations over Beirut is disaggregated to finer temporal resolutions, and maximum rainfall values are extracted to train models that generate IDF curves.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what was the outcome?",{"text":84,"@type":76},"The TCAN model with sparse self-attention explained almost all the variance, outperforming TCN, ANN, SVM, and the Gumbel statistical method, leading to more reliable and robust IDF 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