Dharmayasa I.G.N.P.Putri P.I.D.Sugiana I.P.Jindal R.Surakit K.Thongdara R.Mahidol University2024-11-052024-11-052024-01-012024 10th International Conference on Smart Computing and Communication, ICSCC 2024 (2024) , 212-217https://repository.li.mahidol.ac.th/handle/20.500.14594/101878This study investigates areal rainfall estimation methods in the Ayung Watershed, Bali, a critical source of water for the region. Understanding areal rainfall patterns is crucial for flood risk assessment, water resource management, and infrastructure design. Three methods are evaluated: arithmetic average, Thiessen polygon, and Inverse Distance Weighting (IDW). Rainfall data from eight stations across the watershed (2004-2018) reveals a distinct wet-dry season pattern. All three methods yielded consistent results for areal rainfall estimation, with IDW showing a slight advantage in accuracy. However, the arithmetic average method offers a simpler and faster computational approach, making it suitable for initial analyses. Spatial interpolation methods like Thiessen polygon and IDW provide a more comprehensive picture of spatial rainfall distribution, crucial for detailed hydrological studies and disaster mitigation strategies. Integrating these methods with advancements like the Internet of Things (IoT) can further enhance areal rainfall estimation for improved water resource management.Computer ScienceConference PaperSCOPUS10.1109/ICSCC62041.2024.106905922-s2.0-85207506608