Application of Support Vector Machine and Kriging Interpolation for Rainfall Prediction in Java Island
-
Brian Dimas Purwanto
*
Telkom University,
Indonesia
- Sri Suryani Prasetiyowati Telkom University, Indonesia
- Yuliant Sibaroni Telkom University, Indonesia (*) Corresponding Author
Abstract
Rainfall is one of the crucial meteorological elements that can significantly impact human life. Accurate rainfall prediction is essential for effective natural resource planning and management across various regions, especially in Java Island, which is one of the most densely populated areas in Indonesia. This study aims to develop a rainfall distribution prediction model for Java Island using Support Vector Machine (SVM). The scenario developed involves time-based feature expansion implemented in SVM. This method is combined with Kriging interpolation to obtain the rainfall distribution classification on Java Island. The results show that the model's performance, exceeding 90%, is effective in predicting future rainfall distribution classifications on Java Island. The contribution of this research lies in providing insights into feature expansion techniques in machine learning to refine predictive models applied in meteorology and environmental management.
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Pages: 1071−1082
Copyright (c) 2024 Brian Dimas Purwanto, Sri Suryani Prasetiyowati, Yuliant Sibaroni
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