Optimization of Early Warning System for Landslides Based on Rainfall Using Naive Bayes Classifier and Multiclass Support Vector Machine Algorithm in Takari Region

Sefri Imanuel Fallo

Sefri Imanuel Fallo Mathematics Study Program, San Pedro University, Kupang, Indonesia

Keywords: Landslide, Rainfall, Early Warning System, Naïve Bayes Classifier, Support Vector Machine

Abstract

This study explores non-structural disaster mitigation approaches employed by researchers, utilizing machine learning algorithms to analyze weather data and assess landslide vulnerability in the Takari Sub-district. Through field investigations and secondary data analysis, the research underscores the significance of rainfall intensity as a key factor in triggering landslides in the region. Additionally, soil types and slope gradients are identified as critical considerations in landslide vulnerability detection. Evaluation of a multiclass support vector algorithm for rainfall prediction reveals a notable accuracy rate of 57.97%, with predictions indicating instances of various rainfall intensities. Factors influencing these predictions include average temperature, humidity, wind speed, duration of sunshine, and wind direction. However, the study notes limitations in predictive accuracy due to the constrained availability of rainfall data. Consequently, the findings emphasize the need for preemptive measures, urging governmental authorities and local communities to prioritize structural disaster mitigation strategies to mitigate the heightened susceptibility to landslides in the Takari region

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