Comparing Machine Learning Methods for Early Warning of Floods and Landslides in Thailand

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Joanna Sophie Abraham Supatta Labaiusuh Ekkasit Ismael Chatree Nilnumpetch

Abstract

        Flood disasters and landslides have a strong impact on people's lives, property, and the economy of the country. Heavy rainfall is the primary cause of these disasters. Therefore, prediction warnings is necessary for people to help them prepare for the disaster in time. This paper outlines the process used to identify appropriate models for prediction warnings for floods and landslides by comparing the recall performance of eight different models. The models were Rule-Based, K-nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, and Multilayer Perceptron. The process involved five phases: data collection, data pre-processing, building a model, 5-fold cross-validation, and model evaluation. This study utilized a rainfall-related dataset collected by the Department of Water Resources in Thailand for training and testing the models. After the process was applied along with a detailed evaluation, it found that when 5-fold cross-validation was applied, better performance was achieved with Random Forest having the highest recall value at 74%, followed by Decision Tree, Multilayer Perceptron and Support Vector Machine. From these results, it can be concluded that the Random Forest model is suitable for predicting warnings and can be implemented in future works for developing an early warning application to reduce the aftermath of these disasters.


Keywords: Machine Learning, Random Forest, Decision Tree, Early Warning; Landslides

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Section
Research Articles

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How to Cite
ABRAHAM, Joanna Sophie et al. Comparing Machine Learning Methods for Early Warning of Floods and Landslides in Thailand. Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 31, n. 4, p. 77-92, nov. 2023. ISSN 2539-553X. Available at: <https://www.journal.nu.ac.th/NUJST/article/view/3609>. Date accessed: 24 feb. 2024. doi: https://doi.org/10.14456/nujst.2023.37.