Zanya Reubenne D. Omadlao
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Nica Magdalena A. Tuguinay
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Ricarido Maglaqui Saturay
A machine learning-based prediction system for rainfall-induced landslides in Benguet First Engineering District is proposed to address the landslide risk due to the climate and topography of Benguet province. It is intended to improve the decision support system for road management with regards to landslides, as implemented by the Department of Public Works and Highways Benguet First District Engineering Office. Supervised classification was applied to daily rainfall and landslide data for the Benguet First Engineering District covering the years 2014 to 2018 using scikit-learn. Various forms of cumulative rainfall values were used to predict landslide occurrence for a given day. Following typical machine learning workflows, rainfall-landslide data set was divided into training and testing data sets. Machine learning algorithms such as K-Nearest Neighbors, Gaussian Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and AdaBoost were trained using the training data sets, and the trained models were used to make predictions based on the testing data sets. Predictive performance of the models vis-a-vis the testing data sets were compared using true positive rates, false positive rates, and the area under the Receiver Operating Characteristic Curve. Predictive performance of these models were then compared to 1-day cumulative rainfall thresholds commonly used for landslide predictions. Among the machine learning models evaluated, Gaussian Naïve Bayes has the best performance, with mean false positive rate, true positive rate and area under the curve scores of 7%, 76%, and 84% respectively. It also performs better than the 1-day cumulative rainfall thresholds. This research demonstrates the potential of machine learning for identifying temporal patterns in rainfall-induced landslides using minimal data input -- daily rainfall from a single synoptic station, and highway maintenance records. Such an approach may be tested and applied to similar problems in the field of disaster risk reduction and management.
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