A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia
One of the most natural catastrophes in Malaysia, landslides, has resulted in several fatalities, infrastructure damage and economic losses. Over time, researchers have used various methods to forecast the vulnerability to landslides. Unfortunately, the most accurate algorithm which can be used to...
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my.unimas.ir.394982022-09-07T02:05:21Z http://ir.unimas.my/id/eprint/39498/ A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia Soo See, Chai Dorothy, Martin GE Environmental Sciences QA76 Computer software One of the most natural catastrophes in Malaysia, landslides, has resulted in several fatalities, infrastructure damage and economic losses. Over time, researchers have used various methods to forecast the vulnerability to landslides. Unfortunately, the most accurate algorithm which can be used to develop a landslide susceptibility model is still lacking. Therefore, the current study aims to evaluate how well Kota Kinabalu, Sabah's landslide susceptibility, can be predicted using three different machine learning techniques: K-Nearest Neighbor (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost). The research areas had 242 landslide locations, and the inventory data was arbitrarily separated into training and testing datasets in a 7/3 ratio. As prediction parameters, ten spatial databases of landslides conditioning factors were employed. The area under the curve (AUC) was utilized as the models’ performance metric. With an AUC score of 87.52 %, the final analysis showed that KNN had the highest prediction accuracy, followed by Random Forest (84.34 %) and XGBoost (78.07%). According to the AUC findings, KNN, Random Forest, and XGBoost performed consistently well in forecasting landslide susceptibility. The final forecast map can be a helpful tool for urban planning and development and for aiding the authorities in creating a strategic mitigation plan. 2022 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/39498/3/A%20Study%20on%20-%20Copy.pdf Soo See, Chai and Dorothy, Martin (2022) A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia. In: 13th Control and System Graduate Research Colloquium (ICSGRC), 23-23 July 2022, Shah Alam, Malaysia. https://ieeexplore.ieee.org/document/9845146 |
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GE Environmental Sciences QA76 Computer software Soo See, Chai Dorothy, Martin A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia |
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One of the most natural catastrophes in Malaysia,
landslides, has resulted in several fatalities, infrastructure damage and economic losses. Over time, researchers have used various methods to forecast the vulnerability to landslides. Unfortunately, the most accurate algorithm which can be used to develop a landslide susceptibility model is still lacking. Therefore, the current study aims to evaluate how well Kota Kinabalu, Sabah's landslide susceptibility, can be predicted using three different machine learning techniques: K-Nearest Neighbor (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost). The research areas had 242 landslide locations, and the inventory data was arbitrarily separated into training and testing datasets in a 7/3 ratio. As prediction parameters, ten spatial databases of landslides conditioning factors were employed. The area under the curve (AUC) was utilized as the models’ performance metric. With an
AUC score of 87.52 %, the final analysis showed that KNN had the highest prediction accuracy, followed by Random Forest (84.34 %) and XGBoost (78.07%). According to the AUC findings, KNN, Random Forest, and XGBoost performed consistently well in forecasting landslide susceptibility. The final forecast map can be a helpful tool for urban planning and development and for aiding the authorities in creating a strategic mitigation plan. |
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Proceeding |
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Soo See, Chai Dorothy, Martin |
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Soo See, Chai Dorothy, Martin |
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Soo See, Chai |
title |
A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia |
title_short |
A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia |
title_full |
A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia |
title_fullStr |
A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia |
title_full_unstemmed |
A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia |
title_sort |
study on performance comparisons between knn, random forest and xgboost in prediction of landslide susceptibility in kota kinabalu, malaysia |
publishDate |
2022 |
url |
http://ir.unimas.my/id/eprint/39498/3/A%20Study%20on%20-%20Copy.pdf http://ir.unimas.my/id/eprint/39498/ https://ieeexplore.ieee.org/document/9845146 |
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