Benchmarking Machine Learning Models for Landslide Susceptibility: A Study in the Ngororero Sector
Keywords:
Landslide prediction, Machine learning algorithms, Feature selection, RwandaAbstract
This study evaluates the performance of six machine learning algorithms: Decision Trees (DT), Neural Networks (NN), Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting Machines (GBM), and k-nearest Neighbors (k-NN) for landslide prediction in the Ngororero sector, Rwanda. Using Sentinel-2 satellite imagery, meteorological data, and topographical datasets from 2015, 2019, and 2023, the study incorporates critical features such as slope, rainfall, soil type, and vegetation cover. The findings indicate significant temporal and algorithmic variations in prediction performance. K-Nearest Neighbors and Random Forest consistently achieved high accuracies, with kNN showing a value of 84% in the 2019 dataset and more than 80% in other datasets. Random Forest demonstrated robust performance with a 78.98% accuracy in 2015. The research concludes that k-nearest Neighbors and Random Forest are optimal for predicting landslides in the Ngororero sector.
References
Ayalew, L., Yamagishi, H., Marui, H., & Kanno, T. (2005). Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology, 81(4), 432–445. https://doi.org/10.1016/J.ENGGEO.2005.08.004
BİZİMANA, H., & SÖNMEZ, O. (2015). Landslide Occurrences in The Hilly Areas of Rwanda, Their Causes and Protection Measures. Disaster Science and Engineering, 1(1), 1–7. https://dergipark.org.tr/en/pub/dse/issue/34556/381756
Claude, M. J., Martin, N. V., Abias, M., Francoise, M., Johnson, U., Tonny, K., & Martine, U. (2020). Mapping Landslide Susceptibility and Analyzing Its Impact on Community Livelihoods in Gakenke District, Northern Rwanda. Journal of Geoscience and Environment Protection, 08(05), 41–55. https://doi.org/10.4236/gep.2020.85003
Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1995). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 13(6), 1995.
Henninger, S. M. (2013). Does the global warming modify the local Rwandan climate? Natural Science, 05(01), 124–129. https://doi.org/10.4236/ns.2013.51a019
Hong, H., Pourghasemi, H. R., & Pourtaghi, Z. S. (2016). Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, 259, 105–118. https://doi.org/10.1016/J.GEOMORPH.2016.02.012
Kuradusenge, M., Kumaran, S., & Zennaro, M. (2020). Rainfall-induced landslide prediction using machine learning models: The case of ngororero district, rwanda. International Journal of Environmental Research and Public Health, 17(11), 1–20. https://doi.org/10.3390/ijerph17114147
Montello, F., & Rossi, C. (2024). LANDSLIDE MAPPING FROM SENTINEL-2 IMAGERY THROUGH CHANGE DETECTION.
Nahayo, L., Li, L., & Mupenzi, C. (2018). Spatial Distribution of Landslides Vulnerability for the Risk Management in Ngororero District of Rwanda. Eajst, 8(2), 1–14.
NISR. (2022). Fifth Population and Housing Census - 2022. National Institute of Statistics of Rwanda.
Nwazelibe, V. E., Egbueri, J. C., Unigwe, C. O., Agbasi, J. C., Ayejoto, D. A., & Abba, S. I. (2023). GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach. Environmental Earth Sciences, 82(19), 1–23. https://doi.org/10.1007/s12665-023-11134-4
Pourghasemi, H. R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA, 162, 177–192. https://doi.org/10.1016/J.CATENA.2017.11.022
Sumbiri, D., Afullo, T. J. O., & Alonge, A. (2016). Rain attenuation prediction for terrestrial links at microwave and millimeter bands over Rwanda. Progress in Electromagnetic Research Symposium (PIERS), 4233–4236. https://doi.org/10.1109/PIERS.2016.7735592
Uwihirwe, J., Hrachowitz, M., & Bogaard, T. A. (2020). Landslide precipitation thresholds in Rwanda. Landslides, 17(10), 2469–2481. https://doi.org/10.1007/s10346-020-01457-9
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining. In Data Mining. https://doi.org/10.1016/C2009-0-19715-5
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