PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru
Version 1
: Received: 10 May 2024 / Approved: 10 May 2024 / Online: 10 May 2024 (14:15:59 CEST)
How to cite:
Badillo-Rivera, E. N.; Olcese Huerta, M. D.; Santiago Chirinos, R.; Poma Inche, T. I.; Muñoz Fernandez, N. E.; Rojas-León, C. A.; Chávez Campos, T. J.; Eyzaguirre Gorvenia, L. D. F.; Rodriguez Aburto, C. A.; Oyanguren Ramírez, F. J. A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru. Preprints2024, 2024050698. https://doi.org/10.20944/preprints202405.0698.v1
Badillo-Rivera, E. N.; Olcese Huerta, M. D.; Santiago Chirinos, R.; Poma Inche, T. I.; Muñoz Fernandez, N. E.; Rojas-León, C. A.; Chávez Campos, T. J.; Eyzaguirre Gorvenia, L. D. F.; Rodriguez Aburto, C. A.; Oyanguren Ramírez, F. J. A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru. Preprints 2024, 2024050698. https://doi.org/10.20944/preprints202405.0698.v1
Badillo-Rivera, E. N.; Olcese Huerta, M. D.; Santiago Chirinos, R.; Poma Inche, T. I.; Muñoz Fernandez, N. E.; Rojas-León, C. A.; Chávez Campos, T. J.; Eyzaguirre Gorvenia, L. D. F.; Rodriguez Aburto, C. A.; Oyanguren Ramírez, F. J. A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru. Preprints2024, 2024050698. https://doi.org/10.20944/preprints202405.0698.v1
APA Style
Badillo-Rivera, E. N., Olcese Huerta, M. D., Santiago Chirinos, R., Poma Inche, T. I., Muñoz Fernandez, N. E., Rojas-León, C. A., Chávez Campos, T. J., Eyzaguirre Gorvenia, L. D. F., Rodriguez Aburto, C. A., & Oyanguren Ramírez, F. J. (2024). A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru. Preprints. https://doi.org/10.20944/preprints202405.0698.v1
Chicago/Turabian Style
Badillo-Rivera, E. N., César Augusto Rodriguez Aburto and Fernando José Oyanguren Ramírez. 2024 "A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru" Preprints. https://doi.org/10.20944/preprints202405.0698.v1
Abstract
This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). Previous exploration of the topographic variables revealed high correlation and multicollinearity among some of them, which led to dimensionality reduction through principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being an El Niño phenomenon and the second being an earthquake exceeding 8.8Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of >0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8Mw, approximately 40% of the NLC area would be exposed to the highest hazard levels. It also highlights the importance of integrating methodologies to reduce model uncertainty, such as comprehensive analysis of variables supported by high quality data and organized workflows. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.
Keywords
mass movement, weight evidence, principal component analysis, machine learning.
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
