Version 1
: Received: 5 September 2024 / Approved: 6 September 2024 / Online: 6 September 2024 (13:04:28 CEST)
How to cite:
Arcia Castro, V.; Corzo, G. Machine Learning Analysis of Precipitation as Trigger for Shallow Landslides: Ometepe Island, Nicaragua. Preprints2024, 2024090526. https://doi.org/10.20944/preprints202409.0526.v1
Arcia Castro, V.; Corzo, G. Machine Learning Analysis of Precipitation as Trigger for Shallow Landslides: Ometepe Island, Nicaragua. Preprints 2024, 2024090526. https://doi.org/10.20944/preprints202409.0526.v1
Arcia Castro, V.; Corzo, G. Machine Learning Analysis of Precipitation as Trigger for Shallow Landslides: Ometepe Island, Nicaragua. Preprints2024, 2024090526. https://doi.org/10.20944/preprints202409.0526.v1
APA Style
Arcia Castro, V., & Corzo, G. (2024). Machine Learning Analysis of Precipitation as Trigger for Shallow Landslides: Ometepe Island, Nicaragua. Preprints. https://doi.org/10.20944/preprints202409.0526.v1
Chicago/Turabian Style
Arcia Castro, V. and Gerald Corzo. 2024 "Machine Learning Analysis of Precipitation as Trigger for Shallow Landslides: Ometepe Island, Nicaragua" Preprints. https://doi.org/10.20944/preprints202409.0526.v1
Abstract
In this study, we address the need for improved landslide forecasting by exploring innovative machine learning approaches to classify shallow landslide events. Focusing on Ometepe Island, Nicaragua, known for its susceptibility to hydrometeorological incidents and seismic activities, we constructed a comprehensive shallow landslide database using coherence layers derived from SAR images. We applied coherence thresholds ranging from 0.9 to 0.99, in conjunction with 7-day aggregated spatio-temporal precipitation data. By employing machine learning models such as Logistic Regression, Random Forest, and Support Vector Classifier, we established a correlation between precipitation patterns and landslide occurrences. The Support Vector Classifier with a Sigmoidal kernel proved to be the most effective, achieving an F1 score of 0.67. Our results demonstrate a linkage between precipitation and landslide events, confirming the critical role of rainfall over 7-day periods in triggering these events. The study also indicates that, while rainfall is a crucial factor, it is not the sole trigger for landslides, as seismic activities also play a significant role. This research contributes valuable insights into landslide prediction and risk assessment, underscoring the potential of machine learning in enhancing environmental hazard analysis.
Keywords
data-driven modelling; machine learning; landslide; remote sensing; Logistic Regression; Random Forest; Support Vector Classifier; Ometepe; Nicaragua
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.
