Defining High Risk Landslide Areas Using Machine Learning
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Byron Guerrero-Rodriguez [1] ; Jose Garcia-Rodriguez
;
Jaime Salvador
[1]
;
Christian Mejia-Escobar
[1]
;
Michelle Bonifaz
[1]
;
Oswaldo Gallardo
[1]
-
[1] Universidad Central del Ecuador
Universidad Central del Ecuador
Quito, Ecuador
-
[2] Universitat d'Alacant
Universitat d'Alacant
Alicante, España
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- Localización: Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II / José Manuel Ferrández Vicente (dir. congr.) , José Ramón Álvarez Sánchez (dir. congr.) , Félix de la Paz López (dir. congr.) , Hojjat Adeli (aut.), 2022, ISBN 978-3-031-06527-9, págs. 183-192
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Predicting landslides is a task of vital importance to prevent disasters, avoid human damage and reduce economic losses. Several research works have determined the suitability of Machine Learning techniques to address this problem. In the present study, we leverage a neural network model for landslide prediction developed in our previous work, in order to identify the specific areas where landslides are most likely to occur. We have created a dataset that collects an inventory of landslides and geological, geomorphological and meteorological conditioning factors of a region susceptible to this type of events. Among these variables, precipitation is widely recognized as a trigger of the phenomenon. In contrast to related works, we considered precipitation in a cumulative form with different time windows. The application of our model produces probability values which can be represented as multi-temporal landslide susceptibility maps. The distribution of the values in the different susceptibility classes is performed by means of equal intervals, quantile, and Jenks methods, whose comparison allowed us to select the most appropriate map for each cumulative precipitation. In this way, the areas of maximum risk are identified, as well as the specific locations with the highest probability of landslides. These products are valuable tools for risk management and prevention.
