Abstract
In recent years, several studies have been carried out to predict landslides by applying different methodologies and techniques. The present work proposes the use of data generated by meteorological stations to predict landslides in certain areas. These data are accumulated in certain periods of time looking for a persistence of the meteorological conditions and using machine learning (ML) techniques such as support vector machine (SVM). To validate the proposal, an area sensitive to these phenomena that is monitored by several weather stations was selected for the experimentation. Data on precipitation, temperature, wind, solar radiation and relative humidity were obtained for 36 years between 1979 and 2014, using a time windows for the predominant precipitation variable. A precision accuracy of 0.99 was obtained using the meteorological data to feed a SVM classifier.
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Acknowledgement
I would like to express my gratitude to the Central University of Ecuador and FIGEMPA, which in the framework of the inter-institutional agreement with the University of Alicante, made this research work possible.
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Rodríguez, B.G., Salvador-Meneses, J., Garcia-Rodriguez, J. (2022). Predicting Landslides with Machine Learning Methods Using Temporal Sequences of Meteorological Data. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_33
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