Abstract
Several studies determine that one of the predominant factors for landslides are torrential rains. The present work proposes the use of data generated by weather stations to predict landslides in certain areas. These pluviosity was accumulated in certain periods of time looking for a persistence of weather conditions that impact in the landslides forecasting. We will use machine learning (ML) techniques such as Random Forest (RF) in two steps: first to determine the variables with greater predictive capacity and them using these variables to predict landslides. To validate the proposal, an area sensitive to these phenomena, monitored by several weather stations, was selected for the experimentation. Precipitation, temperature, wind, solar radiation and relative humidity data were obtained during14 years. We used time windows accumulation for the precipitation variable, Gain Ranking Filter was applied to the variables under study, obtaining that the 15-day accumulation precipitation is the most relevant. We obtained an accuracy of 99.992% using (RF) to predict landslides.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dou, J., et al.: Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci. Total Environ. 662, 332–346 (2019). https://doi.org/10.1016/j.scitotenv.2019.01.221
Lee, J.-H., Kim, H., Park, H.-J., Heo, J.-H.: Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution. Landslides 18(1), 321–338 (2020). https://doi.org/10.1007/s10346-020-01502-7
Maxwell, A.E., et al.: Slope failure prediction using random forest machine learning and LiDAR in an eroded folded mountain belt. Remote Sens. 12(3) (2020). https://doi.org/10.3390/rs12030486
Rodríguez, B.G., Meneses, J.S., Garcia-Rodriguez, J.: Implementation of a low-cost rain gauge with Arduino and Thingspeak. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds.) SOCO 2020. AISC, vol. 1268, pp. 770–779. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57802-2_74
Wu, B., Qiu, W., Jia, J., Liu, N.: Landslide susceptibility modeling using bagging-based positive-unlabeled learning. IEEE Geosci. Remote Sens. Lett. 1–5 (2020). https://doi.org/10.1109/lgrs.2020.2989497
Ray, A., Kumar, V., Kumar, A., Rai, R., Khandelwal, M., Singh, T.N.: Stability prediction of Himalayan residual soil slope using artificial neural network. Nat. Hazards 103(3), 3523–3540 (2020). https://doi.org/10.1007/s11069-020-04141-2
Cano, G., et al.: Automatic selection of molecular descriptors using random forest: application to drug discovery. Expert Syst. Appl. 72, 151–159 (2017). https://doi.org/10.1016/j.eswa.2016.12.008
Polit, E., Ch, Z.R.: Técnicas de Aprendizaje Automático Aplicadas al Procesamiento de Información Demográfica
Nguyen, C., Wang, Y., Nguyen, H.N.: Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. J. Biomed. Sci. Eng. 06(05), 551–560 (2013). https://doi.org/10.4236/jbise.2013.65070
Krkač, M., Bernat Gazibara, S., Arbanas, Ž, Sečanj, M., Mihalić Arbanas, S.: A comparative study of random forests and multiple linear regression in the prediction of landslide velocity. Landslides 17(11), 2515–2531 (2020). https://doi.org/10.1007/s10346-020-01476-6
Pham, B.T., et al.: A novel intelligence approach of a sequential minimal optimization-based support vector machine for landslide susceptibility mapping. Sustainability 11(22) (2019). https://doi.org/10.3390/su11226323
Sahin, E.K.: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl. Sci. 2(7), 1–17 (2020). https://doi.org/10.1007/s42452-020-3060-1
Harmouzi, H., Nefeslioglu, H.A., Rouai, M., Sezer, E.A., Dekayir, A., Gokceoglu, C.: Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN). Arab. J. Geosci. 12(22), 1–18 (2019). https://doi.org/10.1007/s12517-019-4892-0
Chen, T., Trinder, J.C., Niu, R.: Object-oriented landslide mapping using ZY-3 satellite imagery, random forest and mathematical morphology, for the Three-Gorges Reservoir, China. Remote Sens. 9(4) (2017). https://doi.org/10.3390/rs9040333
Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J.: Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci. Total Environ. 626, 1121–1135 (2018). https://doi.org/10.1016/j.scitotenv.2018.01.124
Acknowledgement
We would like to express our 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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rodríguez, B.G., Meneses, J.S., Garcia-Rodriguez, J. (2022). Improving Landslides Prediction: Meteorological Data Preprocessing Using Random Forest-Based Feature Selection. 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_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-87869-6_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87868-9
Online ISBN: 978-3-030-87869-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)