Method for prediction of landslide movements based on random forests

Abstract

Prediction of landslide movements with practical application for landslide risk mitigation is a challenge for scientists. This study presents a methodology for prediction of landslide movements using random forests, a machine learning algorithm based on regression trees. The prediction method was established based on a time series consisting of 2 years of data on landslide movement, groundwater level, and precipitation gathered from the Kostanjek landslide monitoring system and nearby meteorological stations in Zagreb (Croatia). Because of complex relations between precipitations and groundwater levels, the process of landslide movement prediction is divided into two separate models: (1) model for prediction of groundwater levels from precipitation data and (2) model for prediction of landslide movements from groundwater level data. In a groundwater level prediction model, 75 parameters were used as predictors, calculated from precipitation and evapotranspiration data. In the landslide movement prediction model, 10 parameters calculated from groundwater level data were used as predictors. Model validation was performed through the prediction of groundwater levels and prediction of landslide movements for the periods from 10 to 90 days. The validation results show the capability of the model to predict the evolution of daily displacements, from predicted variations of groundwater levels, for the period up to 30 days. Practical contributions of the developed method include the possibility of automated predictions, updated and improved on a daily basis, which would be an important source of information for decisions related to crisis management in the case of risky landslide movements.

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Correspondence to Snježana Mihalić Arbanas.

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Krkač, M., Špoljarić, D., Bernat, S. et al. Method for prediction of landslide movements based on random forests. Landslides 14, 947–960 (2017). https://doi.org/10.1007/s10346-016-0761-z

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Keywords

  • Landslide
  • Movement prediction
  • Random forests
  • Monitoring
  • Groundwater level
  • Precipitation