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TXT-tool 4.385-1.1: Method for Prediction of Landslide Movements Based on Random Forests

  • Martin Krkač
  • Drago Špoljarić
  • Sanja Bernat Gazibara
  • Snježana Mihalić Arbanas
Chapter

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 daily basis, which would be an important source of information for decisions related to crisis management in the case of risky landslide movements.

Keywords

Landslide Movement prediction Random forests Monitoring Groundwater level Precipitation 

Notes

Acknowledgements

The results presented herein have been obtained with the financial support from JST/JICA’s SATREP Program. This support is gratefully acknowledged. The authors would also like to thank the Croatian Meteorological and Hydrological Service for the meteorological data. The authors are grateful to Ž. Arbanas and anonymous reviewers for their valuable advice.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Martin Krkač
    • 1
  • Drago Špoljarić
    • 2
  • Sanja Bernat Gazibara
    • 1
  • Snježana Mihalić Arbanas
    • 1
  1. 1.Faculty of Mining, Geology and Petroleum EngineeringUniversity of ZagrebZagrebCroatia
  2. 2.Intellomics LtdZagrebCroatia

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