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Concepts for Improving Machine Learning Based Landslide Assessment

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Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques

Part of the book series: Advances in Natural and Technological Hazards Research ((NTHR,volume 48))

Abstract

The main idea of this chapter is to address some of the key issues that were recognized in Machine Learning (ML) based Landslide Assessment Modeling (LAM). Through the experience of the authors, elaborated in several case studies, including the City of Belgrade in Serbia, the City of Tuzla in Bosnia and Herzegovina, Ljubovija Municipality in Serbia, and Halenkovice area in Czech Republic, eight key issues were identified, and appropriate options, solutions, and some new concepts for overcoming them were introduced. The following issues were addressed: Landslide inventory enhancements (overcoming small number of landslide instances), Choice of attributes (which attributes are appropriate and pros and cons on attribute selection/extraction), Classification versus regression (which type of task is more appropriate in particular cases), Choice of ML technique (discussion of most popular ML techniques), Sampling strategy (overcoming the overfit by choosing training instances wisely), Cross-scaling (a new concept for improving the algorithm’s learning capacity), Quasi-hazard concept (introducing artificial temporal base for upgrading from susceptibility to hazard assessment), and Objective model evaluation (the best practice for validating resulting models against the existing inventory). All of them are followed by appropriate practical examples from one of abovementioned case studies. The ultimate objective is to provide guidance and inspire LAM community for a more innovative approach in modeling.

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Acknowledgements

This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No TR 36009.

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Authors and Affiliations

  1. Faculty of Mining and Geology, University of Belgrade, Belgrade, Serbia

    Miloš Marjanović & Biljana Abolmasov

  2. Faculty of Civil Engineering, University of Belgrade, Belgrade, Serbia

    Mileva Samardžić-Petrović & Uroš Đurić

Authors
  1. Miloš Marjanović
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  2. Mileva Samardžić-Petrović
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  3. Biljana Abolmasov
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  4. Uroš Đurić
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Corresponding author

Correspondence to Miloš Marjanović .

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Editors and Affiliations

  1. Natural Resources and Environmental Engineering, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  2. IRPI, National Research Council, Perugia, Italy

    Mauro Rossi

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Marjanović, M., Samardžić-Petrović, M., Abolmasov, B., Đurić, U. (2019). Concepts for Improving Machine Learning Based Landslide Assessment. In: Pourghasemi, H., Rossi, M. (eds) Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques. Advances in Natural and Technological Hazards Research, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-73383-8_2

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