A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning

Natural Hazards, vol. 120, pp. 8953–8982

Abstract

Abstract Machine learning has grown in popularity in the past few years for susceptibility and hazard mapping tasks. Necessary steps for the generation of a susceptibility or hazard map are repeatedly implemented in new studies. We present a Random Forest classifier-based landslide susceptibility and hazard mapping framework to facilitate future mapping studies using machine learning. The framework, as a piece of software, follows the FAIR paradigm, and hence is set up as a transparent, reproducible and modularly extensible workflow. It contains pre-implemented steps from conceptualisation to map generation, such as the generation of input datasets. The framework can be applied to different areas of interest using different environmental features and is also flexible in terms of the desired scale and resolution of the final map. To demonstrate the functionality and validity of the framework, and to explore the challenges and limitations of Random Forest-based susceptibility and hazard mapping, we apply the framework to a test case. This test case conveys the influence of the training dataset on the generated susceptibility maps in terms of feature combination, influence of non-landslide instances and representativeness of the training data with respect to the area of interest. A comparison of the test case results with the literature shows that the framework works reliably. Furthermore, the results obtained in this study complement the findings of previous studies that demonstrate the sensitivity of the training process to the training data, particularly in terms of its representativeness.

Authors 6

1. Ann-Kathrin Edrich corresponding Aachen Methods for Model–based Development in Computational Engineering

   RWTH Aachen University

   Affiliation as printed

   Methods for Model-based Development in Computational Engineering, RWTH Aachen University, Aachen, Germany

2. Anil Yıldiz Aachen Methods for Model–based Development in Computational Engineering

   RWTH Aachen University

   Affiliation as printed

   Methods for Model-based Development in Computational Engineering, RWTH Aachen University, Aachen, Germany

3. Ribana Roscher

   University of Bonn · Forschungszentrum Jülich

   Affiliation as printed

   Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany

   Institute of Geodesy and Geoinformation, Remote Sensing Group, University of Bonn, Bonn, Germany

4. Alexander Bast

   WSL Institute for Snow and Avalanche Research SLF

   Affiliation as printed

   Alpine Environment and Natural Hazards/Permafrost, WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland

   Climate Change, Extremes and Natural Hazards in Alpine Regions Research Centre CERC, WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland

5. Frank Graf

   WSL Institute for Snow and Avalanche Research SLF

   Affiliation as printed

   Alpine Environment and Natural Hazards/Mountain Ecosystems, WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland

   Climate Change, Extremes and Natural Hazards in Alpine Regions Research Centre CERC, WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland

6. Julia Kowalski Aachen Methods for Model–based Development in Computational Engineering

   RWTH Aachen University

   Affiliation as printed

   Methods for Model-based Development in Computational Engineering, RWTH Aachen University, Aachen, Germany