Preprints
https://doi.org/10.5194/nhess-2021-299
https://doi.org/10.5194/nhess-2021-299

  18 Nov 2021

18 Nov 2021

Review status: this preprint is currently under review for the journal NHESS.

Assessing the importance of feature selection in Landslide Susceptibility for Belluno province (Veneto Region, NE Italy)

Sansar Raj Meena1,2, Silvia Puliero1, Kushanav Bhuyan1,2, Mario Floris1, and Filippo Catani1 Sansar Raj Meena et al.
  • 1Department of Geosciences, University of Padova, Padova, Italy
  • 2Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, Netherlands

Abstract. In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas.

Sansar Raj Meena et al.

Status: open (until 30 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-299', Anonymous Referee #1, 01 Dec 2021 reply

Sansar Raj Meena et al.

Sansar Raj Meena et al.

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Short summary
The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms.
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