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Landslide impacting factors and susceptibility assessment in part of the Purvanchal Himalayas using data mining approaches

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Abstract

This research was to assess the terrain factors responsible for landslide and generation of landslide susceptibility map (LSM) in the Mao-Maram of Manipur, India. Using multi-source spatial data, twelve causal factors were identified slope, relative relief, road buffer, fault/fold and thrust (FFT) buffer, aspect, stream power index (SPI), drainage buffer, profile curvature, land used/land cover (LULC), soil, and geology. In addition, using fieldwork and remote sensing technique, 234 landslide incident was recorded for training and testing the model of the study region. Three data mining models used are support vector machine, random forest, and boosted tree which was used to generate a landslide susceptibility map. The ensemble technique of these three data mining models has been used to generate a separated landslide susceptibility map by normalizing landslide susceptibility index maps as well as analyzing image data using the GIS platform. Five relative susceptibility classes (very high, high, medium, low, and very low) were created using natural break (Jenks) for the study region. Prediction accuracy for four landslide susceptibility maps was done with a receiver operating characteristic (ROC) curve area under the curve. The prediction accuracy was obtained for boosted tree (90%), support vector machine (90.6%), random forest (91.5%), and ensemble technique (92.5%). Interestingly, from the accuracy result, it is found that the ensemble technique has obtained the highest accuracy. LSM created from each of the four models can be useful for development, management, disaster prevention, and planning.

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Data availability

The corresponding author may provide all of the datasets that were used and analyzed during this study upon reasonable request.

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Acknowledgements

The authors would like to thank the Lovely Professional University in Punjab for providing laboratory and software resources to carry out this work. The first author expressed gratitude to A.Elow, senior geologist (Geological Survey of India), for his assistance in gathering auxiliary data during the investigation. Additionally, the writers thank the Mao Council for their kindness and goodwill during the field study.

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  1. Lovely Professional University, Phagwara, Punjab, India

    Kaikho Khusulio & Rohan Kumar

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  1. Kaikho Khusulio
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The first author has visited the field and carried out the research, collecting data, creating maps, and preparing a manuscript, while the second author has guided the first author throughout the research.

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Correspondence to Kaikho Khusulio.

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Khusulio, K., Kumar, R. Landslide impacting factors and susceptibility assessment in part of the Purvanchal Himalayas using data mining approaches. Arab J Geosci 16, 612 (2023). https://doi.org/10.1007/s12517-023-11719-0

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