Evaluation of Random Forest Method in Landslide Susceptibility Mapping in Sadat Mahalleh Watershed of Sari

Document Type : Research

Authors

1 Professor, Department of Watershed Management Engineering, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

2 M.Sc. Student, Department of Management Engineering, Sari University of Agricultural Sciences and Natural Resources

3 Associate Professor, Department of Watershed Management Engineering, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

4 Assistant Professor, Soil Conservation and Watershed Management Department, Mazandaran Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Sari, Iran

Abstract

Landslides are one of the major types of slope instability which cause enormous financial losses and casualties; therefore, it is imperative to consider them in the study of geomorphology, erosion and sedimentation in important watershed. The aim of the present study was to investigate and rank the landslide susceptibility using a random forest method in the Sadat Mahalleh Watershed, Sari. Elevation from the sea level, geology, land use, slope (degree, direction, shape and length), distance from linear elements such as faults, streams and roads, normalized difference vegetation index and slope form were considered as effective parameter in the landslide occurrence. Zoning landslide sensitivity was achieved by coding in the R and GIS10.3 soft wares. In order to determine the weight of each of the factors and classes effective in zoning the landslide sensitivity and validation of landslide potential prediction maps, the abundance ratio method and the receiver operating characteristics (ROC) were used. The results indicated that among the effective factors, vegetative cover and land use, had the most paramount impact on the landslide occurrence in the study area. A landslide sensitivity map was prepared using the random forest method by dividing into five class, namely: very low (3.85%), low (5.38%), moderate (23.08%), high (50%) and very high (7.69%) sensitivities. Validation of the results of the landslide sensitivity zoning maps, using the relative factor characteristics index diagram, indicated that the area under the curve (AUC) for random forest method was 0.709 and its standard error was 0.10. It is reasonable to claim that the forest method provided an acceptable accuracy for mapping the landslide sensitivity. A landslide sensitivity map provides prone complete and accurate information for natural resource managers to detect the landslide areas.

Keywords

References

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  • Receive Date: 20 January 2020
  • Revise Date: 13 May 2020
  • Accept Date: 20 September 2020

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