The Effect of Morphometry Indices on Improving the Performance of Data Mining Models for Landslide Sensitivity Zoning in Cherikabad Watershed, Urmia

جغرافیا و مخاطرات محیطی

Article 3, Volume 10, Issue 4 - Serial Number 40, February 2022, Pages 47-68   

Document Type: Research Article

DOI: 10.22067/geoeh.2021.69707.1041

Authors

AbdulAziz Hanifinia^{* 1}; Habib Nazarnejad²

¹PhD Candidate in Watershed Management, Department of Range and Watershed Management, Urmia University, Urmia, Iran

²Associate Professor, Department of Range and Watershed Management, Urmia University, Urmia, Iran

Abstract

The purpose of this study was to evaluate the performance of two data mining models; artificial neural network and vector support machine algorithm in three modes: Using morphometric indices including topographic wetness index, topographic position index, stream power index, length slope index, terrain ruggedness index, mass balance index, profile curvature index and surface curvature index‌; Using environmental and human factors including rainfall, basin height, slope, slope direction, lithology, land use, normalized vegetation difference index, distance from stream, distance from road, and distance from fault; And a combination of the above two conditions in zoning the landslide sensitivity of the Cherikabad watershed in Urmia. For this purpose, 92 landslide points in the watershed were identified using field study and Google Earth images. The map of morphometric indices and maps of environmental and human factors were prepared and digitized in ArcGIS10.5. The evaluation results of the two models using the ROC curve showed that in the case of using only morphometric indices, the two models SVM and ANN with the area under the curve of 0.742 and 0.763, respectively, have good performance in landslide sensitivity zoning. In the case of using human and environmental factors, the above two models with an area under the curve of 0.876 and 0.929 have good and very good performance, respectively; and in the case of using both human and environmental factors along with morphometric indices, the two models with an area under the curve of 0.940 and 0.936 had almost the same performance with excellent rank in the zoning of sensitive areas. Moreover, the highest quality­ sum (Qs) and Density ratio (Dr) had the highest correlation between risk categories for the SVM model in the third case. The results of Kappa index in the superior state showed that lithology, LS, and basin height factors had the greatest effect on the occurrence of landslides, respectively. Therefore, the effects of natural factors in comparison with human factors, and in general, the morphometric indices are higher in the occurrence of landslides than environmental and human factors, and the basin is inherently sensitive to landslides.

Keywords

Morphometric Indices; SVM; ANN; ROC Curve; Cherikabad Watershed

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