A comprehensive analysis of landslide susceptibility in Iyidere Basin (NE, Turkey) using machine learning techniques and statistical bivariate methods

Kemal Ersayin () and Ali Uzun
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Kemal Ersayin: Tokat Gaziosmanpasa University
Ali Uzun: Ondokuz Mayıs University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 12, No 16, 14283-14319

Abstract: Abstract Natural events are called disasters when they cause great damage, human suffering, or loss of life. Landslides, one of these disasters, cause significant damage to property and infrastructure and pose risks to people's lives. In this research, landslide susceptibility was studied in Iyidere Basin, located in northeastern Turkey. This basin, which is among the cities where the most landslide events occur in Turkey, is a very important representative area in terms of a comprehensive analysis of landslides in the region. Bivariate (frequency ratio, weight of evidence, statistical index) and machine learning methods (artificial neural network, logistic regression) were used to evaluate landslide susceptibility with fifteen environmental parameters and 588 landslide inventory data. Landslide inventory data was generated using different sources, and environmental parameters databases were created using various sources and software. A receiver operating characteristic curve and Kappa statistic value were generated to test the performance and reliability of the susceptibility maps. It was determined that landslide susceptibility is higher in the downstream part of the basin. Although it varies between methods, it has been determined that approximately one-quarter of the basin has high and very high landslide susceptibility. The most effective parameters (drainage density, slope, curvature, lithology, land cover, distance to stream, and roads) for susceptibility and their classes were revealed.

Keywords: Landslide susceptibility; Machine learning; Bivariate statistical methods; Rize; İyidere (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07354-5

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