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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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  08 Jul 2020

08 Jul 2020

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

Assessment of Landslide Susceptibility using Weight of Evidence and Frequency Ratio Model in Shahpur Valley, Eastern Hindu Kush

Ghani Rahman1, Atta Ur Rahman2, Alam Sher Bacha3, Shakeel Mahmood4, Muhammad Farhan Ul Moazzam5, and Byung Gul Lee5 Ghani Rahman et al.
  • 1Department of Geography, University of Gujrat, Pakistan
  • 2Department of Geography, University of Peshawar, Pakistan
  • 3National Center of Excellence in Geology, University of Peshawar, Pakistan
  • 4Government College University, Lahore, Pakistan
  • 5Department of Civil Engineering, College of Ocean Sciences, Jeju National University, South Korea

Abstract. This study assessing the landslide susceptibility using Weight of Evidence (WoE) and Frequency Ratio (FR) model in Shahpur valley, situated in the eastern Hindu Kush. Here, landslide is a recurrent phenomenon that disrupts natural environment and cause huge property damages as well as incurs human losses every year. These damages are expected to increase due to high rate of deforestation in the region, population growth, agricultural expansion and infrastructural development on the fragile slopes. Initially, landslide inventory map was prepared from SPOT5 satellite image and were verified from frequent visits in the field. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, proximity to roads and stream were selected. To analyze the relationship of landslide occurrence with these causative factors, WoE and FR models were used. Based on WoE and FR model landslide susceptibility zonation maps were prepared and were reclassified into very low to very high landslide susceptible zones. Finally, the resultant maps of landslide susceptibility were authenticated using success rate curve and prediction rate curve approach to validate the models.

Ghani Rahman et al.

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Ghani Rahman et al.

Ghani Rahman et al.


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