GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia

Abstract

Simada area is found in the South Gondar Zone of Amhara National Regional State and it is 780Km far from Addis Ababa. Physiographically, it is part of the northwestern highlands of Ethiopia. This area is part of the Guna Mountain which is characterized by weathered volcanic rocks, rugged morphology with deeply incised gorges, heavy rainfall and active surface processes. Many landslides have occurred on August 2018 after a period of heavy rainfall and they caused many damages to the local people. In this study, Frequency Ratio (FR) and Weights of Evidence (WoE) models were applied to evaluate the landslide causative factors and generate landslide susceptibility maps (LSMs). The landslide inventory map that consists of 576 active and passive landslide scarps was prepared from intensive fieldwork and Google Earth image interpretation. These landslide locations were randomly divided into 80% training and 20% validation datasets. Seven landslide causal factors including aspect, slope, curvature, lithology, land use, rainfall and distance to stream were combined with a training dataset using GIS tools to generate the LSMs of the study area. Then the area was divided into five landslide susceptibility zones of very low, low, moderate, high and very high. Later, the resulting maps have been validated by using area under the curve and landslide density index methods. The result showed that the predictive rate of FR and WoE models were 88.2% and 84.8%, respectively. This indicated that the LSM produced by FR model showed a better performance than that of WoE model. Finally, the LSMs produced by FR and WoE models can be used by decision-makers for land use planning and landslide mitigation purpose.

Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

“…Curvature has been mostly employed in landslide research related to water flow (Mersha and Meten 2020;Ghasemian et al 2020;Thanh et al 2020). Curvature affects landslides because curvature can control the flow of water on the ground.…”

Landslide Disaster Potential Mapping in Feasibility Study of Walini Areaas the Candidate for the Capital City of West Java

“…LULC classes are based on Glade (2003) classification, then it is divided into 5 classes. Proximity to Stream and Proximity to Road are classified into five classes: 0 -50, 50 -100, 100 -150, 150 -200 and > 200 meter for stream (Mersha & Meten, 2020);and 0-50, 50-100,100-200, 200-500, >500 for road (Arsyad & Hamid, 2019). After that, we use the WLC method using these 4 parameters to make a Landslide Vulnerability Assessment Map (Figure 7.).…”

Landslide Susceptibility Zonation Using Geospatial Technologies and Multi Criteria Evaluation Techniques in Upper Didessa Sub-basin, Southwest Ethiopia

“…Landslide frequency is mostly found at mountainous and hilly regions (Sharma et al, 2012), where forests have been severely destroyed due to the deforestation (Mersha and Meten, 2020). About 57% of the basin is dominated by low to high density of vegetation.…”