Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, nort
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Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia Azemeraw Wubalem1,2 · Matebie Meten1 Received: 20 August 2019 / Accepted: 17 March 2020 © Springer Nature Switzerland AG 2020
Abstract Goncha Siso Eneses area of East Gojam Zone in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent landslide occurrences causing fatalities and damages in cultivated and non-cultivated lands, infrastructure and properties. Hence, preparing a landslide susceptibility map is very helpful in reducing the damages in infrastructure and properties and loss of animal and human lives. In this study, GIS-based information value and logistic regression models were applied. A reliable and detailed landslide inventory with 894 landslides was prepared through detailed fieldwork and Google Earth image interpretation. These landslides were randomly divided into training data set for model development and testing data set for model validation. Nine landslide causative factors like slope, curvature, aspect, lithology, distance to stream, distance to lineament, distance to spring, rainfall and land use/cover were integrated with training landslides to determine the weight(s) of each landslide factor and factor classes using Information Value and Logistic Regression models, respectively. The landslide susceptibility index map was then produced by summing the weights of all the landslide factors using raster calculator of the spatial analyst tool in GIS. To evaluate the performance of the information value and logistic regression models for landslide susceptibility modeling, the relative landslide density index and area under the curve (AUC) of the receiver operating characteristic curves were performed on both the training and testing landslide data sets. The model has an AUC accuracy of 88.9% success rate and 85.9% prediction rate for information value model whereas 81.8% success rate and 80.2% predictive rate for logistic regression model. Keywords Landslide · GIS · Information value · Logistic regression · Landslide susceptibility
1 Introduction Natural hazards, particularly landslides, are affecting most parts of the world by causing damages in farmlands, engineering structures and loss of human lives [1–5]. These problems commonly occur in mountainous regions where the topography is rugged. These catastrophic natural hazards become an impediment to the development of both developed and developing countries [6]. Landslides in
Ethiopia have resulted in a loss of human and animal lives, damages in infrastructures and properties in the last 5 decades. From 1960 to 2010 alone, 388 people died, 24 people injured, a wide area of cultivated and non-cultivated land, environment, infrastructure, and houses were affected [7–10]. In 2018, rainfall triggered landslides also caused the death of 62 people, injury of 30 people, displacement of 5091 households, damage of houses and destruction of both cultivated and non-cultiv
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