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.

Uatzau basin in northwestern Ethiopia is one of the most landslide-prone regions, which characterized by frequent high landslide occurrences causing damages in farmlands, non-cultivated lands, properties, and loss of life. Preparing a Landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. GIS-based frequency ratio, information value, and certainty factor methods were applied. The landslide inventory map was prepared from detailed fieldwork and Google Earth imagery interpretation. Thus, 514 landslides were mapped, and out of which 359 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models, while the remaining 155 (30%) of the landslides were used to model validation. In this study, six factors, including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were evaluated. The effects of the landslide factor of slope instability were determined by comparing with landslide inventory raster using the GIS environment. The landslide susceptibility maps of the Uatzau area were categorized into very low, low, moderate, high and very high susceptibility classes. The landslide susceptibility maps of the three models validated by the ROC curve. The results for the area under the curve (AUC) are 88.83% for the frequency ratio model, 87.03% for certainty factor, and 84.83% of information value models, which are indicating very good accuracy in the identification of landslide susceptibility zones of a region. From these resulted maps, it is possible to recommend, the statistical methods (Frequency Ratio, Information Value, and Certainty Factor Methods) are adequate to landslide susceptibility mapping. The landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes.

The study area in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent high landslide occurrences. To predict future landslide occurrence, preparing a landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. Geographic information system (GIS)-based frequency ratio (FR), information value (IV), certainty factor (CF), and logistic regression (LR) methods were applied. The landslide inventory map is prepared from historical records and Google Earth imagery interpretation. Thus, 717 landslides were mapped, of which 502 (70%) landslides were used to build landslide susceptibility models, and the remaining 215 (30%) landslides were used to model validation. Eleven factors such as lithology, land use/cover, distance to drainage, distance to lineament, normalized difference vegetation index, drainage density, rainfall, soil type, slope, aspect, and curvature were evaluated and their relationship with landslide occurrence was analyzed using the GIS tool. Then, landslide susceptibility maps of the study area are categorized into very low, low, moderate, high, and very high susceptibility classes. The four models were validated by the area under the curve (AUC) and landslide density. The results for the AUC are 93.9% for the CF model, which is better than 93.2% using IV, 92.7% using the FR model, and 87.9% using the LR model. Moreover, the statistical significance test between the models was performed using LR analysis by SPSS software. The result showed that the LR and CF models have higher statistical significance than the FR and IV methods. Although all statistical models indicated higher prediction accuracy, based on their statistical significance analysis result (Table 5), the LR model is relatively better followed by the CF model for regional land use planning, landslide hazard mitigation, and prevention purposes.

The flood is one of the frequently occurring natural hazards within the sub-basin of Lake Tana. The flood hazard within the sub-basin of Lake Tana causes damage to cropland, properties, and a fatality every season. Therefore, flood susceptibility modeling in this area is significant for hazard reduction and management purposes. Thus, the analytical hierarchy process (AHP), bivariate (information value [IV] and frequency ratio [FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field survey, historical document, and Google Earth Imagery, 1,404-flood locations were determined, classified into 70% training datasets and 30% testing flood datasets using a subset within the geographic information system (GIS) environment. The statistical relationship between the probability of flood occurrence and 11 flood-driving factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by summing all weighted aspects using a raster calculator. It is classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The accuracy and performance of the models were evaluated using the area under the curve (AUC). As the result indicated, the FR model has better performance (AUC = 99.1%) compared to the AHP model (AUC = 86.9%), LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This research finds out that the applied methods are quite worthy for flood susceptibility modeling within the study area. In flood susceptibility modeling, method selection is not a serious challenge; the care should tend to the input parameter quality. Based on the AUC values, the FR model is comparatively better, followed by the AHP model for regional land use planning, flood hazard mitigation, and prevention purposes.

No abstract