Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia

Database construction for landslide factors (slope, aspect, profile curvature, plan curvature, lithology, land use, distance from lineament & distance from river) and landslide inventory map is an important step in landslide susceptibility modelling. Using the frequency ratio model, the weights for each factor classes were calculated and assigned in GIS so as to add these factors and produce landslide susceptibility index maps based on mathematical combination theory. However, before combining them, their independence among each other should be ascertained. For this, the correlation matrix of logistic regression was applied and this showed that most of the correlations between factors were either absent or very insignificant suggesting that all landslide factors are independent. From a set of eight landslide factors, a total of 247 landslide susceptibility map combinations can be generated. However, for simplification, only 28 landslide susceptibility maps were chosen. Then the best landslide susceptibility map was selected based on high prediction accuracy. But, when there is similarity in the prediction accuracies of different combinations, the landslide susceptibility index difference values can be used as another selection criterion. Hence, the susceptibility map from a combination of all landslide factors except distance from river was found to be the best one. Among the 28 representative combinations, landslide susceptibility maps with the same prediction accuracy of 87.7% have been found in spite of their dissimilarity in their difference values. The combination, with a limited number of landslide factors and the highest prediction accuracy of 87.7%, was found from a combination of slope, lithology, land use and distance from lineament. In order to validate the prediction model, landslides were overlaid over the landslide susceptibility map and the number of landslides that fall into each susceptibility class was calculated. From this analysis 0.39%, 1.84%, 9.1%, 32.04% and 56.63% of the landslides fall in the very low, low, medium, high and very high landslide susceptibility classes respectively. Since 88.67% of the landslides fall in the high and very high susceptibility classes, the landslide susceptibility map can be considered reliable to predict future landslides.

[1]  M. Terlien.  The determination of statistical and deterministic hydrological landslide-triggering thresholds , 1998 .

[2]  Minoru Yamanaka,et al.  Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence , 2008 .

[3]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

[4]  W. Dietrich,et al.  The importance of hollows in debris flow studies; Examples from Marin County, California , 1987 .

[5]  T. Korme,et al.  Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet Area, Ethiopia , 2001 .

[6]  M. Ruff,et al.  Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria) , 2008 .

[7]  W. Z. Savage,et al.  Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning , 2008 .

[8]  G. Ohlmacher.  Plan curvature and landslide probability in regions dominated by earth flows and earth slides , 2007 .

[9]  M. Kirkby,et al.  Hillslope Form and Process , 1972 .

[10]  E. Mark Lee,et al.  Landslide Risk Assessment , 2023 .

[11]  Manfred F. Buchroithner,et al.  Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model , 2011 .

[12]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[13]  B. Pradhan.  Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling , 2011, Environmental and Ecological Statistics.

[14]  Theo van Asch,et al.  Deterministic Modelling in Gis-Based Landslide Hazard Assessment , 1995 .

[15]  Chang-Jo Chung,et al.  Fuzzy Logic Integration for Landslide Hazard Mapping Using Spatial Data from Boeun, Korea , 2022 .

[16]  Andrea G. Fabbri,et al.  Validation of Spatial Prediction Models for Landslide Hazard Mapping , 2003 .

[17]  R. Soeters,et al.  Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment , 2003 .

[18]  A. Clerici,et al.  A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: the Baganza valley case study (Italian Northern Apennines) , 2006 .

[19]  Saied Pirasteh,et al.  Geo‐information technology (GiT) and tectonic signatures: the River Karun & Dez, Zagros Orogen in south‐west Iran , 2009 .

[20]  B. Pradhan.  Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches , 2010 .

[21]  Saro Lee,et al.  Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea , 2007 .

[22]  C. Gokceoğlu,et al.  Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach , 2002 .

[23]  B. Pradhan,et al.  Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models , 2007 .

[24]  Hiromitsu Yamagishi,et al.  Slope failures in the Blue Nile basin, as seen from landscape evolution perspective , 2004 .

[25]  Rex L. Baum,et al.  Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework , 2008 .

[26]  J. T. Hack,et al.  Geomorphology and forest ecology of a mountain region in the central Appalachians , 1960 .

[27]  Netra R. Regmi,et al.  Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA , 2010 .

[28]  Saro Lee,et al.  The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea , 2004 .

[29]  L. Ayalew,et al.  The effect of seasonal rainfall on landslides in the highlands of Ethiopia , 1999 .

[30]  John E. Costa,et al.  Debris Flows/Avalanches: Process, Recognition, and Mitigation , 1987 .

[31]  Biswajeet Pradhan,et al.  A recent scenario of mass wasting and its impact on the transportation in Alborz Mountains, Iran using geo-information technology , 2011 .