Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS

• Published in: Geosciences journal (Seoul, Korea) Vol. 19; no. 3; pp. 449 - 469
• Main Authors: Youssef, Ahmed Mohamed, Pradhan, Biswajeet, Pourghasemi, Hamid Reza, Abdullahi, Saleh
• Format: Journal Article
• Language: English
• Published: Seoul The Geological Society of Korea 01.09.2015; Springer Nature B.V; 한국지질과학협의회
• Subjects: Arabia; Buildings; Construction; Earth and Environmental Science; Earth Sciences; Geographic information systems; Landslides; Landslides & mudslides; Mathematical models; Remote sensing; Risk assessment; River basins; Satellite imagery; Training; 지질학; Saudi Arabia; GIS; Saudi Arabia; remote sensing; landslide; weights-of-evidence; frequency ratio
• ISSN: 1226-4806; 1598-7477
• DOI: 10.1007/s12303-014-0065-z

This paper presents an evaluation of two bivariate statistical approaches, frequency ratio (FR) and weights-of-evidence (WoE) for landslide susceptibility mapping (LSM) at south-western part of Saudi Arabia, Jizan region. Landslide locations were identified and mapped from interpretation of high resolution satellite images (GeoEye 0.5 m and QuikeBird 0.6m), topographic maps (scale of 1:10,000), historical records, and extensive field surveys. A total of 106 landslide locations were mapped using Arc-GIS software and divided into two groups, 75% and 25% of landslide locations were used for training and validation of models, respectively. Eleven landslide conditioning factors such as elevation, slope, curvature, aspect, lithology, topographic wetness index (TWI), normalized difference vegetation index (NDVI), proximity to lineament, roads and rivers were considered in this evaluation. The effects of these factors on landslide occurrence were assessed using aforementioned bivariate statistical approaches. For validation, the models results were compared with landslide locations which were not used during the models building. Subsequently, the receiver operating characteristic (ROC) curves were established and area under the curves (AUC) was calculated for the landslide susceptibility maps using the success (training data) and prediction (validation data) rates. The results showed that the area under the curve for success rates are 0.861 (86.1%) and 0.839 (83.9%) and for prediction rates are 0.796 (79.6%) and 0.791 (79.1%), respectively for frequency ratio and weight-of-evidence models. The resulting landslide susceptibility maps showed five classes of susceptibility such as very high, high, moderate, low, and very low. The percentage of existing training and validating landslides data in high and very high zones of the susceptibility maps were calculated to be 90.02% and 76.03% for frequency ratio model and 88.33% and 79.3% for weight-of-evidence model, respectively. The results revealed that the frequency ratio and weights-of-evidence models produced reasonable accuracy. The resultant maps would be useful and can also help planners for general choosing favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection.