Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

Abstract

The Mugling-Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.

Tokat-Ni̇ksar Karayolundaki̇ Kazaya Meyi̇lli̇ Kesi̇mleri̇n Frekans Orani Yöntemi̇yle Tespi̇t Edi̇lmesi̇

“…FOY kolayca uygulanan bir olasılık modelidir ve özellikle heyelan duyarlılık haritalarının üretiminde yaygın şekilde kullanılmaktadır [15][16][17][18][19][20]. Bu çalışmaların temel varsayımı; "genelde heyelanların bilinen bazı çevresel faktörlere bağlı olarak ortaya çıktığı ve gelecekteki heyelanların da geçmiştekilerle aynı şartlar altında meydana geleceği"dir [21].…”

Landslide Susceptibility Mapping for 2015 Earthquake Region of Sindhupalchowk, Nepal using Frequency Ratio

“…For this purpose, the collected landslide inventory is itself used by randomly splitting in two groups: one for analysis and one for validation (Regmi et al, 2014;Bui et al, 2014;Xu et al, 2012). The analysis is carried out in part of the study area or model and tested in another with different landslides.…”

“…The analysis is carried out in part of the study area or model and tested in another with different landslides. In literature, statistically, the accuracy assessment is carried by using error rates, receiver operating characteristics (ROC) curve, success and prediction rate curve and Area under curve (AUC) these curves (Regmi et al, 2014;Lee et al, 2007). and lies between latitude 27 51'56.00"N to 27 56'38.00"N and longitude 85 49'18.00"E to 85 52'58.00"E (Fig.…”

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

“…Some researchers have recently applied different classification methods to LSM prediction. Many statistical methods exist for LSM, such as the logistic regression (LR) method [13][14][15][16][17], statistical index (SI) [18,19], discriminant analysis (DA) [20] and bivariate statistical analysis (BSA) [21]. However, the statistical approaches have very strict mathematical reasoning, and their application must meet strict demands.…”