Application of Morphometric Indices in Optimization of Landslide Susceptibility Zonation Using Probabilistic Methods

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Baboli Moakher Hamid; TAGHIAN SEYED ALIREZA; SHIRANI KOUROSH

Title

Application of Morphometric Indices in Optimization of Landslide Susceptibility Zonation Using Probabilistic Methods

Author(s)

Baboli Moakher Hamid | TAGHIAN SEYED ALIREZA | SHIRANI KOUROSH | Issue Writer Certificate

Keywords

landslideQ1

Dempster-Shafer modelQ2

evidence of weight modelQ1

morphometryQ1

Fahlian WatershedQ2

Abstract

Introduction As a geomorphic hazard, landslide causes great deals of financial damage and casualties every year, and directly and/or indirectly contributes to large economic losses in different areas. Given that numerous factors contribute to the occurrence of a landslide, in order to prepare more accurate zonation maps, it is necessary to use more information layers and evaluate various factors possibly leading to the occurrence of the event before adopting the existing models to zonation the susceptibility map. This phenomenon is a natural hazard which is affected by the land surface shape (morphology). When it comes to susceptibility analysis of landslides in a particular area, not only common factors in zonation, but also morphometric features of ground surface are important and should be evaluated. Geomorphometric indices can be used for analysis of many geomorphologic events and natural hazards. These indices express quantitatively characteristics of hillsides which are susceptible to landslide. Materials and methods In this research, a total of 18 factors contributing to the occurrence of landslides in Fahlian Watershed were identified and evaluated. These factors are including slope, aspect, slope length, altitude, distance to fault, distance to river, precipitation, lithology, landuse, general curvature, Plan curvature, profile curvature, Normalized Difference Vegetation Index (NVDI), topographic position index (TPI), Length and Slope Factor (LSF), Terrain Ruggedness Index (TRI), Stream Power Index (SPI), and topographic wetness index (TWI). In order to prepare the layers of the effective factors, we have used geological maps at scale 1: 100, 000, topographic maps at scale 1: 50, 000, Digital Elevation Model (DEM: ASTER), satellite images, and aerial photographs. The data have been analyzed by ArcGIS, Global Mapper, Surfer, and ENVI 4. 5 software packages. Given the focus of this research on the application of morphometric indices to optimize zonation map of susceptibility to landslide, the indices were extracted. Land surface characteristics, i. e., morphometric, hydrologic, and climatic properties, etc., and land features including watersheds, stream networks, landforms, etc. were extracted using digital models of ground surface (DEM) and parameterization software. Subsequently, using Dempster-Shafer probabilistic models and the morphometric indices, we have prepared zonation map of landslide susceptibility for Fahlian River watershed. Finally, using receiver operating characteristics (ROC), both models were validated. Results and discussion Based on the weights related to the role of each unit of factor layers and their order of priority and importance in the occurrence of landslide, factor maps have been combined to produce landslide distribution maps. The weights of each level have been calculated based on the relationships related to the Dempster-Shafer model in GIS environment. For example, in this study the slope > 40% in weight and imposes the largest contributions into the occurrence of landslide across the watershed. At lower slopes, other forces such as the friction between soil particles and other hillside material are usually dominant over driving forces such as gravity. In contrast, on highly sloping hillsides, due to the dominance of shear stress over resisting force, one may end up with increased probability of the occurrence of a landslide. Moreover, based on the obtained results, with a belief weight of 0. 77, TRI > 14 was the second most effective factor on the occurrence of landslides across the studied watershed. Other contributions are as following: Stream Power Index < 1. 2, precipitation < 750 mm/year, TPI <-4. 2, profile curvature of 0. 3 – 4. 2, TWI of-1. 5 to 2. 5, surface curvature of-5 to-2. 99, distance to fault from 0 to 500. The Pabdeh – Gurpi have belief function values of 0. 68, 0. 63, 0. 60, 0. 57, 0. 49, 0. 49, 0. 47, 0. 46, 0. 38, and 0. 37, in order. . According to the evidence of weight model, the class of TRI >14 (final weight: 2939. 32) was found to be the most effective factor on the occurrence of landslide across the region. Following a similar trend of reasoning, the class of slopes higher than 40% (final weight: 2611. 21) was the second most important factor, which are in agreement with the results of Javadi et al. (2014) and Teymoori-Yanseri et al. (2017). Moreover, in their research, Pourghasemi et al. (2011) referred to the slope as the second most important factor contributing to the occurrence of landslide. In this model, NVDI > 0. 6 (final weight: 400. 60) is identified as the third most important factor. Following the land use, Stream Power Index > 1. 2, TPI <-4. 2, TRI of 7 – 14, profile curvature of 0. 1 – 0. 3, NVDI of 04 – 0. 6, precipitation > 750 mm. The Pabdeh-Gurpi Formation imposed the largest impacts. The impacts are sorted in the order of effectiveness, from final weights of 2037. 60, 1925. 99, 1803. 48, 1793. 34, 1722. 40, 1494. 60, and 1340. 28. Conclusion Final results of the present research have indicated that, in both of the models, slopes higher than 40% and TRI > 14 exhibited the highest weights and played the most significant roles in the occurrence of landslide across the region. Moreover, based on the obtained results, 82. 59% of the landslides across the watershed in an area of 547. 82 hectare had occurred in pastures. Based on the results of Dempster-Shafer model, very low, low, intermediate, high, and very high susceptibility classes covered 23. 85% (961. 34 km2), 31. 82% (1282. 49 km2), 21. 72% (875. 63 km2), 16. 41% (661. 45 km2), and 6. 20% (249. 97 km2) of the entire region, respectively. Moreover, the results obtained from the evidence of weight model shows that zones of very low, low, intermediate, high, and very high susceptibility have areas of 25. 29% (1019. 59 km2), 30. 98% (1248. 82 km2), 21. 28% (857. 64 km2), 15. 68% (631. 93 km2), and 6. 77% (272. 90 km2) on the entire susceptibility zonation map, respectively. Results of evaluating the models using ROC documented that the Dempster-Shafer model provides higher prediction accuracy (0. 79) than the evidence of weight model (0. 76). Given quantitative results of validation, the combination of Dempster-Shafer model with morphometric indices is herein introduced as an appropriate model for landslide susceptibility zonation.

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APA: Copy

Baboli Moakher, Hamid, TAGHIAN, SEYED ALIREZA, & SHIRANI, KOUROSH. (2019). Application of Morphometric Indices in Optimization of Landslide Susceptibility Zonation Using Probabilistic Methods. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, 50(4 ), 747-773. SID. https://sid.ir/paper/138714/en

Vancouver: Copy

Baboli Moakher Hamid, TAGHIAN SEYED ALIREZA, SHIRANI KOUROSH. Application of Morphometric Indices in Optimization of Landslide Susceptibility Zonation Using Probabilistic Methods. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2019;50(4 ):747-773. Available from: https://sid.ir/paper/138714/en

IEEE: Copy

Hamid Baboli Moakher, SEYED ALIREZA TAGHIAN, and KOUROSH SHIRANI, “Application of Morphometric Indices in Optimization of Landslide Susceptibility Zonation Using Probabilistic Methods,” PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, vol. 50, no. 4 , pp. 747–773, 2019, [Online]. Available: https://sid.ir/paper/138714/en

Scientific Information Database

ISSN: 2588-4824

Journal Paper

Application of Morphometric Indices in Optimization of Landslide Susceptibility Zonation Using Probabilistic Methods