Spatial analysis of landslide susceptibility using failure rate approach in the Hindu Kush region, Pakistan

Abstract

This paper analyses and applies a spatio-statistical failure rate (SSFR) technique for landslide susceptibility zonation in the Hindu Kush region, Pakistan. The study area (Shahpur valley) is located in the eastern Hindu Kush mountain system. In Shahpur valley, land sliding is a recurrent and costly extreme event. Geologically, this region constitutes the youngest mountain systems and almost every year landslide-induced losses are reported. The frequency and intensity of landslide events is expected to further increase in future due to rapid population growth over the fragile slopes, infrastructural development and deforestation. In order to achieve objectives of the study, data were obtained from both primary and secondary sources. In Shahpur valley, an inventory of the past 300 landslide events of various sizes has been identified and marked on a SPOT satellite image of 2.5 m resolution. In order to identify the influence of landslide triggering factors, such as geology, tectonic structures, land use, slope angle, slope aspect, roads and streams, a univariate SSFR technique has been tested and applied for calculating the susceptibility score in each class of the selected parameters. Based on factor maps and cumulative score, the landslide susceptibility zones have been developed and validated appearing to be significantly reflecting the pattern of the past landslide events.

Assessment of Landslide Susceptibility using Weight of Evidence and Frequency Ratio Model in Shahpur Valley, Eastern Hindu Kush

“…The valley receives heavy rainfall during the summer season from monsoon, while in winter at higher altitudes mostly precipitation occurs in the form of heavy snowfall. The climate of the valley remains mild to warm in summer while temperature decrease to chill cold in winter season throughout the valley (G. Rahman et al, 2019). The uplift of HKH region is due to the collision of Eurasian and Indian plate during the Cretaceous and Mio-Pliocene epoch.…”

“…The occurrence of landslides has a strong correlation with fault lines (Korup, 2004;G. Rahman et al, 2019).…”

Spatio-statistical comparative approaches for landslide susceptibility modeling: case of Mae Phun, Uttaradit Province, Thailand

“…Every year thousands of people lost their lives, and about 4 billion dollars property damage occurs globally due to landslide [7,8]. It is the third most devastating natural hazard in terms of damages after earthquake and flood [9]; however, individual landslide is not so obvious and devastating as an event of earthquake and flood [10]. Identification of the factors responsible for existing landslides is the initial step toward the prediction of future landslides by analyzing the most influencing factors, and the processes are known as landslide susceptibility mapping studied by various researchers around the globe [11].…”

“…Landslide susceptibility modeling identifies the vulnerable areas and its relationship with various influencing factors. Globally, it is a widely studied phenomenon and various quantitative and qualitative methods have been developed for landslide susceptibility modeling [10,12]. The qualitative method helps to rank the conditioning factors based on the precedence scale, whereas the quantitative methods highly depend upon the relationship between conditioning factors and landslide inventory to classify the area into different landslide susceptible zones [13].…”

Assessment of Landslide Susceptibility using Weight of Evidence and Frequency Ratio Model in Shahpur Valley, Eastern Hindu Kush

“…heavy snowfall. Climate of the valley remain mild to warm in summer while temperature decrease to chill cold in winter season throughout the valley (G Rahman et al, 2019)…”