Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India

Abstract

Abstract.A quantitative procedure for estimating landslide risk to life and property is presented and applied in a mountainous area in the Nilgiri hills of southern India. Risk is estimated for elements at risk located in both initiation zones and run-out paths of potential landslides. Loss of life is expressed as individual risk and as societal risk using F-N curves, whereas the direct loss of properties is expressed in monetary terms.An inventory of 1084 landslides was prepared from historical records available for the period between 1987 and 2009. A substantially complete inventory was obtained for landslides on cut slopes (1042 landslides), while for natural slopes information on only 42 landslides was available. Most landslides were shallow translational debris slides and debris flowslides triggered by rainfall. On natural slopes most landslides occurred as first-time failures.For landslide hazard assessment the following information was derived: (1) landslides on natural slopes grouped into three landslide magnitude classes, based on landslide volumes, (2) the number of future landslides on natural slopes, obtained by establishing a relationship between the number of landslides on natural slopes and cut slopes for different return periods using a Gumbel distribution model, (3) landslide susceptible zones, obtained using a logistic regression model, and (4) distribution of landslides in the susceptible zones, obtained from the model fitting performance (success rate curve). The run-out distance of landslides was assessed empirically using landslide volumes, and the vulnerability of elements at risk was subjectively assessed based on limited historic incidents.Direct specific risk was estimated individually for tea/coffee and horticulture plantations, transport infrastructures, buildings, and people both in initiation and run-out Correspondence to: P. Jaiswal (jaiswal@itc.nl) areas. Risks were calculated by considering the minimum, average, and maximum landslide volumes in each magnitude class and the corresponding minimum, average, and maximum run-out distances and vulnerability values, thus obtaining a range of risk values per return period. The results indicate that the total annual minimum, average, and maximum losses are about US$ 44 000, US$ 136 000 and US$ 268 000, respectively. The maximum risk to population varies from 2.1 × 10 −1 for one or more lives lost to 6.0 × 10 −2 yr −1 for 100 or more lives lost. The obtained results will provide a basis for planning risk reduction strategies in the Nilgiri area.

Landslide Databases as Tools for Integrated Assessment of Landslide Risk

“…. Examples of recent studies dealing with analysis of distributed landslide risk for different types of physical infrastructures Jaiswal et al (2011), and Erener and Düzgün (2013). In landslide risk assessment for the transportation sector, a major role is played by analyzing the vehicle risk or the risk of loss of life (e.g., Ko Ko et al 2005;Dorren et al 2009;Li et al 2009;Ferlisi et al 2012;Michoud et al 2012), whereas potential property losses received little scientific attention so far (e.g., Zêzere et al 2007;Jaiswal et al 2010;Bründl et al 2012).…”

The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

“…The results show that the 'slope' was the most important factor as well as it was 1.4 times more important than 'aspect' in landslide susceptibility mapping. Additionally, logistic regression model got the output value (0.278) using Hosmer and Lemeshow Goodnessof-Fit test [42]. The output value more than 0.05 means that the logistic regression model is valid.…”

“…Previous studies have applied probabilistic models including an AHP: Analytic Hierarchy Process, Arti cial Neural Network, Dempster-Shapfer theory of evidence, fuzzy logic and Monte Carlo methods [1][2][3][4][5][6][7][8][9][10][11] among statistical models, the logistic regression model has also been applied to landslide susceptibility mapping [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. More sophisticated assessments have involved weight of evidence approaches and frequency ratio [25][26][27][28][29][30][31][32][33][34][35][36][37][38] Research on rainfall probability calculation has primarily been limited to improving the rainfall probability predictions accuracy and to studies targeting water resources [39][40][41][42][43]. Recently, analysis of lan...…”

Shallow landslide's stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications

“…However, these studies provide a mean annual risk with no information on the expected distribution of annual costs. More recently, applications of regional-scale QRA providing exceedance probabilities were presented in Jaiswal et al (2011) and Ghosh et al (2012). Although most of the QRA methodologies are developed for local or regional scales, some of them, for example Catani et al (2005), might be generalized to a larger area.…”