Landslide susceptibility evaluation and hazard zonation techniques – a review

Abstract

Landslides are the most destructive geological hazard in the hilly regions. For systematic landslide mitigation and management, landslide evaluation and hazard zonation is required. Over the past few decades several techniques have been developed that can be used for landslide evaluation and zonation. These techniques can broadly be classified into qualitative and quantitative approaches. Qualitative approaches include geomorphological analysis and heuristic techniques whereas quantitative approaches include statistical, artificial intelligence and deterministic techniques. In quantitative techniques prediction for landslide susceptibility is based on the actual realistic data and interpretations. Further, the quantitative techniques also overcome the subjectivity of qualitative approaches. Each of these techniques may consider different causative factors and utilizes various means for factor evaluation and analysis. When compared, each of these techniques has its own advantage and disadvantage over other techniques. The selection of appropriate technique for landslide hazard evaluation and zonation is very crucial. The factors that need to be considered to adopt an appropriate approach are; investigation purpose, the extent of the area to be covered, the type of mapping units, the scale of map to be produced, type of data to be used, type of landslides, availability of resources, capability and skill set of an evaluator and the accessibility to the study area. The main aim of this article is to present a comprehensive review on various techniques and approaches available for landslide susceptibility and hazard zonation mapping. Further, attempt is also made to assess the effectiveness of these techniques in landslide hazard zonation studies.

Landslide hazard zonation using logistic regression technique: the case of Shafe and Baso catchments, Gamo highland, Ethiopia

“…It is often chosen if the predictor variables consist of continuous and categorical variables and/or if they are not nicely distributed but logistic regression makes no assumptions about the distributions of the predictor variables). According to (Lee & pradhan 2006;Shano et al 2020), calculated the Landslide Hazard Index (LHI) by solving the regression equation. Correlation between landslide event and landslide affecting factors is estimated, and then, an equation predicting the landslide hazard is obtained.…”

“…A general definition of landslide is it consists of almost all varieties of mass movements on the slope, including some, such as rockfalls, topples, and debris flows, that involve little or no true sliding (Varnes et al 1984;Fell et al 2008;van Westen et al 2008;Reichenbach et al 2018;Shano et al 2021). To characterize or model the above-defined landslide types, there are different types of techniques such as heuristic, statistical, machine learning and deterministic approaches (Raghuvanshi et al 2015;Zumpano et al 2018;Tiranti & Crenonini 2019;Shano et al 2020). These approaches have advantages and disadvantages as well as purpose and application scale ranges when using for landslide hazard zonation or mapping; these were well characterized (Shano et al 2020).…”

“…To characterize or model the above-defined landslide types, there are different types of techniques such as heuristic, statistical, machine learning and deterministic approaches (Raghuvanshi et al 2015;Zumpano et al 2018;Tiranti & Crenonini 2019;Shano et al 2020). These approaches have advantages and disadvantages as well as purpose and application scale ranges when using for landslide hazard zonation or mapping; these were well characterized (Shano et al 2020). A landslide hazard zonation is that applies in a general sense to divide the land surface into areas and the ranking of these according to degrees of the actual or potential hazard for landslides or other mass movements on slopes (Guzzetti et al 1999;Chen and Wang 2007;Wang and Peng 2009;Davies 2015;Raghuvanshi et al 2015;Thennavan and Pattukandan Ganapathy 2020).…”

“…The study applied a binary logistic regression model because the collected data were a combination of continuous and discrete and also the dependent variable is binary output. The working scale, the data type, and the characteristics of the dependent variable are well described (Chen and Wang 2007;Lee 2015;Shano et al 2020). For landslide hazard analysis this study used different thematic layers or causative factors.…”

Landslide Risk Mapping in an Urban Area of the City of Natal, Brazil

“…Mapping the risk of landslides is an essential tool for the definition of land use and occupation policies in urban areas (e.g., [21]). The methodology applied in this study falls well within the range of available techniques used for susceptibility and hazard mapping of landslides [22]. Based on the classification of mapping techniques into qualitative, semi-quantitative and qualitative-where the qualitative techniques are supported by geomorphic analysis and the analysis of landslide inventory that occurred in the studied region while, on the other hand, semi-quantitative techniques are based on multicriteria decision analysis, and the quantitative techniques include statistical and probabilistic analysis, a deterministic approach, and artificial intelligence-the method used in this study can be classified as semi-quantitative.…”