An Analytical Hierarchy Process-Based Landslides Prioritization Framework for Under-developed Regions

Abstract

Landslides are among the most dangerous geohazards. Frameworks developed in previous studies to assess landslide prevention have relied on machine learning, artificial intelligence, and probabilistic methods that require substantial expertise and resources. Furthermore, the collective use of geotechnical, governance, and demographic factors for prioritizing landslide prevention projects has not been explored much. There exists the need of prioritizing factors which are important for preventing landslide prevention. To achieve this, factors such as ‘area topography’ and ‘Rainfall’ are identified that can be associated with the prevention of landslides. After identifying 21 such factors from previous studies, their relevance for landslide prevention is ensured through an expert survey. These factors are categorized into four groups using Alford’s Theory of political interest and Evidencebased policy making theory. In the final step the relative significance of these factors is determined for which pair-wise comparisons of factors are made using questionnaire survey of disaster management experts. These pairwise comparisons are analyzed using Analytical hierarchy process (AHP). The results showed that earthquakes history is the most important factor to prioritize landslide projects in an area, followed by ‘rainfall density’ and governance will’ to manage such hazards. This study contributes to practice by ranking the factors which are important for preventing landslide prevention. This list can be used to develop decision-making frameworks so that different landslide projects in a region can be prioritized. Findings of this study can enable management to make wellinformed choices in landslide prevention projects while considering their budget constraints and allocate resources, optimally.