Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system

Abstract

Landslides are among hazards that undermine the social, economic, and environmental well‐being of the vulnerable community. Assessment of landslides vulnerability reveals the damages that could be recorded, estimates the severity of the impact, and increases the preparedness, response, recovery, and mitigation as well. This study aims to estimate landslides vulnerability for the western province of Rwanda. Field survey and secondary data sources identified 96 landslides used to prepare a landslides inventory map. Ten factors—altitude, slope angles, normalized difference vegetation index (NVDI), land use, distance to roads, soil texture, rainfall, lithology, population density, and possession rate of communication tools—were analyzed. The Analytical Hierarchy Process (AHP) model was used to weight and rank the vulnerability conditioning factors. Then the Weighted Linear Combination (WLC) in geographic information system (GIS) spatially estimated landslides vulnerability over the study area. The results indicated the altitude (19.7%), slope angles (16.1%), soil texture (14.3%), lithology (13.5%), and rainfall (12.2%) as the major vulnerability conditioning parameters. The produced landslides vulnerability map is divided into 5 classes: very low, low, moderate, high and very high. The proposed method is validated by using the relative landslides density index (R‐index) method, which revealed that 35.4%, 25%, and 23.9% of past landslides are observed within moderate, high, and very high vulnerability zones, respectively. The consistency of validation indicates good performance of the methodology used and the vulnerability map prepared. The results can be used by policy makers to recognize hazard vulnerability lessening and future planning needs. Integr Environ Assess Manag 2019;00:000–000. © 2019 SETAC

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

“…The findings of the present study are in agreement with the results from previous studies (MIDIMAR, 2015; Nahayo et al., ; Nsengiyumva et al., ; Piller, ) that confirmed the western part of Rwanda as a landslide‐prone zone. Furthermore, all 10 districts are alarmingly susceptible to shallow landslide hazards.…”

Safety Assessment of Casting Workshop by Cloud Model and Cause and Effect–LOPA to Protect Employee Health

“…A safety assessment is usually used to obtain the safety level of enterprises, and an assessment indicator system was established here, in which the sub-indicators mainly include educational training, safety input, dangerous and harmful factors control, hidden danger identification and security systems. After the subjective weights of the evaluated indicators were determined by analytic hierarchy process [25,26], and an appropriate safety situation was determined by experts for each evaluated indicator, the safety level of the casting workshop was preliminary assessed by the traditional fuzzy evaluation method [41]. However, the assessment process has two weaknesses: on the one hand, the fuzzy evaluation method does not reflect the randomness of assessment result; on the other hand, the subjective analytic hierarchy process does not take advantage of the objectivity of real data.…”

“…After comparing the relative importance of evaluation indicators according to the analytic hierarchy process [25,26], the judgment matrix can be achieved as follows [43].…”

“…Based on reference [25,26], we can see that the degree of preference may reach up to 9 in extreme circumstances for different industries and different evaluation indicators. For the casting workshop of the foundry enterprise, there is not much difference among the importance of different evaluation indicators in Table 1.…”

“…There are three main methods to obtain the weights of the evaluation indicators: subjective, objective, and integrated weight methods. The subjective weight methods include the analytic hierarchy process [25,26] and the Delphi method [27,28]. These subjective methods rely on subjective judgments from experts rather than actual data.…”