Spatial Multi-criteria Evaluation Method Research Articles

Mapping The Location of Flood Shelters in Demak Regency using The Spatial Multi Criteria Evaluation Method

Floods are the most common disasters in the world and severely disrupt lives and livelihoods. Furthermore, the developing countries, especially Indonesia, around 241 flood events in early 2023. The World Bank’s through its Climate Change Action Plan 2021-2025, aims to support Green, Resilient, and Inclusive Development, in pursuing poverty eradication and shared prosperity with a sustainability lens. In the action plan, there are actions to identify and prioritize mitigation and adaptation activities to this effect. In connection with Flood Risk Management where there is no flood shelter location Demak Regency, this study aims to research location using the Spatial Multi Criteria Evaluation method by using the Geographic Information System. Geographic Information System can provide a precise and an effective mapping to mitigate the flood disaster. The Flood shelter can help disaster preparedness activities by minimizing casualties in Demak Regency. Based on the identification results, it was found that there were 36 main points and priority flood shelters in the form of worship facilities and educational facilities.

Introduce a framework for landslide risk assessment using geospatial analysis: a case study from Kegalle District, Sri Lanka

Landslides have become a frequent natural hazard and pressing severe environmental issues in Sri Lanka. The upward trend in high-intensity rainfall events, growing population, expansion of plantation, and lifelines increased the landslide risk of the country. Though developed countries adopted in risk assessment-based management, conversely, they rely on conventional landslide hazard assessment-based risk management. Therefore, this study is attempted to create a standardized landslide risk assessment framework, combining susceptibility and vulnerability. In the experimental design, landslide susceptibility was determined by nine (09) landslide causative factors, and fourteen (14) factors assessed for landslide vulnerability. Factors were prepared, standardized, and analyzed according to the level of contribution to susceptibility and vulnerability by using spatial multi-criteria evaluation method and entropy method under geographical information system. Spatial distribution of susceptibility and vulnerability were integrated to obtain the spatial distribution of risk. Analyses indicate that highly susceptible and high vulnerable areas are not demonstrated a high level of risk individually. However, a combination of them creates a high level of risk. The risk was classified into six classes, such as highest, high, moderate, low, lowest, and no risk. The highest-risk and high-risk zones of the area show 257 km2 (15%) and 21% (350 km2) of the total land area, respectively. Moderately risk zones take part 27% (446 km2). However, 22% (375 km2) of land area categorized as low or lowest risk and 15% (255 km2) under the no-risk. The study concluded that the developed framework is transparent and easy to update periodically by the local authorities. Hence, public policymakers can use the findings of this study to plan the future development of the region and the country. In contrast, risk assessment provides essential information to enhance national disaster risk reduction strategies.

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

The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators-accuracy, precision, and the root mean square error (RMSE)-were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central-east Africa.

Landslide Susceptibility Mapping (LSM) in Kelud Volcano Using Spatial Multi-Criteria Evaluation

Mount Kelud eruption on February 2014 has a tremendous impact on the surrounding physical environment which ejected more than 200x106m3 of material. Thus, triggered secondary hazard such as landslides in the surrounding area. The purpose of this study is to map landslides susceptibility using Spatial Multi-Criteria Evaluation (SMCE) approach in Kelud mountainous area in particular within KRB 1 and 2. The identification of landslide occurrence was conducted based on remote sensing data and field observation along with four considerations criteria; topography, hydrology, soil, and environmental characteristics. Each factor then reduced into several sub-criteria such as slope, aspect, topographic position index, topographic wetness index, stream power index, rainfall, soil texture, soil structure, COLE index and land use. The SMCE method was also engaged with expert judgment provided by academic university’ view and the BPBD agencies. The result showed that the river channel and surrounding areas categorized as landslide high prone area. Furthermore, eruption material found as the sources of landslides occurrences.

A Hybrid Spatial Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in Kullu Valley, Himalayas

In this paper we report our results from analysing a hybrid spatial multi-criteria evaluation (SMCE) method for generating landslide susceptibility mapping (LSM). This study is the first of its kind in the Kullu valley, Himalayas. We used eight related geospatial conditioning factors from three main groups: geological, morphological and topographical factors. Our landslide inventory dataset has a total of 149 GPS points of landslide locations, collected based on a field survey in July 2018. The relationships between landslide locations and conditioning factors were determined using the GIS-based statistical methods of frequency ratio (FR), multi-criteria decision-making (MCDM) and the integration method of hybrid SMCE. We compared the performance of applied methods by dividing the inventory into testing (70%) and validation (30%) datasets. The area under the curve (AUC) was used to validate the results. The integration method of hybrid SMCE gave the highest accuracy rate (0.910) compared to the other two methods, with 0.797 and 0.907 accuracy rates for the analytical hierarchy process (AHP) and FR, respectively. The applied methodologies are easily transferable to other areas, and the resulting landslide susceptibility maps (LSMs) can be useful for risk mitigation and development planning purposes in the Kullu valley, Himalayas.

Toward a disaster risk assessment and mapping in the virtual geographic environment of Armenia

This paper presents the results of a research aiming natural and technological hazard, and risk assessment and mapping in Web-based holistic geographic environment, and the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 in Armenia. A common spatial multi-criteria evaluation method was used for natural and technological hazard, risk and population vulnerability assessments. The virtual geographic environment developed by the authors during the previous period of investigations was upgraded and used for the implementation of this research. It involves the following components: 1. geodatabase, 2. modeling and simulation, 3. interface for digital mapping, 4. metadata, 5. Web–based network service for collaboration. Moreover, the country specific natural and technological hazard, risk and population vulnerability assessment methods were developed, implemented, and appropriate digital maps were created.

Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future

Land cover is one of the most important conditioning factors in landslide susceptibility analysis. Usually it is considered as a static factor, but it has proven to be dynamic, with changes occurring even in few decades. In this work the influence of land cover changes on landslide susceptibility are analyzed for the past and for future scenarios. For the application, an area representative of the hilly-low mountain sectors of the Italian Southern Apennines was chosen (Rivo basin, in Molise Region). With this purpose landslide inventories and land cover maps were produced for the years 1954, 1981 and 2007. Two alternative future scenarios were created for 2050, one which follows the past trend (2050-trend), and another one more extreme, foreseeing a decrease of forested and cultivated areas (2050-alternative). The landslide susceptibility analysis was performed using the Spatial Multi-Criteria Evaluation method for different time steps, investigating changes to susceptibility over time.The results show that environmental dynamics, such as land cover change, affect slope stability in time. In fact there is a decrease of susceptibility in the past and in the future 2050-trend scenario. This is due to the increase of forest or cultivated areas, that is probably determined by a better land management, water and soil control respect to other land cover types such as shrubland, pasture or bareland. Conversely the results revealed by the alternative scenario (2050-alternative), show how the decrease in forest and cultivated areas leads to an increase in landslide susceptibility. This can be related to the assumed worst climatic condition leading to a minor agricultural activity and lower extension of forested areas, possibly associated also to the effects of forest fires. The results suggest that conscious landscape management might contribute to determine a significant reduction in landslide susceptibility.

Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping

This study evaluated the application of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping in Deokjeok-ri Creek, located in the northeastern part of Korea. This region has frequent shallow landslides often caused by intense rainfall on weathered granite soil slopes. This study compared the predictive capability of two different models: a spatial multi-criteria evaluation (SMCE) model, which is a semi-quantitative model, and a shallow landslide stability (SHALSTAB) model, which is a deterministic model used to produce shallow landslide susceptibility maps. For the SMCE model, input layers of landslide causative factors (i.e., topographic, hydrological, soil, forest, and geological factors) were prepared for pairwise comparison to obtain susceptibility weightage. For SHALSTAB, a digital elevation model was used to calculate slope and wetness indices. Field inventories were used to validate and combine the two models. A comparison of the susceptibility map obtained from the SMCE method with that obtained with the SHALSTAB method revealed that the total mismatch area between the two maps for all three susceptibility classes was about 53%. Therefore, the two results were combined to improve the reliability of the susceptibility map. The performance of the combined map was determined using the receiver operator curve (ROC). The area under curve (AUC) revealed a success accuracy of 79.56%, and the predictive accuracy was 83.6%. These results demonstrate that the combined model was more accurate than either individual model at delineating landslide-prone areas of weathered granite soil slopes.

Assessing landslide exposure in areas with limited landslide information

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans.

Evaluation of Ecological Capability using Spatial Multi Criteria Evaluation Method(SMCE) (Case study: Implementation of Indoor Recreation in Varjin Protected Area-Iran)

This study aims to investigate the potentials of Varjin, a protected area near the Tehran, for implementation of indoor recreation. Existence of many natural attractive sceneries as well as proximity to the capital city of Tehran caused this region to be a suitable area for recreational purposes. For this purpose, this research identifies the ecological potential of this area in the utilization of indoor (intensive) recreation. Thus by identifying the ecological resources of the region, in a scale of 1:100000, map layers information is dispatched to the Arc GIS. Since, the implementation of recreational usage needs more variables and criterion, multi criteria evaluation method is essentially applicable. Therefore, generation of the criterion maps and their standardization are performed using fuzzy logic and S functions. Totally, 17 layers information are utilized as criterion maps for evaluation of intensive recreational potential usage. Evaluation of criteria is utilized by means of analytical hierarchy process and Expert Choice software (1). 1