Spatial Multi-criteria Evaluation Model Research Articles

A spatial model for assessment of urban vulnerability in the light of the UN New Urban Agenda guidelines: case study of Assiut City, Egypt.

Most cities in developing countries suffer environmental degradation caused by the growth of unplanned areas that sprawl in the cities. In the current paper, we attempted to integrate a set of selected UN-based urban indicators based on the New Urban Agenda (NUA) within a GIS framework to observe and assess some aspects of urban vulnerability among city districts based on deprivation. The vulnerability map for the districts in Assiut City was created through a spatial multicriteria evaluation model. Thirteen sub-indicators related to shelter, social environmental and economic situations have been assessed in the model using standardization, weighting and aggregation methods. Results revealed that: districts, namely, El Thaltha, El Owla, El Thania, and El Rabaa are most vulnerable in most scenarios, while districts, namely, El Sheyakha El Sabaa, and El Sadsa, El-Walidya El Qiblia and El-Hamra El Thania are among the least vulnerable zones. Results also revealed that vulnerable districts encompass the highest percentage of slums, highest density of population, highest rates for urban growth and poor connection to services. Eventually, we assume that the most vulnerable zones in the city are under the highest risk of airborne diseases including COVID-19 epidemic. Eventually, a subset of selected urban vulnerability indicators that could be triggering the spread of the pandemic was chosen for another spatial multcriteria model to delineate city zones under risk. The result revealed that expected high-risk areas exist in the south-west of the city and include El Thaltha, El Owla, El Thania and El Rabaa districts, while the least risk district is El-Walydia El-Qeblia. The applied methodology and its outputs could support decision makers in reviewing priorities, setting contingency plans, allocation of funds and raising resilience among the city districts.

Zoning the Rate of Pollution in Domestic River Using Spatial Multi - Criteria Evaluation Model

Abstract Surface water, especially rivers, is the most important sources of water supplies for drinking, agricultural and industry use and livelihoods. Controlling the pollution of BalikhlouChai River as one of the most important sources of water supply in Ardabil province in Iran is very significance and vital. In this research, the pollution zoning by using the Spatial Multi-Criteria Evaluation (SMCE) model in BalikhlouChai River was carried out to evaluate the trend of changes in water index and other important parameters by considering the physicochemical, biological, socio-economic and cultural constraints. First of all six stations were selected in the study zone and the sampling was performed over a 12 months period, and then the average of obtained parameters was compared with the standard values of Department of Environment in Iran. The results showed that fecal coliform in most stations and BOD5 in stations 4 and 5, and Nitrate in the stations 4, 5, and 6 were more than standard. Next, the related factors and criteria with the changes in pollution trend in river was identified, weighted and prioritized by the Analytical Network Process (ANP) model. Then the basic maps were prepared and the information layers were incorporated into the SMCE model. Finally, the zoning map of BalikhlouChai River specified in terms of pollution and river health in two contaminated and safe areas in the study basin. According to the results of this study, 3 zones for the safe and 3 zones for the contaminated areas in the study region of river basin were determined which respectively the numerical values of 1, 0.85 and 0.75 for the safe zones and 0.2 and 0.1 and about zero values for the contaminated zones were calculated. The results of quality and quantity zoning indicate that the northeast regions of the study area, including Ardabil City outlet and the Qarasu River basin have gained almost zero value.

Simulasi Sebaran Penyakit Blas Pada Tanaman Padi Menggunakan Model Spatial Multi Criteria Evaluation: Studi Kasus Kabupaten Karawang dan Purwakarta

Dispersal Simulation of Rice Blast Disease Using Spatial Multi Criteria Evalution Model: Case Study In District of Karawang and PurwakartaRice blast is caused by Pyricularia oryzae. The potential epidemic of this disease can be spatially simulated using the MCA (Multi Criteria Analysis) method based on geographical characteristics, cultivation practices, and eviromental condition. A software that can be used for MCA is SMCE (Spatial Multi Criteria Evaluation). This study was aimed to predict the spatial dispersal of blast disease using SMCE model, and identify the factors that supports the epidemic. The study was conducted in February - August 2018 in Karawang and Purwakarta District. The research methods include observing the severity of blast disease, cultivation practices and environmental conditions, and analyzing SMCE. The SMCE analysis uses rice crop maps from the Sistem Monitoring Pertanaman Padi (Simotandi), which consists of grouping factors, standardizing factors, and weighting factors. The SMCE results are a simulation map of blast disease dispersal which is then compiled with predictions of its severity. Accuracy of prediction results was evaluated by MAPE (Mean Absolute Percentage Error) based on observational data on actual disease severity. The prediction results for Karawang and Purwakarta showed means of accuration 78.16% and 73.95% respectively. In general, factors that have a strong influence on the development of blast disease include altitude, distance from source of the epidemic, history of disease in the fields, number of spores (inoculum) trapped, irrigation quality, application of herbicides, soil nutrient (N, P, K) contents and the level of soil acidity.

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 assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods

The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.

Pan-European landslide susceptibility mapping: ELSUS Version 2

ABSTRACTWe present an updated version of the European landslide susceptibility map ELSUS 1000 released through the European Soil Data Centre in 2013. The ELSUS V2 map shows the landslide susceptibility zonation for individual climate-physiographic zones across Europe. ELSUS V2 covers a larger area of Europe than ELSUS 1000 at a higher spatial resolution (200 × 200 m). The updated map was prepared using the same semi-quantitative method as for ELSUS 1000, combining landslide frequency ratios information with a spatial multi-criteria evaluation model of three thematic predictors: slope angle, shallow subsurface lithology and land cover. However, the new map was prepared using also: (i) an extended landslide inventory, containing 30% of additional locations for model calibration, map validation and classification and (ii) a new lithological data set derived from the International Hydrogeological Map of Europe (IHME). The new version of the map increases the overall predictive performance of ELSUS by 8 %.

Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda.

Landslides susceptibility assessment has to be conducted to identify prone areas and guide risk management. Landslides in Rwanda are very deadly disasters. The current research aimed to conduct landslide susceptibility assessment by applying Spatial Multi-Criteria Evaluation Model with eight layers of causal factors including: slope, distance to roads, lithology, precipitation, soil texture, soil depth, altitude and land cover. In total, 980 past landslide locations were mapped. The relationship between landslide factors and inventory map was calculated using the Spatial Multi-Criteria Evaluation. The results revealed that susceptibility is spatially distributed countrywide with 42.3% of the region classified from moderate to very high susceptibility, and this is inhabited by 49.3% of the total population. In addition, Provinces with high to very high susceptibility are West, North and South (40.4%, 22.8% and 21.5%, respectively). Subsequently, the Eastern Province becomes the peak under low susceptibility category (87.8%) with no very high susceptibility (0%). Based on these findings, the employed model produced accurate and reliable outcome in terms of susceptibility, since 49.5% of past landslides fell within the very high susceptibility category, which confirms the model’s performance. The outcomes of this study will be useful for future initiatives related to landslide risk reduction and management.

Targeted nanoparticle delivery of a novel nitric oxide donor increased fetal weight in a mouse model of fetal growth restriction

Promotion of bioenergy production is an important contemporary topic around the world. Vast amounts of research are allocated towards analysing and understanding bioenergy systems, which are by nature multi-faceted. Despite a focus on the deployment of multi-criteria decision-making (MCDM) methods for planning of bioenergy systems, only little research has addressed the location component of bioenergy facility planning. In this paper the authors develop a model for sustainable capacity expansion of the Danish biogas sector allowing for an identification and prioritization of suitable locations for biogas production. The model builds on a framework for spatial planning and decision making through the application of spatial multi-criteria evaluation (SMCE). The paper is structured around a case study including four Danish municipalities in order to demonstrate the power of the spatial multi-criteria evaluation model. The model allows a two level comparison of suitability, within municipalities as well as between municipalities. Criteria weights for generation of alternatives are obtained through an analytical hierarchy process (AHP) analysis, carried out among a group of Danish central governmental decision makers. We find that resource and production economic criteria are given highest priority followed by environmental and social criteria. In all four case study municipalities, the identified alternatives are compared through incorporating economic, environmental and social criteria. It is found that 4–6% of the municipal area is suitable for biogas facility location and among the best performing sustainable locations the potential of reducing overall production costs is 3% as compared with current biogas plants. The results of this paper can provide support to central governmental decision makers, regarding regional allocation of subsidies in the country. Likewise local decision makers can obtain important information for planning and decision support, allowing for a more inclusive and transparent planning procedure.

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.

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning.

Towards a sustainable capacity expansion of the Danish biogas sector

Promotion of bioenergy production is an important contemporary topic around the world. Vast amounts of research are allocated towards analysing and understanding bioenergy systems, which are by nature multi-faceted. Despite a focus on the deployment of multi-criteria decision-making (MCDM) methods for planning of bioenergy systems, only little research has addressed the location component of bioenergy facility planning. In this paper the authors develop a model for sustainable capacity expansion of the Danish biogas sector allowing for an identification and prioritization of suitable locations for biogas production. The model builds on a framework for spatial planning and decision making through the application of spatial multi-criteria evaluation (SMCE). The paper is structured around a case study including four Danish municipalities in order to demonstrate the power of the spatial multi-criteria evaluation model. The model allows a two level comparison of suitability, within municipalities as well as between municipalities. Criteria weights for generation of alternatives are obtained through an analytical hierarchy process (AHP) analysis, carried out among a group of Danish central governmental decision makers. We find that resource and production economic criteria are given highest priority followed by environmental and social criteria. In all four case study municipalities, the identified alternatives are compared through incorporating economic, environmental and social criteria. It is found that 4–6% of the municipal area is suitable for biogas facility location and among the best performing sustainable locations the potential of reducing overall production costs is 3% as compared with current biogas plants. The results of this paper can provide support to central governmental decision makers, regarding regional allocation of subsidies in the country. Likewise local decision makers can obtain important information for planning and decision support, allowing for a more inclusive and transparent planning procedure.

GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran)

The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

Evaluation of eco-environmental vulnerability in Efon-Alaye using remote sensing and GIS techniques

This study evaluates the environmental vulnerability of Efon-Alaye located in Ekiti State, Nigeria, which is a typical mountainous region with steep slope and an upland ecosystem. An Integrated Remote Sensing and Geographical Information System approach was used to analyse the environmental vulnerability of the region. Satellite data of the study area for two periods 1986 and 2002 were used for the analysis. Spatial multi-criteria evaluation operation was used to generate the vulnerability map of the study area. Indicators used were land-use, vegetation map (NDVI) and slope map generated from digital elevation model. The vulnerability distribution was classified into five levels: potential, slight, moderate, high and extreme. The results show that the environmental vulnerability in the study is at potential level and the driving force of this change is attributed mainly to the cultural activities of the inhabitants of the study area. The result of this study has also shown that using Integrating remote sensing, geographical information system and spatial multi-criteria evaluation model to evaluate environmental vulnerability in mountainous regions will assist decision makers in environmental management. Key words: Environmental vulnerability, spatial multi-criteria evaluation model, GIS, remote sensing, vegetation, land-use.