Weighted Linear Combination Method Research Articles

Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach.

The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F-score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F-score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides.

Proposing Optimal Locations for Runoff Harvesting and Water Management Structures in the Hami Qeshan Watershed, Iraq

Iraq, including the investigated watershed, has endured destructive floods and drought due to precipitation variability in recent years. Protecting susceptible areas from flooding and ensuring water supply is essential for maintaining basic human needs, agricultural production, and industry development. Therefore, locating and constructing storage structures is a significant initiative to alleviate flooding and conserve excessive surface water for future growth. This study aims to identify suitable locations for Runoff Harvesting (RH) and dam construction in the Hami Qeshan Watershed (HQW), Slemani Governorate, Iraq. We integrated in situ data, remotely sensed images, and Multi-Criteria Decision Analysis (MCDA) approaches for site selection within the Geographical Information Systems (GIS) environment. A total of ten criteria were employed to generate the RH suitability maps, including topographic position index, lithology, slope, precipitation, soil group, stream width, land cover, elevation, distance to faults, and distance to town/city. The weights of the utilized factors were determined via Weighted Linear Combination (WLC) and Analytic Hierarchy Process (AHP). The resulting RH maps were validated through 16 dam sites preselected by the Ministry of Agriculture and Water Resources (MAWR). Findings showed that the WLC method slightly outperformed AHP regarding efficiency and exhibited a higher overall accuracy. WLC achieved a higher average overall accuracy of 69%; consequently, it was chosen to locate new multipurpose dams for runoff harvesting in the study area. The overall accuracy of the 10 suggested locations in HQW ranged between 66% and 87%. Two of these sites align with the 16 locations MAWR has recommended: sites 2 and 5 in the northwest of HQW. It is noteworthy that all MAWR dam sites were situated in medium to excellent RH zones; however, they mostly sat on ineffective geological localities. It is concluded that a careful selection of the predictive factors and their respective weights is far more critical than the applied methods. This research offers decision-makers a practical and cost-effective tool for screening site suitability in data-scarce rugged terrains.

Water Harvesting in the Garmian Region (Kurdistan, Iraq) Using GIS and Remote Sensing

Rainwater harvesting (RWH) is one of the essential alternative sources of water that may be used. However, finding potential RWH locations on a broad scale has always been challenging, requiring further research. The effectiveness of RWH structures depends on their design and the selection of suitable sites. This study aimed to select potential sites for rainwater harvesting in the Garmian region of Kurdistan-Iraq, using GIS and Remote Sensing (RS) techniques. Two multicriteria analysis (MCA) techniques: the analytical hierarchal process (AHP) and Fuzzy analytical hierarchal process (FAHP) were used for comparison purposes to identify potential RWH sites. A suitability map of potential RWH systems was then generated following the weighted linear combination (WLC) and the Fuzzy Gamma methods. The outcomes showed that, unlike most recent studies, which relied on the minimum and maximum local rainfall values, a minimum agreed-upon rainfall depth should be used as a benchmark when classifying rainfall to obtain realistic results. The findings also showed that 9.7%, 43.4%, 33.6%, and 13.3% of the sites have very high, high, moderate, and low suitability for rainwater harvesting, respectively, depending on the suitability map produced by the WLC method. However, the outcome of the Fuzzy Gamma technique appeared more realistic and revealed that 10.6%, 15.6%, 26.6%, 21.9%, and 25.3% of the sites have very high, high, moderate, low, and very low appropriateness for rainwater harvesting, respectively. The methods adopted in this study can be helpful for researchers in the field of RWH to achieve reliable results. Also, this study’s findings can assist in well-organized water resource management planning to ensure sustainable water development in the Garmian region.

Agro Climatic Zoning of Saffron Culture in Miyaneh City by Using WLC Method and Remote Sensing Data

Recent continuous droughts and decreasing ground water tables have prompted efforts to improve irrigation schedules and introduce crops that need less water. A study was recently conducted to determine suitable zones for saffron in Miyaneh using Landsat-8 images and the weighted linear combination (WLC) method. Climatic and geographical indices for saffron cultivation in the region were for soil type, slope, soil moisture, and soil salinity. Parameters such as 30 years of data on climate, soil, and water conditions were collected from synoptic and climatologic stations such as Tabriz. Then, parameters were weighted using WLC for importance in each region. The data were transferred to expert choice and clustered, rated, and integrated to produce the last layer. The results showed that the southeastern and northwestern regions of Miyaneh, especially the banks of rivers and catchments, were identified as suitable places for saffron cultivation and that 28% of the area is in the suitable class, 36% in the relatively moderately suitable class, 20% in the critical suitability class, and the rest of the area, which covers about 16% of the area, is in the non-suitable class. Therefore, if it is possible to identify favorable areas for saffron cultivation according to the climatic requirements and it is possible in practice to achieve higher performance per unit area, that in itself will contribute to improved economic conditions and levels of income for farmers. Due to the special characteristics of saffron, substituting it for the cultivation of crops with high water requirements, such as onions, potatoes, tomatoes, etc., will help reduce water consumption.

A multicriteria evaluation approach to set forest restoration priorities based on water ecosystem services

Forest plays an important role in keeping water ecosystem services, such as drinking water provision. Thus, payment for ecosystem services is an essential instrument to promote forest restoration in agricultural watersheds. However, funds are limited and must be well planned to ensure water resources conservation and water ecosystem services improvement. In this context, our study aimed to identify priority areas for forest restoration, based on water ecosystem services in agricultural landscapes. For this, we have developed a decision-making support model for agricultural watersheds (in the Atlantic Forest region), based on mixed approaches, that were multicriteria evaluation (MCE) and Participatory Technique. The model will help decision-makers and stakeholders to set priorities for payment for ecosystem services programs implementation. So, we evaluate its application in watersheds with different forest cover patterns to check if it can be applied to different landscape patterns. The base of the model was the following criteria, that were produced with high-resolution data and ranking in the Participatory Technique context, considering their importance for the study: proximity to spring, slope, soil erodibility, topographic index, and land-use/land-cover (LULC). The criteria were aggregated by the Weighted Linear Combination (WLC) method (an MCE method). The priorities maps showed areas classified as high priority near the rivers (at most 200 m far from rivers), on the greatest slopes (>40%), on soils associated with high potential of erosion, and predominantly in agriculture lands. However, this class presented more percentage of the area associated with native forest in the forested watershed (native forest covers 55% of its area) than in the watershed non-forested (native forest covers 25%). Another important point of the final maps was a high percentage of areas associated with the medium class, which is a characteristic of the WLC method. Thus, areas classified as high and medium priority was defined as targets for forest restoration in the watersheds. We can conclude that for small watersheds, the MCE method, with high-resolution data, supports an appropriate prioritization of areas for forest restoration, aiming at the improvement of water ecosystem services. This way, our model can be applied to various payments for ecosystem services schemes in agricultural landscapes worldwide.

Land use suitability analysis for town development planning in Nanjing hilly areas: A case study of Tangshan new town, China

Land use suitability analysis plays an important role in sustainable land use and solving environmental problems caused by rapid urban development. A land use suitability mapping approach for town development planning in hilly areas was constructed based on two multi-criteria evaluation methods: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA), to comparatively evaluate and map land use suitability of Tangshan new town in Nanjing, China. Fourteen evaluation factors related to topographic, environmental, socio-economics and historical sites data were used as suitability criteria. The analytic hierarchy process (AHP) method and GIS techniques were integrated into the evaluation models to create the land use suitability map for town development planning. The results of WLC approach showed that 11.4% of the total area is highly suitable while the 48.6% is unsuitable. The results of WLC and OWA approach showed the distribution of degree of land use suitability is almost the same. The areas located at the southern and eastern flat regions are highly suitable for land use, whereas the areas close to the mountain forests, steep slopes, waters, and hot springs, have lower suitability for land use. Sensitivity analysis indicated that the suitability results of the two proposed methods are robust. Indirect validation was achieved by mutual comparison of suitability maps derived from the WLC and OWA methods. It demonstrated that the overall agreement is 90.81% and kappa coefficient is 0.81, indicating that both methods provide very similar spatial suitability distributions. By overlaying the resultant map with the previous master plan map of Tangshan new town, the overlay map once again indicated a satisfactory ecological fit between the two maps. At last, several recommendations are proposed aiming at improving the long-term town development plan for Tangshan new town.

Landfill site selection by integrating fuzzy logic, AHP, and WLC method based on multi-criteria decision analysis.

Rapid population growth integrated with poor governance and urban planning is highly challenging resulting key for the selection of unsuitable landfill sites, particularly in developing counties. Therefore, the aim of this study is to investigate the suitable solid waste landfill sites in the capital of the country as a case study, by the integration of Geographical Information System (GIS) with fuzzy logic, analytical hierarchy process (AHP), and weighted linear combination (WLC) method based on multi-criteria decision-making (MCDM). We chose thirteen (13) criteria (9 factors and 4 constraints) and grouped them into two main categories (environmental and socioeconomic) to achieve the objectives. The AHP was employed to evaluate the relative importance of the factors followed by standardization of criteria factors based on fuzzy set theory. Subsequently, all criteria factors were combined based on AHP and fuzzy logic-WLC method in order to obtain land suitability map. Finally, the sites were identified by the intersection of two combined suitability index layers. The obtained results depicted that the integration of fuzzy logic, AHP, and WLC technique with GIS can produce satisfactory results for the suitable locations of solid waste landfill sites over complex topographic regions. Overall, the land suitability obtained based on fuzzy-WLC is more refined and smooth because of its better segregation and its potential to consider full tradeoff between factors and average risk. The AHP was identified (47km2) as high suitable while fuzzy-WLC generated 36km2 as suitable area. Finally, the intersection of both suitability index map shows numerous suitable landfill sites available in Islamabad city; however, the surface areas of the sites are small at individual level (less than 15ha).

Priority areas for forest restoration aiming at the maintenance of water resources in a basin in the Cerrado/Amazon ecotone, Brazil

Mato Grosso is one of the states with the greatest agricultural importance in Brazil and represents one of the main agricultural frontiers in the world. The expansion of these frontiers, along with deforestation, livestock farming and urbanization, leads to a new dynamics with significant environmental impacts on the availability of water resources. In this context, the objective of the study was to evaluate methods of multi-criteria analysis and map priority areas for forest restoration in the Upper Teles Pires basin – Mato Grosso, Brazil, in order to support decision makers and the maintenance of water resources, according to the quantity and quality of water. Multi-criteria Analysis (MCA), with integration of the criteria of proximity to forest, slope, erodibility and proximity to surface water, was used. The criteria maps were aggregated using the Ordered Weighted Averaging (OWA), with varying risk and tradeoff, and Weighted Linear Combination (WLC) methods. From the MCA it was possible to successfully identify potential areas for forest restoration for the basin under study. The WLC, OWA2 and OWA6 methods showed a good solution for decision-making as they respectively set 16.38%, 22.73% and 12.79% of the total area of the basin as of very high priority.

Landfill site selection using GIS-based multi-criteria evaluation (case study: SaharKhiz Region located in Gilan Province in Iran)

Recognizing potential sites for landfill has increasingly become an important waste management strategy around the world. This study aims to determine municipal solid waste (MSW) landfills in SaharKhiz Region located in Gilan by comparing Fuzzy logic and Boolean logic. Fuzzy logic, which has been used in this research, is based on weighted linear combination (WLC); however, the utilized Boolean logic is considered only to determine the accuracy and validity of every method. At first, the Boolean logic using a geographical information system (GIS) is used to recognize potential and excluded zones, based on zero and one value system. In the next phase, Fuzzy logic is used, between zero and one, to standardize information layers, based on their type (increasing or decreasing). The final weight of every layer was determined using the analytical hierarchy process. Finally, the WLC method was used to integrate layers in the GIS environment to provide the final site suitability map in five classes of Fuzzy membership degree. The results show that Fuzzy logic, based on WLC, has more flexibility to resolve conflicts of human judgment, and it also has higher accuracy than Boolean logic in the selection of optimal landfill sites for MSW in SaharKhiz Region, Gilan Province, based on ecological and socioeconomic parameters.

Identifying Suitable Sites for Macroalgae Cultivation on the Swedish West Coast

Special attention has been paid to sustainable macroalgae cultivation in Europe. The question on where suitable cultivation areas lie, without conflicting with current marine socio-economic activities and respecting the environment, remains a great challenge. Considering 13 criteria critical to seaweed farming such as depth, shipping traffic, and distance to ports, this paper aimed to identify suitable and sustainable offshore areas on the West Coast of Sweden for the cultivation of the Sugar Kelp, Saccharina latissima. An integrated approach with the tools geographic information systems (GIS) and multi-criteria analysis (MCA) was used to aggregate the criteria by means of Boolean and weighted linear combination (WLC) techniques. The Boolean method singled out 544 km2 as suitable, whereas the WLC method indicated 475 km2 as highly suitable. Both techniques complement each other in finding optimal sites. Furthermore, the integrated models excelled in providing an overview for effective spatial decision-making that fosters sustainable development of macroalgae cultivations within marine and coastal systems.HighlightsTo the authors’ knowledge no study on seaweed aquaculture site selection has been conducted using such a range of criteria with the purpose of including sustainability aspects within a comparative GIS-MCDA.The large areas identified on the West Coast of Sweden as suitable highlight the potential of this new industry and the complexity of associated marine spatial planning.Boolean and weighted linear combination methods were applied and compared, providing valuable insights in the selection of methods for spatial decision-making support. These insights should support a more sustainable development of macroalgae cultivation in the region, as well as a more resilient marine spatial planning process for blue growth strategies.

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.

A New Hybrid Decision Making Approach for Housing Suitability Mapping of an Urban Area

In urban planning, housing evaluation of residential areas plays a critical role in promoting economic efficiency. This study produced an evolutionary-based map through the combination of hybrid Multicriteria Decision Making (MCDM) and Geographical Information System (GIS) by assessing suitability of housing location. Suitable locations were modelled and determined with the present study from very low suitability to very high suitability. In the first stage, Fuzzy DEMATEL (the Decision Making Trial and Evaluation Laboratory) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) under fuzzy conditions as a subjective and an objective (model-based) technique, respectively, were employed to find the weights of criteria which are critical part of decision making. In the second stage, housing evaluation map for these two approaches was drawn and their performances were classified and measured with WLC (Weighted Linear Combination) method. 29 criteria determined were prioritized as per judgment of urban planning and real estate experts for Fuzzy DEMATEL and CMA-ES. After having been coded to MATLAB for obtaining optimum weights in CMA-ES, all collected data for 160 houses were mapped as vectorial (positional) and transformed to raster (pixel) data by getting entered in ArcGIS 10.4 software. We achieved CMA-ES-WLC maximization values for 104 alternatives with (positive value) 65% performance, but we obtained FDEMATEL-WLC maximization values for 56 alternatives with (negative value) 35% performance. WLC values calculated with CMA-ES and FDEMATEL weights allowed us to conclude that the houses with the highest suitability in terms of investment are in Alpaslan, Köşk, and Melikgazi streets. The result shows that the methodology used in the application of this study performed in Turkey is an important and powerful technology in providing decision support for spatial planning.

Ecosystem services-based composite indicator for assessing community resilience to floods

Disaster resilience studies have acknowledged the role of the natural environment in reinforcing community resilience; however, pragmatic environmental indicators are lacking, particularly geospatial composite indicators. This paper aims to introduce a composite environmental indicator for assessing community resilience to floods, targeting regional-scale geospatial applications. The composite indicator has been built on conceptualized inter-relationships between Ecosystem Services (ESs) and community resilience. The environmental parameters used to measure the composite were identified by surveying the cross-disciplinary literature from the domains of ESs and disaster resilience. The application of the composite indicator was demonstrated by a case study in Colombo, Sri Lanka. The composite indicator was tested on flood declaration and community response data from Colombo. The application employed a Weighted Linear Combination Method (WLCM) and was executed in a Geographic Information System (GIS)-based platform. The geospatial data for application and validation were collected through secondary sources. The developed composite indicator consists of four proxy indicators (i.e., soil hydraulic properties, slope, land use, and a precipitation factor) and parameters used to measure them. The parameters were also derived from the conceptualized relationship that elaborates ESs into a bundle of services, including flood regulation, climate regulation, and nutrient recycling, whereas many of the existing resilience assessment methodologies focused only on flood regulation. Furthermore, the composite indicator organized the environmental parameters into two tiers, facilitating a range of users. Hence, incorporating this ESs-based composite indicator into existing resilience assessment methodologies could guide community resilience-building initiatives towards more sustainable outcomes.

PRIORITY AREAS FOR FOREST CONSERVATION, AIMING AT THE MAINTENANCE OF WATER RESOURCES, THROUGH THE MULTICRITERIA EVALUATION1

Abstract Replacing the original land cover by other land uses, especially when it is associated with inadequate management practices, can cause changes in runoff and rainwater infiltration. This can result in above normal levels of soil erosion and sediment-carrying to the rivers and streams. The original land cover conservation in the watersheds is, therefore, essential for the maintenance of its water resources. In this context, the main objective of this study was to prioritize areas for forest conservation in two watersheds, aiming at maintaining the water availability, in terms of quantity and quality, for the public supply. The watersheds were selected considering their regional importance and because they are similar in terms of land use / land cover. The study was developed in the Multicriteria Evaluation (MCE) context, which permits the integration of different landscape characteristics (i.e. factors), in order to obtain a solution for the decision-making process. The following criteria were selected by considering the expert's opinions: slope, flow accumulation, aspect, and land use / land cover. Their relative importance (i.e. factor weight) was defined through the Pairwise Comparison Method. The criteria maps units were normalized by a common scale and then aggregated through an MCE method named Weighted Linear Combination (WLC). Pearson correlation was used to evaluate the criteria contribution on the final map. The watershed 1 was classified in approximately 14% of its area as very high priority; 27% as high; 19% as medium; 21% as low; and 18% as very low. The watershed 2 obtained, respectively, 17%; 29%; 17%; 21%; e 17%. We conclude that the WLC method supports the definition of priority areas for forest conservation in the watersheds, in order to have an appropriate design of actions for forest conservation.

Determining an appropriate method for the purpose of land allocation for ecotourism development (case study: Taleghan County, Iran).

Appropriate management and planning of suitable areas for the development of ecotourism activities can play an important role in ensuring proper use of the environment. Due to the complexity of nature, applying different tools and models-particularly multi-criteria methods-can be useful in order to achieve these goals. In this study, to indicate suitable areas (land allocation) for ecotourism activities in Taleghan county, weighted linear combination (WLC) using geographical information system (GIS), fuzzy logic, and analytical network process (ANP) were used. To compare the applicability of each of these methods in achieving the goal, the results were compared with the previous model presented by Makhdoum. The results showed that the WLC and ANP methods are more efficient than the Makhdoum model in allocating lands for recreational areas and ecotourism purposes since concomitant use of fuzzy logic and ANP for ranking and weighing the criteria provides us with more flexible and logical conditions. Furthermore, the mentioned method makes it possible to involve ecological, economic, and social criteria simultaneously in the evaluation process in order to allocate land for ecotourism purposes.

Surface Water Contamination Risk Assessment Modeled by Fuzzy-WRASTIC.

This research provides a Fuzzy-WRASTIC new model for water resource contamination risk assessment in a GIS (Geographic Information System) environment. First, this method setting in a multi-criteria evaluation framework (MCE) reviewed and mapped the sub criteria of every above-mentioned criterion. Then, related sub-layers were phased by the observance of GIS environment standards. In the next step, first the sub-layers were combined together, next the modeling of pollution risk status was done by utilizing a fuzzy overlay method and applying the OR, AND, SUM, PRODUCT and GAMMA operators by using WLC (Weighted Linear Combination) method and providing weights in the WRASTIC model. The results provide the best combination of modeling and the percentages of its risk categories of low, medium, high and very high, which are respectively 1.8, 14.07, 51.43 and 32.7. More areas have severe risk due to the unbalanced arrangement and compact of land uses around the compact surface water resources.

Assessment of WLC and Fuzzy Logic Methods for Site Selection of Water Reservoirs in Malaysia

The purpose of this study is to compare the weighted linear combination (WLC) and fuzzy logic (FL) models in identifying the most suitable location for a water reservoir in the area of Batu Pahat, Johor, Malaysia. First of all, parameters important to a water reservoir for the studied area were identified. Then maps of the study area were prepared and integrated. The main criteria selected for this study are pipe line, elevation, rive, land use, road network, water supply network, and slope. Suitable locations for the water reservoir were selected using each model. The results of this study indicated that the FL method in the early stages had better decision-making powers for locating water reservoir sites when compared to the WLC method. The appropriate places for water reservoir construction based on two models were located to the northwest and west of the study area. The two models are validated using the Kappa index. According to the results of the validation method, the map produced by the FL method exhibits satisfactory properties in the study area. Thus, it can be concluded that the results derived from two models (WLC and FL) integrated in ArcGIS can be a useful tool in GIS analysis for the determination of suitable locations for water reservoir in the study area.

Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam)

The purpose of this study is to carry out a regional landslide susceptibility mapping for the upper Lo River catchment (ULRC) in northern Vietnam, where data on spatial distribution of historic landslides and environmental factors are very limited. Two methods, analytical hierarchy process (AHP) and weighted linear combination (WLC), were combined to create a landslide susceptibility map for the ULRC study area. In the first step, 216 existing landslides that occurred in the study area were mapped in field surveys in 2010 and 2011. A spatial database including six landslide factor maps related to elevation, slope gradient, drainage density, fault density, types of weathering crust, and types of land cover was constructed from various sources. To determine the relative importance of the six landslide factors and their classes within the landslide susceptibility analysis, weights of each factor and each factor class were defined by expert knowledge using the AHP method. To compute the landslide susceptibility, defined weights were assigned to all factor maps in raster format using the WLC method. The result is a landslide susceptibility index that is reclassified into four susceptible zones to produce a landslide susceptibility map. Finally, the landslide susceptibility zonation map was overlaid with the observed landslides in the inventory map to validate the produced map as well as the overall methodology. The results are in accordance with the occurrences of the observed landslides, in which 47.69 % of observed landslides are located in the two most susceptible zones (very-high-susceptibility zone and high-susceptibility zone) that cover 40.96 % of the total area. As the approach is able to integrate expert knowledge in the weighting of the input factors, the actual study shows that the combination of AHP and WLC methods is suitable for landslide susceptibility mapping in large mountainous areas at medium scales, particularly for areas lacking detailed input data.

Landslide susceptibility modelling applying user-defined weighting and data-driven statistical techniques in Cox’s Bazar Municipality, Bangladesh

Landslides are common geophysical hazards in the highly urbanized hilly areas of Cox’s Bazar Municipality, Bangladesh. Every year, during the monsoon, landslides cause human casualties, property damage, and economic losses. Indiscriminate hill cutting, developing settlements in dangerous hill slopes, and torrential rainfall in short period of time are responsible for triggering landslide disasters. The aim of this paper is to produce landslide susceptibility maps (LSM) to help reduce the risks of landslides. Geographic information system and remote sensing-based techniques were used for LSM considering 12 relevant factor maps (i.e. slope, land cover, NDVI, geology, geomorphology, soil moisture, rainfall pattern, distance from road, drain, stream, structure, and faults–lineaments). For the modelling purpose, four techniques were implemented—artificial hierarchy process (AHP), weighted linear combination (WLC), logistic regression (LR), and multiple logistic regression (MLR). A landslide inventory map with 74 historical landslide locations was prepared by field surveying. The modelling results are validated using the area under the relative operating characteristics curves (AUC). AUC values of AHP, WLC, LR, and MLR methods are calculated as 88, 85.90, 74.90, and 90.40 %, respectively.

Nuclear power plant locating by WLC & GIS (case study: Iran, hormozgan province)

Nuclear Power, in this current world, is one of the principle sources of energy world-wide. Electricity demand has to be met and nuclear power has the capacity needed for heat production in order to run the large turbines that generate electricity. Though this kind of power generation produces maximum power, it is a non-renewable source of energy and its effect on the environment is not predictable. Actual impact on the environment is very less, if the locating is carefully done. Also, nuclear power plant locating is an important action in environmental protection. Difference criteria should be paid attention in site selection, so using of special methods are necessary to assimilate the criteria. In this research, GIS software and Analytical Hierarchy Process were used. Weighted Linear Combination (WLC) is a relatively new MCDA which has presented more flexible and reliable results for site selection. In this paper, first of all, maps were built in considering to environmental factors, in next step, each layer, was graded. Low grade showed non-coordination or less coordination and high grade showed more coordination. Assimilate of graded map in WLC process, separates area into unsuitable, relative unsuitable, moderately suitable and suitable parts. Suitable parts can have high priority in decision making and also moderately suitable parts can have high priority for development projects in future. This paper reviews the application of WLC method in a GIS environment to select the optimum sites for nuclear power plants using case study in Hormozgan Province.