The assessment of flooding areas and developing flood hazard maps play a key role in prevention of the many social, economic and environmental damages caused by flood. Most parts of Mazandaran, Iran ...are at risk of flooding caused by heavy rainfall and rivers. In this paper, the flood hazard map of Mazandaran province is assessed using subjective and subjective–objective weights in an Ordered Weighted Averaging-based GIS analysis. Flood hazard maps are produced based on the two types of weights, along the scale ranging from the pessimistic to optimistic decision strategies. The accuracy of the flood hazard maps was evaluated based on: (1) the percentage of historic flood occurrences that are within the flood hazard maps and (2) the assessment ratio, which is the ratio of known flood areas to whole area in a particular class of flood hazard maps. The results indicate that the percentage of flood areas produced by subjective and subjective–objective weights in “Very high class” are the same in the cases of most pessimistic (14.65%) and optimistic strategy (100%). However, in other strategies (0 < ORness < 1), the subjective–objective weights show better values than subjective weights for percentage of flood hazard areas. Similarly, the assessment ratio results show that flood hazard areas produced by subjective and subjective–objective weights in “Very high class” are the same in the most pessimistic (4.47) and optimistic strategies (1.2).
Medical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of ...waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.
Monitoring and managing the rapid growth of cities, especially in developing countries, highlights the need for appropriate spatio‐temporal models to predict urban growth. The parameters affecting ...the spatio‐temporal analysis of urban growth play a key role in the prediction results. This study proposes an urban growth simulation model by integrating local subjective–objective weights and decision risk values (ORness) into a CA–Markov model. This involves the use of ordered weighting averaging as one of the multicriteria decision analysis methods to combine the weights and degree of risk for generating a variety of risk‐averse and/or risk‐taking urban growth prediction scenarios. The proposed model has been applied to predict the physical growth of Babol city, located in Mazandaran, Iran. The results indicate that the degrees of ORness = 0.3 and ORness = 0.9 yield better prediction results in the case of using the local and global weighting strategies, respectively. Furthermore, the overall accuracy of local and global weighting strategies at different degrees of risk was 87.6 and 86.8, respectively. This implies that the use of local subjective–objective weights leads to more accurate results than the global weights for simulating urban growth.
Collaborative Spatial Multicriteria Evaluation (SME) or GIS-based Multicriteria Evaluation (GME) is an effective means for decision makers, or a particular group of people to be engaged in ...participatory spatial decision making, urban planning, land use assessment, environmental assessment, etc. The paper follows two key objectives. The first objective is to provide a thorough and systematic literature review of collaborative SME. Specifically, it focuses on the tasks and tools used in collaborative SME processes. The study gives an overview of single- and multiple-model, intelligent, and interactive tools. It also demonstrates the evolution of tools based on geographic information trends and discusses usability evaluation of tools. The second objective focuses on evolving and future perspectives of collaborative SME tools. It proposes new tools and modern era of collaborative SME by integrating the tools with other relevant technologies/concepts.
Recent incremental trends in environmental pollutants alongside garbage disposals and wastes have undoubtedly affected the global visage of coastal areas. High population density, in conjunction with ...numerous tourists who visit coastal regions along with wastewater from factories, industrial centers, cities, agricultural activities, especially oil contaminants, are amongst major contributors to pollution in coastal areas. Fortunately, developing countries have recently instigated developmental procedures with the participation of the general public in an effort to protect the environment. Thus, one can perceive the importance of creating an appropriate system, which facilitates public participation in monitoring coastal environments. Such a system would notably provide solutions for many issues of pollution management in coastal regions. The purpose of this study is to design and implement a Volunteered Geographic Information (VGI)-based system through integration of concepts and methods from three areas of Geographic Information System (GIS), coastal pollution management, and public participation in order to monitor coastal pollution. After the implementation phase, Nowshahr port city, Mazandaran Province, Iran, was selected as the study area, wherein tourists, residents, and other present individuals were asked to report observable pollutants at their location. After a 3 day monitoring of coastal regions, 98 reports were registered in the system, indicating high amounts of contamination within the respective coastal area. 86% of the total recorded reports were accounts of accumulation of garbage and other dispersed solid material such as foliage and tree trunks, which were somehow the main source of pollution. 10% and 4% of the remaining reports were related to the wastewater pollution and oil contaminants, respectively. According to survey results, 74% of users were satisfied with ease of use and performance of system (26% voted very good and 48% voted good) amongst whom 67% noted the system as an advantageous and effective tool for monitoring the coastal pollution (31% cases of very good and 36% cases of good).
•The study provides a VGI-based system for monitoring and control of coastal pollution.•It integrates the concepts of coastal pollution management, GIS and public participation.•The system was utilized for pollution management in Nowshahr city, Mazandaran Province.
The Web-based Multicriteria Spatial Decision Support Systems (MC-SDSS) enhance the collaborative/participatory spatial decision making by providing the relevant GIS-based MCDA (Multicriteria Decision ...Analysis) tools for active participation/collaboration. Typically, regular/novice decision makers need to acquire knowledge from expert decision makers in a participatory decision making process. Over the last decade or so, significant research efforts have been made to use Web-based GIS-MCDA tools for collaborative spatial decision making. However, these efforts as the collaborative decision making tools lack a knowledge sharing mechanism or framework that allow for exchange and sharing of decision knowledge between decision makers (decision makers' agents). In the case of providing knowledge sharing capabilities by these tools, exchange of decision knowledge relies on decision makers' common sense to manually interpret the meanings of each other's knowledge and use the right ones. To address these limitations, this study proposes an ontology-based multi-agents approach for knowledge sharing in a collaborative MC-SDSS. The decision makers' agents committed to the ontology can interoperate and exchange decision knowledge with intended and unambiguous meanings.
•This paper facilitates knowledge sharing in collaborative spatial decision analyses.•The study uses an ontology-based multi-agents approach for knowledge sharing.•The similarity between the knowledge elements is assessed based on lexical and contextual information.
•We used OWA to generate geothermal prospectivity maps.•The concept of risk is incorporated into geothermal prospectivity mapping.•We developed prospectivity maps for Akita and Iwate provinces.
The ...exploration of geothermal regions is the first step for the use of these resources. This paper attempts to incorporate the concept of risk into the GIS-based analysis for generating geothermal prospectivity maps via Ordered Weighted Averaging (OWA) approach. The use of OWA-based approach provides a model that generates geothermal prospectivity maps with different pessimistic or optimistic strategies. The results indicate that the values of wells percentages in high favorite areas for the most pessimistic and optimistic strategies are 85% and 100%, respectively. Regarding the prediction rate, the results show that the rate for the most pessimistic and optimistic strategies are 18.55 and 1.18, respectively.
Identifying potential locations for installation of solar power plants is a critical step in utilizing sustainable energy resources. In this study, a GIS-based Multi-Criteria Decision Analysis ...(GIS-MCDA) technique is used to generate maps that represent potential areas for solar power plants in four provinces with different climate conditions in Iran. The concept of risk is included in the GIS-MCDA process using the Ordered Weighted Averaging (OWA) model. The OWA model can provide various risk-taking (optimistic) and risk-aversion (pessimistic) scenarios to determine the suitable power plant areas. The results of this study indicate that provinces located in an arid climate such as Yazd contain a more suitable area for the solar power plants compared to wet climate provinces (e.g., Mazandaran). The sensitivity analysis of results show that the criterion “fault” has the minimum effect while the criteria “slope” and “road network” have the maximum effects on the area of the highly desirable class.
•The GIS-MCDA is used to evaluate the locations for establishment of solar power plants.•The evaluation process involves the locations in different climate conditions.•An OWA-based technique is utilized to incorporate risk in solar energy assessment.•The sensitivity of results to each criterion is evaluated.
•We present a group Web-based MC-SDSS for solving parking site selection problems in Tehran, Iran.•We present a collaborative spatial multicriteria analysis procedure for parking site selection in ...Tehran.•We demonstrate the design and development of an analytic-deliberative collaborative GIS-MCDA framework.•We integrate the GIS-MCDA procedure with the Web 2.0 concepts.
Tehran, the capital of Iran and one of the largest cities in the world, faces uncontrolled urban expansion. Over the last few decades, urban expansion and traffic congestion in Tehran has greatly increased the demand for public parking facilities. In recent years, urban policy makers and the local municipalities of Tehran have focused their efforts on increasing the number of public parking facilities in different areas of the city. Their approach to parking site selection has been centralized, and has received some criticism for a perceived failure to represent certain interest groups and stakeholders coupled with an inability to provide a platform for active participation and collaboration. Due to many conflicting issues, the number of factors inherent to parking site selection, the increasing diversity of expertise areas, and the current trend to democratize planning, the use of participatory or collaborative planning methods for parking site selection has proven to be more effective. This paper presents a Web-based group GIS-MCDA procedure and tool to address the issue of parking site selection in Tehran. The integration of GIS and Multicriteria Decision Analysis (MCDA) capabilities into the Web platform offers an effective Multicriteria Spatial Decision Support System (MC-SDSS) with which to involve stakeholders and other groups in site selection processes. Such a system makes it possible to find appropriate sites that may reconcile the conflicting objectives resulting from different opinions and the final site selection outcome that can be accepted by the majority. The paper demonstrates the implementation of the proposed system for tackling the parking site selection problem in the center of District # 22 of Tehran.
The use of wind turbines can help progress towards economic and technological development, lower rates of fossil fuel consumption, decreased greenhouse emissions, and reduced side-effects of climate ...change. A successful mechanism for developing renewable energy worldwide is the guaranteed purchase of electricity generated from renewable energy sources. Accordingly, this study aims to integrate Geographic Information System-based Multi-criteria Evaluation (GIS-MCE) models with economic frameworks to estimate the optimal purchasing price for electricity produced by wind turbines. A total of 13 criteria maps were used and integrated using Ordered Weighted Averaging (OWA) as a type of MCE model. The criteria were initially normalized based on the minimum, and maximum values and weights were assigned to each criterion, using the Best-Worst method. The OWA model identified optimal site locations at various decision risk levels. The economic efficiency of wind turbines and the potential purchasing price of electricity from turbines were also assessed in terms of Net Present Value (NPV). The results show that Ardabil and Southern Khorasan provinces had the most significant areas in the very-suitable class for wind turbine installation (small/large scale). The purchasing prices for wind-generated electricity ranged from 0.047 to 0.182 US$ for large wind farms and 0.074 to 0.384 US$ for small wind plants. The highest electricity produced from large wind farms was found in Maragheh.
•This paper presents a GIS-MCE model to assess the suitability of locations for wind power plants.•It shows a GIS-MCE-based economic model for price estimation of wind energy generated electricity.•The study integrates expert weights, criteria maps, and risk degrees in the price estimation model.
