Integration Of Multi-criteria Analysis Research Articles

Simulation of urban growth scenarios using integration of multi-criteria analysis and game theory

Urban growth is a dynamic and evolutionary spatial and social process. Urban development can be considered as a function of strategic behavior of the involved agents, which is affected by various factors such as land use interactions, density of population, economic situation, etc. This study presents an integrated model of cellular automata, multi-criteria analysis, game-theory and agent-based methods for land-use planning and simulation of urban development scenarios based on growth and distribution of the population. This model quantifies and simulates the land-use interactions concerning regulations and guidelines of the master plan. In the proposed approach, the suitability of developed land-use types is evaluated by using the multi-criteria analysis. Land-use changes are defined as an outcome of the interaction between two types of decision-makers (agents): Land Developer and Municipality. Game theory is used to simulate and quantify the strategic behavior of these two agents. The optimal decision between the agents is obtained using the backward induction process. The proposed model was implemented in the region of Roozieh in the city of Semnan. Six scenarios were simulated based on the Semnan’s approved master plan and considering the stochastic perturbation. The outcome of each scenario was evaluated based on the spatial equity factors. The results demonstrated the ability of the model to simulate the consequence of urban land-use development plans based on the regulations and guidelines of the master plan, especially for the balanced distribution of services concerning existing demand in their undersupply regions.

From Traditional to Electrified Urban Road Networks: The Integration of Fuzzy Analytic Hierarchy Process and GIS as a Tool to Define a Feasibility Index—An Italian Case Study

To achieve sustainable development in the road sector, the use of Electric Vehicles (EVs) appears as a positive response to transport emissions. Among the available technologies, dynamic charging seems to overcome the main weakness points of EVs, even if it requires that traditional roads (t-roads) be equipped with a system providing electricity for EVs. Thus, so-called electrified roads (e-roads) must be implemented into the urban road networks. Since it is not possible to electrify all roads simultaneously, and also to consider the demand needs of citizens, a selection criterion is essential. This research describes and develops a simple, self-explanatory, repeatable, and adaptable selection criterion aimed at helping city managers in prioritizing the roads of an urban network to be upgraded from t-road to e-road status. This method belongs to the so-called Multicriteria Spatial Decision Support Systems (MC-SDSS)—processes useful for solving spatial problems through the integration of multicriteria analysis (Fuzzy Analytic Hierarchy Process, F-AHP) with a geo-referenced data management and analysis tool (GIS). The developed algorithm is based on several criteria related to the infrastructure/transport, social and environmental areas. The result of the implemented method is a Feasibility Index (FI), able to prioritize the roads most eligible to be upgraded as e-roads, as also verified by its application on the urban area of Milan (Italy).

A comparison of the analytical hierarchy process and the fuzzy logic approach for flood susceptibility mapping in a semi-arid ungauged basin (Biskra basin: Algeria)

ABSTRACT The study presented herein proposes a multi-criteria decision-making tool to identify flood susceptibility areas on a regional scale. The adopted approach is based on the integration of multi-criteria analysis, geographical information systems (GIS) and remote sensing (RS) techniques. Accordingly, flood susceptibility maps were prepared depending on information from six flood-conditioning factors namely Rainfall, Elevation, Slope, Land use/cover, Soil type, and Distance from the river. The relative importance of each factor on the flood susceptibility was assigned based on experts’ judgements and then connected to weight values. Two techniques were employed for weight calculation; the analytical hierarchy process (AHP) and the Fuzzy analytic hierarchy process (FAHP). The presented approach was applied to the Biskra basin, located north-eastern Algeria at the Sahara limits. The obtained maps indicate flood susceptibility areas, with high and very high flood susceptibility, mainly occurring at the northern areas of the basin which receive more rainfall, and also in urban areas all over the basin close to drainage networks. The performances of the two models were evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC) by comparing the obtained maps to an inventory map of past flood events. The evaluation results indicated that both the AHP and the FAHP models were found to be suited in predicting flood prone areas in the Biskra basin with AUC values of 93,61% and 96,61% respectively. Nevertheless, the FAHP model is preferred due to its ability to minimize vagueness and uncertainty associated with human judgements.

Modelling flood-prone area and vulnerability using integration of multi-criteria analysis and HAND model in the Ogun River Basin, Nigeria

ABSTRACT In this study, a GIS-based integration of multi-criteria analysis and the Height Above Nearest Drainage (HAND) terrain model was adopted to delineate potential flood hazard zones and vulnerability of the Ogun River Basin, Nigeria. Flood causative factors were used as input for multi-criteria analysis using an analytical hierarchy process (AHP) and weighted overlay in ArcGIS 10.5 to generate potential flood hazard zones. The flood hazard map was overlaid with demographic population data to identify areas where vulnerable people and assets are located. The results show the varying degree of people’s susceptibility to flood hazards. Flood hazard zones were classified into Very High, High, Moderate, Low and Very Low, with area coverage of 1269.40, 14139.50, 7188.40, 17.41 and 0.85 km2, respectively (occupied by 466 290, 355 542, 69 554, 231 and 54 people, respectively). This study serves as a preliminary guide for early warning and policy decision-making for flood disaster risk reduction.

Combining scenario planning, energy system analysis, and multi-criteria analysis to develop and evaluate energy scenarios

Abstract The transition from the current electricity system to a renewable electricity supply poses immense economic, technological, and policy challenges. Energy system models represent the complexity of interactions in combined processes from extraction of primary energy to the use of the final energy to supply services and goods. While these models were originally focused on energy security and costs, climate change, as the most pressing environmental concern as well as sustainability in general require the consideration of a broader range of decision-relevant aspects. In this context, scenario planning and multi-criteria decision-making can complement energy system analysis in the development and evaluation of energy scenarios. Therefore, we propose a combination of these three methods and illustrate it in a case study that investigates the transition of the electricity sector in Lower Saxony, Germany, to energy from renewable sources. The results of our case study show that the integration of multi-criteria analysis allows for better Problem structuring by focusing on relevant alternatives, external uncertainties, and evaluation criteria. The integration of scenario planning allows for a systematic investigation of external uncertainties. Thereby, the fallacy of investigating particular assumptions for uncertain parameters, which are however not consistent with the assumptions in the scenario, can be avoided. Finally, combining the methods allows for a more balanced and objective evaluation of alternative energy systems in terms of multiple criteria, which can be used to inform discussions among stakeholders and may thus increase acceptance.

An Integral Approach to Sustainable Decision-Making within Maritime Spatial Planning—A DSC for the Planning of Anchorages on the Island of Šolta, Croatia

The planning of nautical tourism development and especially, the planning of its supporting infrastructure development, is important topic of the maritime spatial planning. The focus of research is the integration of multicriteria analysis and stakeholders within concept modeling that will provide support to the spatial planning specialists in the design of plans related to the development of anchorage capacities for small vessels. It examines economic, environmental, ecological, social, and civil engineering concerns related to the use of coastal water. It is a complex and ill-defined civil engineering problem because of multiple stakeholders with diverse interests, numerous conflicting goals and criteria, huge quantities of information and data, limited resources, etc. The research is concentrated on an integral approach to sustainable decision-making within maritime spatial planning by the modeling decision support concept to the processes of identification, validation, comparison, and the selection of locations for anchorage construction, based on multicriteria methods, goal analysis, and the logic of the decision support system. The concept is tested on the island of Šolta, Croatia, and has been proven as being an applicable, consistent, efficient, and effective methodology for the planning of the anchorage locations.

Optimizing the Location of Biomass Energy Facilities by Integrating Multi-Criteria Analysis (MCA) and Geographical Information Systems (GIS)

Internationally forest biomass is considered to be a valuable renewable energy feedstock. However, utilization of forest harvesting residues is challenging because they are highly varied, generally of low quality and usually widely distributed across timber harvesting sites. Factors related to the collection, processing and transport impose constraints on the economic viability of residue utilization operations and impact their supply from dispersed feedstock locations. To optimize decision-making about suitable locations for biomass energy plants intending to use forest residues, it is essential to factor in these supply chain considerations. This study conducted in Tasmania, Australia presents an investigation into the integration of Multi-criteria analysis (MCA) and Geographical Information systems (GIS) to identify optimal locations for prospective biomass power plants. The amount of forest harvesting biomass residues was estimated based on a non-industrial private native resource model in Tasmania (NIPNF). The integration of MCA and a GIS model, including a supply chain cost analysis, allowed the identification and analysis of optimal candidate locations that balanced economic, environmental, and social criteria within the biomass supply. The study results confirm that resource availability, land use and supply chain cost data can be integrated and mapped using GIS to facilitate the determination of different sustainable criteria weightings, and to ultimately generate optimal candidate locations for biomass energy plants. It is anticipated that this paper will make a contribution to current scientific knowledge by presenting innovative approaches for the sustainable utilization of forest harvest residues as a resource for the generation of bioenergy in Tasmania.