Decision complexity and consensus in Web-based spatial decision making: A case study of site selection problem using GIS and multicriteria analysis

Cities ◽  
2015 ◽  
Vol 45 ◽  
pp. 60-70 ◽  
Author(s):  
Mohammadreza Jelokhani-Niaraki ◽  
Jacek Malczewski
Keyword(s):  
Decision Making ◽  
Site Selection ◽  
Multicriteria Analysis ◽  
Selection Problem ◽  
Web Based ◽  
Spatial Decision Making ◽  
Decision Complexity

scholarly journals Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)

2021 ◽  
Vol 10 (6) ◽  
pp. 403
Author(s):  
Jiamin Liu ◽  
Yueshi Li ◽  
Bin Xiao ◽  
Jizong Jiao
Keyword(s):  
Decision Making ◽  
Clustering Algorithm ◽  
Fuzzy Membership Function ◽  
Multi Criteria Decision Making ◽  
Landfill Site Selection ◽  
Spatial Distribution Characteristics ◽  
Criteria Weights ◽  
Spatial Decision Making ◽  
Complex Decision

The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods.

On Laplacian energy of picture fuzzy graphs in site selection problem

2021 ◽  
pp. 1-18
Author(s):  
Mahima Poonia ◽  
Rakesh Kumar Bajaj
Keyword(s):  
Decision Making ◽  
Site Selection ◽  
Upper Bounds ◽  
Selection Problem ◽  
Hydropower Plant ◽  
Fuzzy Graph ◽  
Fuzzy Information ◽  
Fuzzy Graphs ◽  
Laplacian Energy ◽  
Selection For

In the present work, the adjacency matrix, the energy and the Laplacian energy for a picture fuzzy graph/directed graph have been introduced along with their lower and the upper bounds. Further, in the selection problem of decision making, a methodology for the ranking of the available alternatives has been presented by utilizing the picture fuzzy graph and its energy/Laplacian energy. For the shake of demonstrating the implementation of the introduced methodology, the task of site selection for the hydropower plant has been carried out as an application. The originality of the introduced approach, comparative remarks, advantageous features and limitations have also been studied in contrast with intuitionistic fuzzy and Pythagorean fuzzy information.

scholarly journals Value-Based Decision-Making Using a Web-Based Tool: A Multiple Case Study

2017 ◽  
Author(s):  
Vitor Freitas ◽  
Mirko Perkusich ◽  
Emilia Mendes ◽  
Pilar Rodriguez ◽  
Markku Oivo
Keyword(s):  
Decision Making ◽  
Multiple Case Study ◽  
Web Based ◽  
Multiple Case

A multicriteria analysis method as decision-making tool for sustainable desalination: the Asinara Island case study

2017 ◽  
Vol 61 ◽  
pp. 274-283 ◽  
Author(s):  
M. Marini ◽  
C. Palomba ◽  
P. Rizzi ◽  
E. Casti ◽  
A. Marcia ◽  
...  
Keyword(s):  
Decision Making ◽  
Multicriteria Analysis ◽  
Analysis Method ◽  
Decision Making Tool

Temporary site selection and decision-making methods: a case study of Tehran, Iran

Disasters ◽  
2013 ◽  
Vol 37 (3) ◽  
pp. 536-553 ◽  
Author(s):  
Babak Omidvar ◽  
Mohammad Baradaran-Shoraka ◽  
Mehdi Nojavan
Keyword(s):  
Decision Making ◽  
Site Selection

Estimating complex ecological variables at high resolution in heterogeneous terrain using multivariate matching algorithms

2021 ◽  
Author(s):  
Rachel Renne ◽  
Daniel Schlaepfer ◽  
Kyle Palmquist ◽  
William Lauenroth ◽  
John Bradford
Keyword(s):  
Decision Making ◽  
Time Series ◽  
High Resolution ◽  
Simulation Model ◽  
Site Selection ◽  
Ecosystem Structure ◽  
Simulation Output ◽  
Representative Points ◽  
Matching Criteria

1. Simulation models are valuable tools for estimating ecosystem structure and function under various climatic and environmental conditions and disturbance regimes, and are particularly relevant for investigating the potential impacts of climate change on ecosystems. However, because computational requirements can restrict the number of feasible simulations, they are often run at coarse scales or for representative points. These results can be difficult to use in decision-making, particularly in topographically complex regions.2. We present methods for interpolating multivariate and time series simulation output to high resolution maps. First, we developed a method for applying k-means clustering to optimize selection of simulation sites to maximize the area represented for a given number of simulations. Then, we used multivariate matching to interpolate simulation results to high-resolution maps for the represented area. The methods rely on a user-defined set of matching variables that are assigned weights such that matched sites will be within a prescribed range for each variable. We demonstrate the methods with case studies using an individual-based plant simulation model to illustrate site selection and an ecosystem water balance simulation model for interpolation.3. For the site-selection case study, our approach optimized the location of 200 simulation sites and accurately represented 96% of a large study area (1.12 x 106 km2) at a 30-arcsecond resolution. For the interpolation case study, we generated high-resolution (30-arcsecond) maps across 4.38 x 106 km2 of drylands in western North America from simulated sites representing a 10 x 10 km grid. Our estimates of interpolation errors using leave-one-out cross validation were low (<10% of the range of each variable).4. Our point selection and interpolation methods provide a means of generating high-resolution maps of complex simulation output (e.g., multivariate and time-series) at scales relevant for local conservation planning and can help resolve the effects of topography that are lost in simulations at coarse scales or for representative points. These methods are flexible and allow the user to identify relevant matching criteria for an area of interest to balance quality of matching with areal coverage to enhance inference and decision-making in heterogenous terrain.

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