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.

scholarly journals Siting MSW landfills via the integration of DEMATEL-ANP and clustering algorithm in a fuzzy logic environment (Case Study: Lanzhou, China)

2021 ◽  
Author(s):  
Jiamin Liu ◽  
Yueshi Li ◽  
Bin Xiao ◽  
Jizong Jiao
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Decision Makers ◽  
Spatial Distribution Characteristics ◽  
Criteria Weights ◽  
Spatial Decision Making ◽  
Complex Decision ◽  
Selection Of

Abstract The siting of Municipal Solid Waste (MSW) landfills is a complex decision based process that involves multiple hydrogeological, morphological, environmental, climatic, and socio-economic criteria. In a fuzzy logic environment, DEMATEL and ANP methods were employed to comprehensively consider uncertainty, fuzziness of data and the subjective scoring and stability of results to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30m resolution, criteria weights were determined via the integration of DEMATEL and ANP, and seven sets of membership functions were simulated to obtain the best fuzzy logic environment. Combining GIS spatial analysis and the three clustering algorithms (DBSCAN, HDBSCAN, and OPTICS), candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics and reachability 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 paper focuses on a flexible and novel framework for the selection of MSW landfill sites for Lanzhou, which is a semi-arid valley basin city in China. In contrast to common techniques, this model not only made the best recommendation scientifically and efficiently but could also provide accurate assessment data for decision makers in landfill construction and high-quality urban development.

Landfill site selection using GIS and multi-criteria decision-making. Case study of a region in Morocco

2023 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Omar Ouhsine ◽  
Mohamed Berkani ◽  
Reda Elkacmi ◽  
Abdellah OUIGMANE ◽  
Aziz Hasib ◽  
...  
Keyword(s):  
Decision Making ◽  
Site Selection ◽  
Landfill Site ◽  
Multi Criteria Decision Making ◽  
Landfill Site Selection

Multi-Criteria Decision Making for Vulnerability Mapping of Flood Hazard: A Case Study of Pune City

2019 ◽  
Vol 2 (1) ◽  
pp. 41-52
Author(s):  
Nitin Mundhe
Keyword(s):  
Decision Making ◽  
Flood Hazard ◽  
Geographical Information ◽  
Multi Criteria Decision Making ◽  
Flood Hazards ◽  
Very High Frequency ◽  
Flood Vulnerability ◽  
Natural Risk ◽  
The Town

Floods are natural risk with a very high frequency, which causes to environmental, social, economic and human losses. The floods in the town happen mainly due to human made activities about the blockage of natural drainage, haphazard construction of roads, building, and high rainfall intensity. Detailed maps showing flood vulnerability areas are helpful in management of flood hazards. Therefore, present research focused on identifying flood vulnerability zones in the Pune City using multi-criteria decision-making approach in Geographical Information System (GIS) and inputs from remotely sensed imageries. Other input data considered for preparing base maps are census details, City maps, and fieldworks. The Pune City classified in to four flood vulnerability classes essential for flood risk management. About 5 per cent area shows high vulnerability for floods in localities namely Wakdewadi, some part of the Shivajinagar, Sangamwadi, Aundh, and Baner with high risk.

scholarly journals STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

2021 ◽  
Vol 1 ◽  
pp. 2701-2710
Author(s):  
Julie Krogh Agergaard ◽  
Kristoffer Vandrup Sigsgaard ◽  
Niels Henrik Mortensen ◽  
Jingrui Ge ◽  
Kasper Barslund Hansen ◽  
...  
Keyword(s):  
Decision Making ◽  
Tacit Knowledge ◽  
True Variance ◽  
Complex Decision ◽  
Maintenance Decision ◽  
Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

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