Using an Evolutionary Algorithm in Multiobjective Geographic Analysis for Land Use Allocation and Decision Supporting

Summary, in English

Usually, allocation of resources is an optimization problem which involves a variety of conflicting economic, social, and ecological objectives. In such a process, advanced geographic analyst tool for manipulation of spatial data and satisfaction of multiple objectives is essential to the success of decision-making. The present research intends to demonstrate the application of a multiobjective optimization method based on NSGA-II (we call it HNSGA-II), along with Geographical Information System (GIS) to select suitable sites for the establishment of large industrial units. Having defined the elements of HNSGA-II for the site selection of industrial units, the method is tested on the data of Zanjan province, Iran, as the case study. The results showed that the proposed approach can easily find a variety of optimized solutions, giving the decision-makers the possibility to opt for the most propitious solution. Using this method, the achievement level regarding each objective function can be studied for any of the nondominated solutions.

• Författare: Zohreh Masoumi; Jamshid Maleki; Mohammad Sadi Mesgari; Ali Mansourian
• Avdelning/ar: Institutionen för naturgeografi och ekosystemvetenskap; BECC: Biodiversity and Ecosystem services in a Changing Climate
• Publiceringsår: 2017
• Språk: Engelska
• Sidor: 58-83
• Publikation/Tidskrift/Serie: Geographical Analysis
• Volym: 49
• Issue: 1
• Länkar: http://dx.doi.org/10.1111/gean.12111
• Dokumenttyp: Artikel i tidskrift
• Förlag: Wiley-Blackwell
• Ämne: Environmental Management; Earth and Related Environmental Sciences; Geosciences, Multidisciplinary; Other Earth and Related Environmental Sciences
• Nyckelord: multi-objective optimization; Geospatial Artificial Intelligence (GeoAI); Artificial Intelligence (AI); Land use allocation
• Status: Published
• ISBN/ISSN/Övrigt: ISSN: 0016-7363