GIS-Based Multi-Criteria Evaluation (MCE) Methods for Aquaculture Site Selection: A Systematic Review and Meta-Analysis

With the growing demand for aquatic products, aquaculture has become a prominent means of meeting this demand. However, the selection of suitable sites for aquaculture remains a key factor in the success of any aquaculture operation. While various methods exist for site selection, geographic information system (GIS)-based multi-criteria evaluation (MCE) methods have emerged as the most widely utilized approach to identifying potential aquaculture sites. Following the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA), this paper presents a systematic review and meta-analysis of GIS-based MCE methods used in aquaculture sites selection. The objective of this study is to offer a comprehensive overview of existing research in this field and develop a general model for selecting sites for fish and shellfish aquaculture. The main findings indicate a growing number of studies utilizing GIS-based MCE in aquaculture site selection in recent years, with Asia being the leading continent in terms of publications in this domain. Among the journals publishing in this field, the Aquaculture journal stands out as the top publisher. Using consistent criteria across the reviewed studies, two models have been generated, each consisting of four sub-models: water quality, soil quality, infrastructure, and socioeconomic factors; and topography, environment, and physical parameters. These models can aid future researchers and assist decision-makers in identifying optimal locations for aquaculture development.

Automated summary

This paper presents a systematic review and meta-analysis of GIS-based multi-criteria evaluation (MCE) methods used in aquaculture site selection. The main findings indicate a growing number of studies utilizing GIS-based MCE in aquaculture site selection in recent years, with Asia being the leading continent in terms of publications in this domain. Two models have been generated, each consisting of four sub-models, which can aid future researchers and assist decision-makers in identifying optimal locations for aquaculture development.