Elanur Adar-Yazar, Buket Karatop, Selim Gökcan Karatop
{"title":"Assessing the risk and effect of climate change with two-layer fuzzy logic-SWARA: A comparative practice in Turkiye","authors":"Elanur Adar-Yazar, Buket Karatop, Selim Gökcan Karatop","doi":"10.3233/jifs-236298","DOIUrl":null,"url":null,"abstract":"Many factors such as population growth, development of industry/technology, and increase in production-consumption disrupt the ecological balance and cause climate change, which is a global problem. Determining the criteria that cause climate change is very important in finding effective solutions to the problem. In the study, the criteria were determined, weighted with a new method, Step-wise Weight Assessment Ratio Analysis (SWARA), and ranked according to their priorities with two-layer fuzzy logic model. The Fuzzy SWARA method allows the evaluation process, which becomes complicated due to the difficulties and factors experienced in decision-making, to be carried out more effectively and realistically. The risk and effect of climate change in Turkiye were evaluated regionally. However, the developed model also has a wide application area. Research findings revealed that the highest risk/effect of climate change have the Marmara and Central Anatolia regions. The lowest risk region is the Eastern Anatolia. Air pollution, population growth and deforestation have the highest weights. Important suggestions have presented especially for priority criteria. In this way, the factors that should be prioritized in climate change environmental problem solutions have been revealed and will make it easier for researchers and managers to provide more effective management.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent & Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/jifs-236298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Many factors such as population growth, development of industry/technology, and increase in production-consumption disrupt the ecological balance and cause climate change, which is a global problem. Determining the criteria that cause climate change is very important in finding effective solutions to the problem. In the study, the criteria were determined, weighted with a new method, Step-wise Weight Assessment Ratio Analysis (SWARA), and ranked according to their priorities with two-layer fuzzy logic model. The Fuzzy SWARA method allows the evaluation process, which becomes complicated due to the difficulties and factors experienced in decision-making, to be carried out more effectively and realistically. The risk and effect of climate change in Turkiye were evaluated regionally. However, the developed model also has a wide application area. Research findings revealed that the highest risk/effect of climate change have the Marmara and Central Anatolia regions. The lowest risk region is the Eastern Anatolia. Air pollution, population growth and deforestation have the highest weights. Important suggestions have presented especially for priority criteria. In this way, the factors that should be prioritized in climate change environmental problem solutions have been revealed and will make it easier for researchers and managers to provide more effective management.
人口增长、工业/技术发展、生产-消费增长等诸多因素破坏了生态平衡,导致气候变化这一全球性问题。确定导致气候变化的标准对于找到有效的解决方法非常重要。本研究采用一种新方法--分步加权评估比率分析法(SWARA)确定了各项标准的权重,并利用双层模糊逻辑模型根据其优先级进行了排序。模糊 SWARA 法使因决策过程中遇到的困难和因素而变得复杂的评估过程得以更有效、更现实地进行。对土耳其气候变化的风险和影响进行了区域性评估。不过,所开发的模型也有广泛的应用领域。研究结果表明,马尔马拉和安纳托利亚中部地区的气候变化风险/影响最高。风险最低的地区是安纳托利亚东部。空气污染、人口增长和森林砍伐的权重最高。特别针对优先标准提出了重要建议。因此,气候变化环境问题解决方案中应优先考虑的因素已经显现出来,这将使研究人员和管理人员更容易提供更有效的管理。