干旱区生态环境知识资源中心(Arid): Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

Arid

DOI	10.1016/j.ecolind.2023.110457
	Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment
	Nakhaei, Mahdi; Nakhaei, Pouria; Gheibi, Mohammad; Chahkandi, Benyamin; Waclawek, Stanislaw; Behzadian, Kourosh; Chen, Albert S.; Campos, Luiza C.
通讯作者	Campos, LC
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2023
卷号	153
英文摘要	This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions.
英文关键词	Artificial intelligence Classic Delphi method Flash flood Risk assessment Watershed modelling
类型	Article
语种	英语
开放获取类型	Green Accepted, Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001024650200001
WOS关键词	MEMETIC ALGORITHM ; MANAGEMENT ; SYSTEM ; QUALITY
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395940
推荐引用方式 GB/T 7714	Nakhaei, Mahdi,Nakhaei, Pouria,Gheibi, Mohammad,et al. Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment[J],2023,153.
APA	Nakhaei, Mahdi.,Nakhaei, Pouria.,Gheibi, Mohammad.,Chahkandi, Benyamin.,Waclawek, Stanislaw.,...&Campos, Luiza C..(2023).Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment.ECOLOGICAL INDICATORS,153.
MLA	Nakhaei, Mahdi,et al."Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment".ECOLOGICAL INDICATORS 153(2023).