Intelligent Recommendation of Multi-Scale Response Strategies for Land Drought Events

Lei He, Yuheng Lei, Yizhuo Yang, Bin Liu, Yuxia Li, Youcai Zhao and Dan Tang ()
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Lei He: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Yuheng Lei: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Yizhuo Yang: School of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China
Bin Liu: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Yuxia Li: School of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China
Youcai Zhao: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Dan Tang: School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China

Land, 2024, vol. 14, issue 1, 1-24

Abstract: Currently, land drought events have become a frequent and serious global disaster. How to address these droughts has become a major issue for researchers. Traditional response strategies for land drought events have been determined by experts based on the severity levels of the events. However, these methods do not account for temporal variations or the specific risks of different areas. As a result, they overlooked the importance of spatio-temporal multi-scale strategies. This research proposes a multi-scale response strategy recommendation model for land drought events. The model integrates characteristics of drought-causing factors, disaster-prone environments, and hazard-bearing bodies using case-based reasoning (CBR). Additionally, the analytic hierarchy process (AHP) and entropy weighting methods (EWMs) are introduced to assign weights to the feature attributes. A case retrieval algorithm is developed based on the similarity of these attributes and the structural similarities of drought cases. The research further classifies emergency strategies into long-term and short-term approaches. Each approach has a corresponding correction algorithm. For short-term strategies, a correction algorithm based on differential evolutions is applied. For long-term strategies, a correction algorithm based on drought risk assessment is developed. The algorithm considers factors such as drought risk, vulnerability, and exposure. It facilitates multi-scale decision-making for drought events. The candidate case obtained using the correction algorithm shows an overall attribute similarity of 94.7% with the real case. The emergency response levels match between the two cases. However, the funding required in the candidate case is CNY 327 million less than the actual expenditure.

Keywords: land; drought events; response strategies; risk assessment (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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