Research on Collaborative and Optimal Deployment and Decision Making Among Major Geological Disaster Rescue Subjects

• Published in: Geotechnical and geological engineering Vol. 40; no. 1; pp. 57 - 71
• Main Authors: La, Ruifang, Lv, Tao, Bai, Pengfei, Zhang, Zaixu
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.01.2022; Springer Nature B.V
• Subjects: Algorithms; Civil Engineering; Collaboration; Decision making; Demand; Deployment; Disasters; Earth and Environmental Science; Earth Sciences; Empirical analysis; Geology; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Landslides; Mudflows; Original Paper; Prediction models; Rescue operations; Technicians; Terrestrial Pollution; Waste Management/Waste Technology; Weirs; China; Synergistic relationship; Dispatching optimization; Demand prediction; Rescue subject; Geological disaster
• DOI: 10.1007/s10706-021-01883-z
• ISSN: 0960-3182

Geological disaster rescue presents features such as multi-stage, multi-sector, multi-subject, and multi-target. However, due to the shortage of professional rescue technicians, it is necessary to consider the matching, fairness, and efficiency of rescue tasks and rescue subjects. This study takes "disaster scenario-rescue task-rescue subject-demand prediction-subject deployment" as the main line in major geological disasters such as landslides and mudslides. By collecting data on 87 rescue operations in China from 2004 to 2019, we screened 18 representative cases of rescue operations and sorted out different types of rescue force function positioning, divisions of responsibility, and collaboration modes, summarizing them into five major rescue tasks and five major rescue subjects. A rescue subject demand prediction model was developed based on different scenarios according to the influence of rescue tasks on the demand of rescue subjects. Considering the urgency of the rescue tasks, a rescue subject deployment model based on different tasks and the NSGA-II algorithm can be built up. We tested the Zhejiang Yongjia weir breach rescue operation case (2019) in China for empirical analysis to verify the effectiveness and practicality of the model and algorithm.