Title: Research on the matching of environmental emergency prediction and emergency rescue resources based on deep learning

Authors: Xifei Huang; Xiaowu Huang

Addresses: Shenzhen Green Century Environmental Technology Co., Ltd., Shenzhen 518000, Guangdong, China ' Shenzhen Green Century Environmental Technology Co., Ltd., Shenzhen 518000, Guangdong, China

Abstract: Most of the current environmental safety emergency predictions rely on experienced experts or staff, and are highly subjective. Therefore, this paper proposes a deep learning-based regional risk assessment method. Based on domestic and foreign research results, this paper adopts comparative analysis, neural network, storage theory in operations research and multi-objective programming as research methods, and establishes a research model for regional hazard prediction and emergency rescue resource matching based on deep learning. By comparing the advantages and disadvantages of several methods, this paper studies the matching of regional hazard control and emergency rescue resources. The results show that after using the research model in this paper, the overall research efficiency is increased by 20%, and compared with the previous research model, the overall efficiency is higher and has certain practical value.

Keywords: regional risk estimation; neural network method; storage theory; multi-objective programming method; emergency rescue resource matching.

DOI: 10.1504/IJAHUC.2023.131362

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.43 No.2, pp.116 - 125

Received: 27 Jul 2022
Accepted: 13 Oct 2022
Published online: 07 Jun 2023 *

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