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A comprehensive literature review of the demand forecasting methods of emergency resources from the perspective of artificial intelligence

机译：从人工智能视角下全面综述应急资源需求预测方法

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摘要

In recent decades, several forecasting methods have been proposed so as to aid in selecting from all optimal alternatives in the demand of emergency resources. Academic research in the field of emergency management has increasingly focused on artificial intelligence. However, more attention has been paid to attempts at simulating the human brain, with little focus on addressing intelligent information processing techniques based on machine learning, big data and smart devices. In this paper, a comprehensive literature review is presented in order to classify and interpret current research on demand forecasting methodologies and applications. A total of 1235 academic papers from 1980 to 2018 in the SpringerLink and Elsevier ScienceDirect databases are categorized as follows: time series analysis, case-based reasoning (CBR), mathematical models, information technology, literature reviews, and discussion and analysis. Application areas from business source premier include papers on the topics of emergency management, decision-making, decision relief, logistics, fuzzy sets and other topics. Academic publications are classified by (1) year of publication, (2) journal of publication, (3) database source, (4) methodology and (5) research discipline. The results of this literature review show that, despite forecasting methods such as ARIMA, CBR and mathematical models appearing to play a pivotal role in promoting prediction performance, there is a need to explore more real-time forecasting approaches based on intelligent information processing techniques so as to achieve appropriate dynamic demand prediction that is adaptable to emergency and rescue situations. The intention for this paper is to be a useful reference point for those with research needs in forecasting methodologies and the applications of emergency resources.
机译：近几十年来，提出了几种预测方法，以帮助选择应急资源需求的所有最佳替代品。紧急管理领域的学术研究越来越关注人工智能。但是，在模拟人类大脑时，已经需要更多的注意力，几乎没有专注于解决基于机器学习，大数据和智能设备的智能信息处理技术。本文提出了一个综合文献综述，以便分类和解释当前的需求预测方法和应用研究。共有1235篇学术论文从1980年到2018年在SpringerLink和Elsevier Sciencedirect数据库中分类如下：时间序列分析，基于案例的推理（CBR），数学模型，信息技术，文学评论和讨论和分析。来自商业来源总理的应用领域包括关于应急管理，决策，决策救济，物流，模糊集等主题的主题的论文。学术出版物被（1）出版年份，（2）出版杂志，（3）数据库源，（4）方法和（5）研究纪律。该文献综述结果表明，尽管在促进预测性能方面出现了诸如Arima，CBR和数学模型等预测方法，但需要基于智能信息处理技术探索更多的实时预测方法为了实现适应紧急和救援情况的适当动态需求预测。本文的意图是对预测方法和应急资源应用有研究需求的有用参考点。

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