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

Zhu, Xiaoxin; Zhang, Guanghai; Sun, Baiqing

doi:10.1007/s11069-019-03626-z

Cited by 59 publications

(31 citation statements)

References 29 publications

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“…The analysis revealed that it is essential to explore the request features of emergency supplies based on the concrete response situation, consistent with the findings of previous studies that emphasize the importance of requirement forecasting for response efficiency [ 7 , 28 ]. The potential benefits of being aware of the request ranking of emergency supplies are that it may aid decision makers and the early preparations for inventory management and material collection.…”

Section: Discussionsupporting

confidence: 85%

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Zhu

Wang

Regan

et al. 2020

IJERPH

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Awareness of the requested quantity and characteristics of emergency supplies is crucial for facilitating an efficient relief operation. With the aim of focusing on the quantitative study of immediate food supplies, this article estimates the numerical autoregressive integrative moving average (ARIMA) model based on the actual data of 14 key commodities in the Sendai City of Japan during the 2011 Tohoku earthquake. Although the temporal patterns of key food commodity groups are qualitatively similar, the results show that they follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. A key finding is that 3 of the 14 items are significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements so as to promptly obtain them from distributors.

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Section: Discussionsupporting

confidence: 85%

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Zhu

Wang

Regan

et al. 2020

IJERPH

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“…The literature of DRPTN devotes to developing decision-making models in which road's components have to be prioritized for reconstruction such that it optimizes predefined objectives (e.g., Orabi et al 2009;Lertworawanich 2012;Kaviani et al 2018;Shiraki et al 2017). As a tool, optimization modeling is embedded in decision support systems that formulates and solves problems involving (often) multiple conflicting objectives (e.g., Zhu et al 2019;Xu et al 2019;Xing 2017). Particularly, in the setting of the transportation network, as a highly critical and intricately interwoven infrastructure, optimized decisions are products of optimizationbased decision modeling that recommend solutions responding to the post-disaster failures of a network's elements (Karlaftis et al 2007;Zamanifar and Seyedhoseyni 2017).…”

Section: Exhibits Structural Damages On the Transportation Network's mentioning

confidence: 99%

Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks

Zamanifar

Hartmann

2020

Nat Hazards

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The purpose of this study is to analyze optimization-based decision-making models for the problem of Disaster Recovery Planning of Transportation Networks (DRPTN). In the past three decades, seminal optimization problems have been structured and solved for the critical and sensitive problem of DRPTN. The extent of our knowledge on the practicality of the methods and performance of results is however limited. To evaluate the applicability of those context-sensitive models in real-world situations, there is a need to examine the conceptual and technical structure behind the existing body of work. To this end, this paper performs a systematic search targeting DRPTN publications. Thereafter, we review the identified literature based on the four phases of the optimization-based decision-making modeling process as problem definition, problem formulation, problem-solving, and model validation. Then, through content analysis and descriptive statistics, we investigate the methodology of studies within each of these phases. Eventually, we detect and discuss four research improvement areas as [1] developing conceptual or systematic decision support in the selection of decision attributes and problem structuring, [2] integrating recovery problems with traffic management models, [3] avoiding uncertainty due to the type of solving algorithms, and [4] reducing subjectivity in the validation process of disaster recovery models. Finally, we provide suggestions as well as possible directions for future research.

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“…For increasing the efficiency (and reducing costs) in hazard risk management, supporting response decisions with computational damage assessment methods is indispensable (Lee, Hancock, & Hu, 2014). Prediction accuracy with limited information, and prediction efficiency with limited time are the main hurdles in developing effective and practical post‐event damage assessment methods (Zhu, Zhang, & Sun, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, no prior study in earthquake engineering applications of RNN (or LSTM) attempted to use it at a regional scale, which arguably would be another impactful application. Rapid post‐event seismic damage assessment still faces numerous obstacles, including: Individual damage prediction generally calls for detailed structural information, yet, at present (and for the foreseeable near future) such level of detail is difficult to achieve at a regional scale (Zhu et al., 2019). Decision‐makers are interested in the overall seismic damage of regions in times of emergency rather than only a few, albeit important, buildings, for which rapid damage assessment based on direct sensor data and structural health monitoring algorithms are actually rational choice (see, e.g., Azimi & Pekcan, 2020; Ghahari, Abazarsa, Ghannad, & Taciroglu, 2013; Omrani, Hudson, & Taciroglu, 2012, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Real‐time regional seismic damage assessment framework based on long short‐term memory neural network

Çetiner

et al. 2020

Computer aided Civil Eng

104

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Effective post‐earthquake response requires a prompt and accurate assessment of earthquake‐induced damage. However, existing damage assessment methods cannot simultaneously meet these requirements. This study proposes a framework for real‐time regional seismic damage assessment that is based on a Long Short‐Term Memory (LSTM) neural network architecture. The proposed framework is not specially designed for individual structural types, but offers rapid estimates at regional scale. The framework is built around a workflow that establishes high‐performance mapping rules between ground motions and structural damage via region‐specific models. This workflow comprises three main parts—namely, region‐specific database generation, LSTM model training and verification, and model utilization for damage prediction. The influence of various LSTM architectures, hyperparameter selection, and dataset resampling procedures are systematically analyzed. As a testbed for the established framework, a case study is performed on the Tsinghua University campus buildings. The results demonstrate that the developed LSTM framework can perform damage assessment in real time at regional scale with high prediction accuracy and acceptable variance.

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

Cited by 59 publications

References 29 publications

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Optimization-based decision-making models for disaster recovery and reconstruction planning of transportation networks

Real‐time regional seismic damage assessment framework based on long short‐term memory neural network

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