Applications of artificial intelligence for disaster management

Sun, Wenjuan; Bocchini, Paolo; Davison, Brian D.

doi:10.1007/s11069-020-04124-3

Cited by 247 publications

(121 citation statements)

References 578 publications

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“…Artificial Intelligence (AI) technology is becoming popular in other fields, such as manufacturing, finance, and engineering, and the potential exists for AI optimization technology to be used to improve the understanding of hydrological risk, economic losses, and disaster management in SCP cities [79,80].…”

Section: Technology and Modelling Capabilitiesmentioning

confidence: 99%

Addressing Challenges of Urban Water Management in Chinese Sponge Cities via Nature-Based Solutions

Chan

Thorne

et al. 2020

Water

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Urban flooding has become a serious issue in most Chinese cities due to rapid urbanization and extreme weather, as evidenced by severe events in Beijing (2012), Ningbo (2013), Guangzhou (2015), Wuhan (2016), Shenzhen (2019), and Chongqing (2020). The Chinese “Sponge City Program” (SCP), initiated in 2013 and adopted by 30 pilot cities, is developing solutions to manage urban flood risk, purify stormwater, and provide water storage opportunities for future usage. Emerging challenges to the continued implementation of Sponge Cities include (1) uncertainty regarding future hydrological conditions related to climate change projections, which complicates urban planning and designing infrastructure that will be fit for purpose over its intended operating life, and (2) the competing priorities of stakeholders and their reluctance to make trade-offs, which obstruct future investment in the SCP. Nature-Based Solutions (NBS) is an umbrella concept that emerged from Europe, which encourages the holistic idea of considering wider options that combine “Blue–Green” practices with traditional engineering to deliver “integrated systems of Blue–Green–Grey infrastructure”. NBS includes interventions making use of natural processes and ecosystem services for functional purposes, and this could help to improve current pilot SCP practices. This manuscript reviews the development of the SCP, focusing on its construction and design aspects, and discusses how approaches using NBS could be included in the SCP to tackle not only urban water challenges but also a wide range of social and environmental challenges, including human health, pollution (via nutrients, metals, sediments, plastics, etc.), flood risk, and biodiversity.

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Section: Technology and Modelling Capabilitiesmentioning

confidence: 99%

Addressing Challenges of Urban Water Management in Chinese Sponge Cities via Nature-Based Solutions

Chan

Thorne

et al. 2020

Water

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“…Most data augmentation algorithms focus on image data classification problems, but there is a data augmentation technique, noise injection [67], that can be applied to non-image data. It is used in the following way (refer to Equations (7) and (8)).…”

Section: Noise Injectionmentioning

confidence: 99%

“…However, the previous works have drawbacks in two aspects: the regression model's generalization ability and the data scarcity. For the former, although various machine learning models are employed for natural disasters [7], they are almost all related to BPNN and SVR in the field of economic loss forecasting. Ensemble learning is a widely-used algorithm [13][14][15][16] that combines several machine learning techniques into an ensemble model to reduce deviation and improve prediction accuracy [17].…”

Section: Introductionmentioning

confidence: 99%

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A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters

Sun

Wang

2021

IJERPH

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The accurate prediction of storm surge disasters’ direct economic losses plays a positive role in providing critical support for disaster prevention decision-making and management. Previous researches on storm surge disaster loss assessment did not pay much attention to the overfitting phenomenon caused by the data scarcity and the excessive model complexity. To solve these problems, this paper puts forward a new evaluation system for forecasting the regional direct economic loss of storm surge disasters, consisting of three parts. First of all, a comprehensive assessment index system was established by considering the storm surge disasters’ formation mechanism and the corresponding risk management theory. Secondly, a novel data augmentation technique, k-nearest neighbor-Gaussian noise (KNN-GN), was presented to overcome data scarcity. Thirdly, an ensemble learning algorithm XGBoost as a regression model was utilized to optimize the results and produce the final forecasting results. To verify the best-combined model, KNN-GN-based XGBoost, we conducted cross-contrast experiments with several data augmentation techniques and some widely-used ensemble learning models. Meanwhile, the traditional prediction models are used as baselines to the optimized forecasting system. The experimental results show that the KNN-GN-based XGBoost model provides more precise predictions than the traditional models, with a 64.1% average improvement in the mean absolute percentage error (MAPE) measurement. It could be noted that the proposed evaluation system can be extended and applied to the geography-related field as well.

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“…Disaster robotics including unmanned ground vehicles and unmanned aerial vehicles are currently the most promising and safe methods for response and rescue operations. At the most basic level, robotics technology is used for mapping affected communities, firefighting, search and rescue (Sun, Bocchini & Davision 2020). Significant advances in robotics technology can be credited to the use of machine-learning techniques for acquiring new robotics skills and deep-learning tools for visual detection.…”

Section: Disaster Roboticsmentioning

confidence: 99%

Implications of artificial intelligence for bushfire management

Zarghami

Dumrak

2021

AJEM

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A country’s history and development can be shaped by its natural environment and the hazards it faces. As a response to the threat of novel and unexpected bushfire disasters, scholars and practitioners have turned to the area of artificial intelligence. This paper explores the underlying principles of artificial intelligence tools and to investigate how these tools have been used to mitigate the risks of catastrophic bushfires. In doing so, this research provides an overview of applications of artificial intelligence tools to enhance effective management of bushfires through preparedness capability, responding capability and recovery capability. The future evolution of tools in artificial intelligence is discussed in the bushfire management context based on emerging trends.

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Applications of artificial intelligence for disaster management

Cited by 247 publications

References 578 publications

Addressing Challenges of Urban Water Management in Chinese Sponge Cities via Nature-Based Solutions

Addressing Challenges of Urban Water Management in Chinese Sponge Cities via Nature-Based Solutions

A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters

Implications of artificial intelligence for bushfire management

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