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Electronics
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AI in Disaster, Crisis, and Emergency Management
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AI in Disaster, Crisis, and Emergency Management

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 14584

Special Issue Editors

Dr. Faheim Sufi

E-Mail Website
Guest Editor
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
Interests: data mining; artificial intelligence; big data analysis; AI; machine learning; decision support system; natural language processing; sentiment analysis; named entity detection; mobile app; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Alam Edris

E-Mail Website
Guest Editor
Rabdan Academy, Abu Dhabi, United Arab Emirates
Interests: natural hazards; disasters; crisis; safety; security; emergency management; climate change adaptation; business continuity management and sustainable development

Special Issue Information

Dear Colleagues,

Recent years have witnessed a significant increase in disasters from a variety of sources, including meteorological and hydrological, geological, extraterrestrial, environmental, biological, chemical, technological and societal hazards, which have caused enormous damage to human lives, infrastructure and the economy. Specific examples of these hazards include but are not limited to earthquakes, hurricanes, droughts, riverine floods, heatwaves, biodiversity loss, wildfires, bacteria, viruses, parasites, venomous animals and mosquitoes carrying disease-causing agents, infectious disease, corrosive, flammable and toxic chemicals, cyber-attacks and violence and conflict. In such scenarios, emergency management plays a critical role in reducing the impact of disasters and saving lives. With the advancements in artificial intelligence (AI) technologies, there is a growing interest in leveraging AI for emergency management to improve the efficiency, effectiveness and accuracy in disaster mitigation, preparedness, emergency response and recovery operations. This Special Issue aims to bring together cutting-edge research on the application of AI in emergency management.

This Special Issue welcomes original research papers and review articles on, but not limited to, the following topics:

  • AI-powered safety, security, disaster, crisis and emergency events analysis, including historical and big data analysis;
  • AI-enabled climate extreme events, disaster risk assessment and early warning systems;
  • AI-enabled public health emergency management including COVID-19;
  • AI-based emergency response planning and decision making;
  • AI-assisted real-time situational awareness and monitoring;
  • AI-driven resource allocation and optimization in emergency management;
  • AI-powered prediction and mitigation of disasters and their impact;
  • AI-supported public communication and social media analysis during emergencies;
  • AI-augmented human-robot collaboration in emergency response;
  • AI-powered autonomous systems for search and rescue operations;
  • AI-enhanced post-disaster assessment and recovery planning;
  • AI-enabled disaster and business continuity management including education continuity operations;
  • Ethical and social implications of AI in emergency management.

Dr. Sufi Faheim
Dr. Alam Edris
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI
  • emergency management
  • disaster risk assessment
  • early warning systems
  • emergency response planning
  • situational awareness
  • resource allocation
  • prediction
  • search and rescue
  • recovery planning
  • ethics
  • social implications
  • human-robot collaboration
  • public communication
  • social media analysis
  • cyber incident emergency
  • pandemic emergency
  • COVID-19
  • civil War
  • refugee crisis
  • earthquake
  • flood
  • landslides
  • bushfires
  • tsunami
  • tornado
  • cyclone emergency management
  • other emergency managements

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

18 pages, 3026 KiB  
Article
Assessing Urban Mobility Resilience: An Exploratory Approach Using Hazard-Based Duration Models
by Luís A. P. Jardim Gonçalves, Sara Ferreira and Paulo J. G. Ribeiro
Electronics 2024, 13(21), 4220; https://doi.org/10.3390/electronics13214220 - 28 Oct 2024
Viewed by 672
Abstract
Urban systems are vulnerable to disturbances from both natural and human origins, which can disrupt their normal functioning. Evaluating the resilience of these systems, particularly the main transportation networks and their usage levels, is crucial and innovative for understanding the impacts of such [...] Read more.
Urban systems are vulnerable to disturbances from both natural and human origins, which can disrupt their normal functioning. Evaluating the resilience of these systems, particularly the main transportation networks and their usage levels, is crucial and innovative for understanding the impacts of such disturbances. Thus, this work aims to assess resilience in urban mobility through the probability of a particular journey using a specific mode: “surviving” through critical travel time. To achieve this, a methodology was developed based on the Weibull model with gamma heterogeneity (hazard-based duration models), which was applied to a medium-sized Portuguese municipality. Eighteen groups representing active populations were set and compared. The results indicated that using the bus and cycling are the most resilient modes of transport, whereas walking is the least resilient. Additionally, a specific group was identified as having lower mobility resilience, making them more vulnerable to disruptions in the transport system. Finally, the findings of this study demonstrate the practical application of this methodology, which relies on travel time to assess resilience and, thus, guide political attention and actions to the less resilient mode. Future research should aim to develop a comprehensive framework that incorporates several variables to fully describe the complex nature of transport systems and their resilience. Full article
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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16 pages, 1680 KiB  
Article
Short-Term Flood Prediction Model Based on Pre-Training Enhancement
by Yang Xia and Jiamin Lu
Electronics 2024, 13(11), 2203; https://doi.org/10.3390/electronics13112203 - 5 Jun 2024
Cited by 1 | Viewed by 933
Abstract
With the rapid advancement of deep learning techniques, deep learning-based flood prediction models have drawn significant attention. However, for short-term prediction in small- and medium-sized river basins, models typically rely on hydrological data spanning from the past several hours to one day, utilizing [...] Read more.
With the rapid advancement of deep learning techniques, deep learning-based flood prediction models have drawn significant attention. However, for short-term prediction in small- and medium-sized river basins, models typically rely on hydrological data spanning from the past several hours to one day, utilizing a fixed-length input window. Such input limits the models’ adaptability to diverse rainfall events and restricts their capability to comprehend historical temporal patterns. To address the underutilization of historical information by existing models, we introduce a Pre-training Enhanced Short-term Flood Prediction Method (PE-SFPM) to enrich the model’s temporal understanding without necessitating changes to the input window size. In the pre-training stage, the model uses a random masking and prediction strategy to learn segment features, capturing a more comprehensive evolutionary trend of historical floods. In the flow forecasting stage, temporal features and spatial features are captured and fused using the temporal attention, spatial attention, and gated fusion. Features are further enhanced by integrating segment features using a feed-forward network. Experimental results demonstrate that the proposed PE-SFPM model achieves excellent performance in short-term flood prediction tasks. Full article
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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26 pages, 22022 KiB  
Article
Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization
by Shun Ye, Yu Liu, Kai Xie, Chang Wen, Hong-Ling Tian, Jian-Biao He and Wei Zhang
Electronics 2024, 13(7), 1271; https://doi.org/10.3390/electronics13071271 - 29 Mar 2024
Cited by 1 | Viewed by 1233
Abstract
The precise extraction of displacement time series for complex landslides poses significant challenges, and conventional landslide prediction models often overlook the deformation impacts of displacement triggers. To address this, we introduce a novel composite model tailored for predicting landslide displacement. This model employs [...] Read more.
The precise extraction of displacement time series for complex landslides poses significant challenges, and conventional landslide prediction models often overlook the deformation impacts of displacement triggers. To address this, we introduce a novel composite model tailored for predicting landslide displacement. This model employs Variational Mode Decomposition (VMD) to isolate each displacement component, with optimization achieved through the groupwise coupling algorithm. Subsequently, Grey correlation analysis (GRA) is applied to quantitatively assess the dynamic correlations between various triggering factors and landslide displacement. This analysis informs the construction of a feature set predicated on these correlation factors. Integrating the time-series VMD module into the standard Transformer architecture facilitates the prediction of landslide displacement. This integration allows for the extraction of critical time-evolution features associated with the displacement components. Ultimately, the predicted displacements are aggregated and reconstructed. We validate our model using the Bazimen landslide case study, analyzing displacement monitoring data from 1 January 2007, to 31 December 2012. The values of the root mean square error and the mean absolute percentage error were 1.86 and 4.85, respectively. This model offers a more nuanced understanding of the multifaceted causes and evolutionary dynamics underpinning landslide displacement and deformation, thereby markedly enhancing prediction accuracy. Full article
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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18 pages, 8189 KiB  
Article
Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images
by Sugi Choi, Bohee Lee, Junkyeong Kim and Haiyoung Jung
Electronics 2024, 13(1), 229; https://doi.org/10.3390/electronics13010229 - 4 Jan 2024
Cited by 3 | Viewed by 2389
Abstract
The accurate detection of P-wave FAP (First-Arrival Picking) in seismic signals is crucial across various industrial domains, including coal and oil exploration, tunnel construction, hydraulic fracturing, and earthquake early warning systems. At present, P-wave FAP detection relies on manual identification by experts and [...] Read more.
The accurate detection of P-wave FAP (First-Arrival Picking) in seismic signals is crucial across various industrial domains, including coal and oil exploration, tunnel construction, hydraulic fracturing, and earthquake early warning systems. At present, P-wave FAP detection relies on manual identification by experts and automated methods using Short-Term Average to Long-Term Average algorithms. However, these approaches encounter significant performance challenges, especially in the presence of real-time background noise. To overcome this limitation, this study proposes a novel P-wave FAP detection method that employs the U-Net model and incorporates spectrogram transformation techniques for seismic signals. Seismic signals, similar to those encountered in South Korea, were generated using the stochastic model simulation program. Synthesized WGN (White Gaussian Noise) was added to replicate background noise. The resulting signals were transformed into 2D spectrogram images and used as input data for the U-Net model, ensuring precise P-wave FAP detection. In the experimental result, it demonstrated strong performance metrics, achieving an MSE of 0.0031 and an MAE of 0.0177, and an RMSE of 0.0195. Additionally, it exhibited precise FAP detection capabilities in image prediction. The developed U-Net-based model exhibited exceptional performance in accurately detecting P-wave FAP in seismic signals with varying amplitudes. Through the developed model, we aim to contribute to the advancement of microseismic monitoring technology used in various industrial fields. Full article
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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18 pages, 1030 KiB  
Article
A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection
by Qiyi He, Han Wang, Changyi Li, Wen Zhou, Zhiwei Ye, Liang Hong, Xinguo Yu, Shengjie Yu and Lu Peng
Electronics 2023, 12(23), 4847; https://doi.org/10.3390/electronics12234847 - 30 Nov 2023
Cited by 1 | Viewed by 1167
Abstract
Anomaly in geoacoustic emission is an important earthquake precursor. Current geoacoustic anomaly detection methods are limited by their low signal-to-noise ratio, low intensity, sample imbalance, and low accuracy. Therefore, this paper proposes a clone selection algorithm optimized one-class support vector machine method (CSA-OCSVM) [...] Read more.
Anomaly in geoacoustic emission is an important earthquake precursor. Current geoacoustic anomaly detection methods are limited by their low signal-to-noise ratio, low intensity, sample imbalance, and low accuracy. Therefore, this paper proposes a clone selection algorithm optimized one-class support vector machine method (CSA-OCSVM) for geoacoustic anomaly detection. First, the interquartile range (IQR), cubic spline interpolation, and time window are designed to amplify the geoacoustic signal intensity and energy change rules to reduce the interference of geoacoustic signal noise and intensity. Secondly, to address the imbalance of positive and negative samples in geoacoustic anomaly detection, a one-class support vector machine is introduced for anomaly detection. Meanwhile, in view of the optimization capabilities of the clone selection algorithm, it is adopted to optimize the hyperparameters of OCSVM to improve its detection accuracy. Finally, the proposed model is applied to geoacoustic data anomaly detection in nine different datasets, which are derived from our self-developed acoustic electromagnetic to AI (AETA) system, to verify its effectiveness. By designing comparative experiments with IQR, genetic algorithm OCSVM (GA-OCSVM), particle swarm optimization OCSVM (PSO-OCSVM), and evaluating the performance of the true positive rate (TPR) and false positive rate (FPR), the experimental results depict that the proposed model is superior to the existing state-of-the-art geoacoustic anomaly detection approaches. Full article
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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21 pages, 8753 KiB  
Article
Swarm Intelligence Response Methods Based on Urban Crime Event Prediction
by Changhao Wang, Feng Tian and Yan Pan
Electronics 2023, 12(22), 4610; https://doi.org/10.3390/electronics12224610 - 11 Nov 2023
Viewed by 1369
Abstract
Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. [...] Read more.
Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime prediction based on spatiotemporal data, along with the implementation of multiple unmanned drone patrols and responses, can effectively reduce a city’s crime rate. This paper utilizes machine learning and data mining techniques, predicts crime incidents in small geographic areas with short timeframes, and proposes a random forest algorithm based on oversampling, which outperforms other prediction algorithms in terms of performance. The research results indicate that the random forest algorithm based on oversampling can effectively predict crimes with an accuracy rate of up to 95%, and an AUC value close to 0.99. Based on the crime prediction results, this paper proposes a multi-drone patrol response strategy to patrol and respond to predicted high-crime areas, which is based on target clustering and combined genetic algorithms. This strategy may help with the pre-warning patrol planning within an hourly range. This paper aims to combine crime event predictions with crowd-sourced cruise responses to proactively identify potential crimes, providing an effective solution to reduce urban crime rates. Full article
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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25 pages, 12310 KiB  
Article
Novel Application of Open-Source Cyber Intelligence
by Fahim Sufi
Electronics 2023, 12(17), 3610; https://doi.org/10.3390/electronics12173610 - 26 Aug 2023
Cited by 5 | Viewed by 2947
Abstract
The prevalence of cybercrime has emerged as a critical issue in contemporary society because of its far-reaching financial, social, and psychological implications. The negative effects of cyber-attacks extend beyond financial losses and disrupt people’s lives on social and psychological levels. Conventional practice involves [...] Read more.
The prevalence of cybercrime has emerged as a critical issue in contemporary society because of its far-reaching financial, social, and psychological implications. The negative effects of cyber-attacks extend beyond financial losses and disrupt people’s lives on social and psychological levels. Conventional practice involves cyber experts sourcing data from various outlets and applying personal discernment and rational inference to manually formulate cyber intelligence specific to a country. This traditional approach introduces personal bias towards the country-level cyber reports. However, this paper reports a novel approach where country-level cyber intelligence is automatically generated with artificial intelligence (AI), employing cyber-related social media posts and open-source cyber-attack statistics. Our innovative cyber threat intelligence solution examined 37,386 tweets from 30,706 users in 54 languages using sentiment analysis, translation, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), N-gram, and Porter stemming. Moreover, the presented study utilized 238,220 open-intelligence cyber-attack statistics from eight different web links, to create a historical cyber-attack dataset. Subsequently, AI-based algorithms, like convolutional neural network (CNN), and exponential smoothing were used for AI-driven insights. With the confluence of the voluminous Twitter-derived data and the array of open-intelligence cyber-attack statistics, orchestrated by the AI-driven algorithms, the presented approach generated seven-dimensional cyber intelligence for Australia and China in complete automation. Finally, the topic analysis on the cyber-related social media messages revealed seven main themes for both Australia and China. This methodology possesses the inherent capability to effortlessly engender cyber intelligence for any country, employing an autonomous modality within the realm of pervasive computational platforms. Full article
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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