Machine Learning for Emergency Management: A Survey and Future Outlook

Kyrkou, Christos; Kolios, Panayiotis; Theocharides, Theocharis; Polycarpou, Marios M.

doi:10.1109/jproc.2022.3223186

Cited by 10 publications

(3 citation statements)

References 180 publications

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“…The weighted adaptive focus loss function formulated in this paper is depicted in Equation (8). As a function reliant on the output value of the ability evaluation model, it necessitates the application of the neural network's forward propagation to ascertain the loss function's value.…”

Section: Forward Calculationmentioning

confidence: 99%

“…Sugumaran et al (2017) proposed a model to assess emergency organization capabilities, offering insights for varying local emergency management entities [7]. In a similar vein, Kyrkou et al (2023) con-ducted a comprehensive survey on the application of machine learning algorithms in emergency management, identifying promising avenues for leveraging these technologies in various phases of disaster management [8].…”

Section: Introductionmentioning

confidence: 99%

“…𝐶 Number of neurons in layer lChange the complexity of the model[4,8].𝑔The activation function of the lth layer Learning non-linear relationships {Relu, Tanh, Sogmoid, Soft-max}…”

mentioning

confidence: 99%

See 2 more Smart Citations

Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

Tang,

Saga,

Dai

et al. 2023

Applied Sciences

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With the rise in meteorological disasters, improving evaluation strategies for disaster response agencies is critical. This shift from expert scoring to data-driven approaches is challenged by sample imbalance in the data, affecting accurate capability assessments. This study proposes a solution integrating adaptive focal loss into the cross-entropy loss function to address sample distribution imbalances, facilitating nuanced evaluations. A key aspect of this solution is the Encoder-Adaptive-Focal deep learning model coupled with a custom training algorithm, adept at handling the data complexities of meteorological disaster response agencies. The model proficiently extracts and optimizes capability features from time series data, directing the evaluative focus toward more complex samples, thus mitigating sample imbalance issues. Comparative analysis with existing methods like UAE-NaiveBayes, UAE-SVM, and UAE-RandomForest illustrates the superior performance of our model in ability evaluation, positioning it as a robust tool for dynamic capability evaluation. This work aims to enhance disaster management strategies, contributing to mitigating the impacts of meteorological disasters.

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Section: Forward Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

Tang,

Saga,

Dai

et al. 2023

Applied Sciences

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Application of Machine Intelligence in Smart Societies: A Critical Review of the Opportunities and Risks

Ajayi

2023

Studies in Computational Intelligence

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DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition

Shianios,

Kolios,

Kyrkou

2024

SN COMPUT. SCI.

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The integration of Unmanned Aerial Vehicles (UAVs) with artificial intelligence (AI) models for aerial imagery processing in disaster assessment, necessitates models that demonstrate exceptional accuracy, computational efficiency, and real-time processing capabilities. Traditionally Convolutional Neural Networks (CNNs), demonstrate efficiency in local feature extraction but are limited by their potential for global context interpretation. On the other hand, Vision Transformers (ViTs) show promise for improved global context interpretation through the use of attention mechanisms, although they still remain underinvestigated in UAV-based disaster response applications. Bridging this research gap, we introduce DiRecNetV2, an improved hybrid model that utilizes convolutional and transformer layers. It merges the inductive biases of CNNs for robust feature extraction with the global context understanding of Transformers, maintaining a low computational load ideal for UAV applications. Additionally, we introduce a new, compact multi-label dataset of disasters, to set an initial benchmark for future research, exploring how models trained on single-label data perform in a multi-label test set. The study assesses lightweight CNNs and ViTs on the AIDERSv2 dataset, based on the frames per second (FPS) for efficiency and the weighted F1 scores for classification performance. DiRecNetV2 not only achieves a weighted F1 score of 0.964 on a single-label test set but also demonstrates adaptability, with a score of 0.614 on a complex multi-label test set, while functioning at 176.13 FPS on the Nvidia Orin Jetson device.

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Machine Learning for Emergency Management: A Survey and Future Outlook

Cited by 10 publications

References 180 publications

Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

Application of Machine Intelligence in Smart Societies: A Critical Review of the Opportunities and Risks

DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition

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