Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

Ghaffarian, Saman; Taghikhah, Firouzeh Rosa; Maier, Holger R.

doi:10.1016/j.ijdrr.2023.104123

Cited by 20 publications

(4 citation statements)

References 113 publications

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“…The capacity of AI-powered data analytics to identify new threats is one of its main benefits. Conventional risk assessment techniques frequently depend on predetermined risk variables and historical data Ghaffarian et al 2023). On the other hand, new risks might materialize quickly in a constantly changing business environment.…”

Section: Ai-powered Data Analyticsmentioning

confidence: 99%

The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece

Kalogiannidis,

Kalfas,

Papaevangelou

et al. 2024

Risks

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This study examined the efficacy of artificial intelligence (AI) technologies in predictive risk assessment and their contribution to ensuring business continuity. This research aimed to understand how different AI components, such as natural language processing (NLP), AI-powered data analytics, AI-driven predictive maintenance, and AI integration in incident response planning, enhance risk assessment and support business continuity in an environment where businesses face a myriad of risks, including natural disasters, cyberattacks, and economic fluctuations. A cross-sectional design and quantitative method were used to collect data for this study from a sample of 360 technology specialists. The results of this study show that AI technologies have a major impact on business continuity and predictive risk assessment. Notably, it was discovered that NLP improved the accuracy and speed of risk assessment procedures. The integration of AI into incident response plans was particularly effective, greatly decreasing company interruptions and improving recovery from unforeseen events. It is advised that businesses invest in AI skills, particularly in fields such as NLP for automated risk assessment, data analytics for prompt risk detection, predictive maintenance for operational effectiveness, and AI-enhanced incident response planning for crisis management.

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Section: Ai-powered Data Analyticsmentioning

confidence: 99%

The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece

Kalogiannidis,

Kalfas,

Papaevangelou

et al. 2024

Risks

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“…The deliberate use of deep learning techniques exemplifies a larger trend in disaster management wherein machine learning approaches are becoming more popular due to its ability to handle complex and dynamic datasets [24,25]. Despite the potential for deep learning algorithms to enhance accuracy, concerns persist regarding their resource-intensive nature and inefficiency in real-time monitoring applications [26].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Prianes,

Fortuno,

Onesa

et al. 2024

IJACSA

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In the era of rapid technological advancement, the integration of cutting-edge technologies plays a pivotal role in enhancing the efficiency and responsiveness of critical systems. iRESPOND, a real-time Geospatial Information and Alert System, stands at the forefront of such innovations, facilitating timely and informed decision-making in dynamic environments. As the demand for accurate and swift responses, the role of CNN models in iRESPOND becomes significant. The study focuses on seven prominent CNN architectures, namely EfficientNet (B0, B7, V2B0, and V2L), InceptionV3, ResNet50, and VGG19 and with the integration of different optimizers and learning rates. The methodology employed a strategic implementation of looping during the training phase. This iterative approach is designed to systematically re-train the CNN models, emphasizing identifying the most suitable architecture among the seven considered variants. The primary objective is to discern the optimal architecture and fine-tune critical parameters, explicitly targeting the optimizer and learning rate values. The differential impact of each model on the system's ability is to discern patterns and anomalies in the image datasets. ResNet50 exhibited robust performance showcasing suitability for real-time processing in dynamic environments with a better accuracy result of 95.02%. However, the EfficientNetV2B0 model, characterized by its advancements in network scaling, presented promising results with a lower loss of 0.187. Generally, the findings not only contribute valuable insights into the optimal selection of architectures for iRESPOND but also highlight the importance of fine-tuning hyperparameters through an iterative training approach, which paves the way for the continued enhancement of iRESPOND as an adaptive system.

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“…By analyzing data on weather patterns, sea level rise projections, and population density, AI can help stakeholders prioritize and implement mitigation and adaptation measures. For example, AI has been used to identify regions prone to flooding and inform the development of resilient infrastructure [17].…”

Section: Potential Benefits Of Ai In Addressing Climate Changementioning

confidence: 99%

Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health

2024

J Curr Trends Comp Sci Res

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Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors. However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health. To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts. In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

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Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

Cited by 20 publications

References 113 publications

The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece

The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Harnessing the Power of Artificial Intelligence in Climate Change Mitigation: Opportunities and Challenges for Public Health

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