The impact of climate risk on technological progress under the fourth industrial era

Technological Forecasting and Social Change, Год журнала: 2024, Номер 202, С. 123325 - 123325

Опубликована: Март 11, 2024

Язык: Английский

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

Risks, Год журнала: 2024, Номер 12(2), С. 19 - 19

Опубликована: Янв. 23, 2024

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

Язык: Английский

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Digital post-disaster risk management twinning: A review and improved conceptual framework

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер 110, С. 104629 - 104629

Опубликована: Июнь 24, 2024

Digital Twins (DT) is the real-time virtual representation of systems, communities, cities, or even human beings with substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against adverse effects disasters. However, this remains largely unrecognized poorly understood in disaster management. This study explores current achievements, existing challenges, untapped DT management, accordingly, proposes an improved twin-based framework. paper employs a systematic literature review approach focusing on digital twinning (DPRMT) derived from two databases: Scopus Web Science. After screening process exclusion criteria, final analysis synthesizes findings selected set 96 papers. The results revealed that previous studies are not beyond only providing general statements about DT. There need for diverse data collection methods, considering demographic financial aspects, understanding social dynamics, employing dynamic models, recognizing interconnected giving due attention often-neglected recovery phase. comprehensive DPRMT concept framework leveraging decision-makers holistic efficient offers real-time, detailed, data-driven modeling solutions insights into disaster-affected areas communities. It also helpful optimize response planning, resource allocation, scenario testing by capturing complex behaviors systems entities often overlooked studies.

Язык: Английский

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Explainability can foster trust in artificial intelligence in geoscience

Nature Geoscience, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Язык: Английский

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Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review

Water, Год журнала: 2025, Номер 17(1), С. 85 - 85

Опубликована: Янв. 1, 2025

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding these chemicals’ fate, distribution, transport, and risk aquatic ecosystems. Modeling is useful approach for determining ECs’ characteristics their behaviors environments. This article proposes systematic taxonomy EC models addresses gaps the comprehensive analysis applications. The reviewed include conventional quality models, multimedia fugacity machine learning (ML) models. Conventional have higher prediction accuracy spatial resolution; nevertheless, they are limited functionality can only be used to predict contaminant concentrations Fugacity excellent at depicting how travel between different environmental media, but cannot directly analyze variations parts same media because model assumes that constant within compartment. Compared other ML applied more scenarios, such as identification assessments, rather than being confined concentrations. In recent years, with rapid development artificial intelligence, surpassed becoming one newest hotspots study ECs. primary challenge faced by outcomes difficult interpret understand, this influences practical value an some extent.

Язык: Английский

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Artificial intelligence for modeling and understanding extreme weather and climate events

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 24, 2025

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes data limited annotations. This paper reviews how AI is being used to analyze climate events (like floods, droughts, wildfires, heatwaves), highlighting importance creating accurate, transparent, reliable models. We discuss hurdles dealing data, integrating real-time information, deploying understandable models, all crucial steps for gaining stakeholder trust meeting regulatory needs. provide an overview can help identify explain more effectively, disaster response communication. emphasize need collaboration across different fields create solutions that are practical, understandable, trustworthy enhance readiness risk reduction. Artificial Intelligence transforming study like helping overcome challenges integration. review article highlights models improve response, communication trust.

Язык: Английский

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Explainable Sinkhole Susceptibility Mapping Using Machine-Learning-Based SHAP: Quantifying and Comparing the Effects of Contributing Factors in Konya, Türkiye

Applied Sciences, Год журнала: 2025, Номер 15(6), С. 3139 - 3139

Опубликована: Март 13, 2025

Sinkholes, naturally occurring formations in karst regions, represent a significant environmental hazard, threatening infrastructure, agricultural lands, and human safety. In recent years, machine learning (ML) techniques have been extensively employed for sinkhole susceptibility mapping (SSM). However, the lack of explainability inherent these methods remains critical issue decision-makers. this study, Konya Closed Basin was mapped using an interpretable model based on SHapley Additive exPlanations (SHAP). The Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Machine (LightGBM) algorithms were employed, interpretability results enhanced through SHAP analysis. Among compared models, RF demonstrated highest performance, achieving accuracy 95.5% AUC score 98.8%, consequently selected development final map. analyses revealed that factors such as proximity to fault lines, mean annual precipitation, bicarbonate concentration difference are most variables influencing formation. Additionally, specific threshold values quantified, effects contributing analyzed detail. This study underscores importance employing eXplainable Artificial Intelligence (XAI) natural hazard modeling, SSM example, thereby providing decision-makers with more reliable comparable risk assessment.

Язык: Английский

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Navigating the Crescendo of Challenges in Harnessing Artificial Intelligence for Disaster Management

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 64 - 94

Опубликована: Фев. 16, 2024

Global warming worsens natural disasters and humanitarian issues. Disaster prediction relies on satellites weather stations. AI may help catastrophe management. reduces disaster risk in many ways. Early warning systems, forecasts, recovery, reconstruction improve. could us predict, prepare, recover from calamities. These technologies provide climate change mitigation community protection hope. They propose a better future amid catastrophes. DRR is aggressively adopting AI, notably ML. This field encompasses severe event prediction, hazard mapping, real-time detection, situational awareness, decision assistance, more. Growing usage of management raises questions about its benefits. We face what issues? How can these difficulties be resolved opportunities maximised? What tell policymakers, stakeholders, the public to reduce disasters? The chapter introduces implementation issues, solutions make world more peaceful will addressed.

Язык: Английский

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SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye

Remote Sensing, Год журнала: 2024, Номер 16(15), С. 2842 - 2842

Опубликована: Авг. 2, 2024

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding mitigating the risks potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), map Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation trained ML showed that Random Forest (RF) model outperformed XGBoost LightGBM, achieving highest test accuracy (95.6%). All classifiers demonstrated strong predictive performance, but RF excelled sensitivity, specificity, precision, F-1 score, making it preferred for generating conducting SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this fills critical gap employing summary dependence plots comprehensively assess each factor’s contribution, enhancing explainability reliability results. analysis reveals clear associations between such as wind speed, temperature, NDVI, slope, distance villages with increased susceptibility, while rainfall streams exhibit nuanced effects. spatial distribution classes highlights areas, flat coastal near settlements agricultural lands, emphasizing need enhanced awareness preventive measures. These insights inform targeted management strategies, highlighting importance tailored interventions like firebreaks management. However, challenges remain, including ensuring selected factors’ adequacy across diverse regions, addressing biases from resampling spatially varied data, refining broader applicability.

Язык: Английский

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Explainable Artificial Intelligence for Sustainable Urban Water Systems Engineering

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104349 - 104349

Опубликована: Фев. 1, 2025

Язык: Английский

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