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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Harnessing artificial intelligence and remote sensing in climate-smart agriculture: the current strategies needed for enhancing global food security

Cogent Food & Agriculture, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 20, 2025

Global food security is seriously threatened by climate change, which calls for creative agricultural solutions. However, little known about how different smart technologies are integrated to enhance security. As a strategic reaction these difficulties, this review investigates the incorporation of remote sensing (RS) as well artificial intelligence (AI) into climate-smart agriculture (CSA). This demonstrates advances can improve resilience, productivity, and sustainability utilizing AI's capacity predictive analytics, crop modelling, precision agriculture, along with RS's strengths in projections, land management, continuous surveillance. Several important tactics were covered, such combining AI RS regulate risks, maximize resource utilization, practice choices. The also discusses issues like policy frameworks, building, accessibility that prevent from being widely adopted. highlights further CSA offers insights they help ensure systems remain secure changing climates.

Язык: Английский

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Towards the next generation of Geospatial Artificial Intelligence

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104368 - 104368

Опубликована: Янв. 20, 2025

Язык: Английский

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Advancing Environmental Monitoring through AI: Applications of R and Python

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Янв. 20, 2025

The integration of Large Language Models (LLMs), artificial intelligence (AI), and programming languages such as Python R has revolutionized environmental monitoring. These technologies enhance data analysis, automate reporting, improve communication among stakeholders, enabling more informed timely decision-making. AI-driven tools facilitate a wide range monitoring activities, including pollution tracking, species conservation, climate change by increasing the accuracy speed processing. predictive capabilities AI are essential for forecasting conditions trends, supporting development effective policies actions. Additionally, aids in regulatory compliance continuously analyzing real-time data, alerting authorities to potential violations. Community engagement is also enhanced makes accessible understandable, fostering greater public awareness participation conservation efforts. Despite these advancements, challenges privacy, model bias, interpretability, quality must be addressed fully leverage technologies. As AI, Python, continue evolve, their applications sciences expected significantly contribute sustainable efforts globally.

Язык: Английский

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Unlocking the Potential of Artificial Intelligence for Sustainable Water Management Focusing Operational Applications

Water, Год журнала: 2024, Номер 16(22), С. 3328 - 3328

Опубликована: Ноя. 19, 2024

Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes their management is crucial to perform efficient resource (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, taking controlling measures manage risks ensure sustainability. Artificial intelligence (AI) techniques leverage these knowledge fields single theme. This review article focuses on the potential AI in two specific areas: supply-side demand-side measures. It includes investigation applications leak detection infrastructure maintenance, demand forecasting supply optimization, treatment desalination, quality pollution control, parameter calibration optimization applications, flood drought predictions, decision support systems. Finally, an overview selection appropriate suggested. The nature adoption investigated using Gartner hype cycle curve indicated that learning application has advanced different stages maturity, big data reach plateau productivity. also delineates pathways expedite integration AI-driven solutions harness transformative capabilities for protection global resources.

Язык: Английский

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Enhancing sub-seasonal soil moisture forecasts through land initialization

npj Climate and Atmospheric Science, Год журнала: 2025, Номер 8(1)

Опубликована: Март 12, 2025

Язык: Английский

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Artificial Intelligence in Water Conservation

Опубликована: Янв. 1, 2025

Язык: Английский

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Wicked Problems: A Novel Approach Using Artificial Intelligence and Scenarios

Journal of Leadership & Organizational Studies, Год журнала: 2025, Номер unknown

Опубликована: Март 28, 2025

‘Wicked’ problems are among today's most complex and pressing issues. Examples of wicked climate change sustainability, as well other that have critical implications for a wide range stakeholders, including organizations their leaders. Despite growing body work on problems, solutions remain elusive, underscoring need new innovative approaches. Recent technological advances creating opportunities to ‘untangle’ by combining the use artificial intelligence with scenarios. The purpose this conceptual paper is therefore present framework dealing more effectively strengthening utility our responses them. first task define explore concept ‘wicked’ problem in order identify suitable analytic entry points. This results proposed typology problems. Next, drawing from literature, set relevant AI capabilities synthesized. Their applicability discussed then operationalized using sequential steps risk management process framework. Using focal issue, it demonstrated combined scenarios synergistic, leaving us better prepared tackle challenges concludes cautionary words, about risks inherent AI. These particular significance leaders roles they should play.

Язык: Английский

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An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence

Water, Год журнала: 2025, Номер 17(9), С. 1384 - 1384

Опубликована: Май 4, 2025

Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, resource management, studies. Among approaches that are employed for estimating ET, the Penman–Monteith equation is known widely accepted reference approach. However, extensive data requirement of this method crucial challenge limits its usage, particularly data-scarce regions. Therefore, an alternative approach, artificial intelligence (AI) models have gained prominence evapotranspiration because their capacity to handle complicated relationships between meteorological variables loss processes. These leverage large datasets advanced algorithms provide accurate timely ET predictions. The current research aims review previous studies addressing application AI model modeling under four main categories: neuron-based, tree-based, kernel-based, hybrid models. results study indicated traditional like (PM) require input data, while AI-based offer promising alternatives due ability complex nonlinear relationships. Despite potential, face challenges overfitting, interpretability, inconsistent variable selection, lack integration with physical processes, highlighting need standardized configurations, better pre-processing techniques, incorporation remote sensing data.

Язык: Английский

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A statistical learning approach to Mediterranean cyclones

Chaos An Interdisciplinary Journal of Nonlinear Science, Год журнала: 2025, Номер 35(5)

Опубликована: Май 1, 2025

Mediterranean cyclones are extreme meteorological events of which much less is known compared to their tropical, oceanic counterparts. The rising interest in such phenomena due impact on a region increasingly more affected by climate change, but precise characterization remains nontrivial task. In this work, we showcase how Bayesian algorithm (Latent Dirichlet Allocation) can classify relying wind velocity data, leading drastic dimensional reduction that allows the use supervised statistical learning techniques for detecting and tracking new cyclones.

Язык: Английский

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