Machine learning algorithms, such as Decision Tree, Random Forest, and Artificial Neural Network, are effective in analyzing climate change risks, but future scenarios and cascading hazards require further research.

Exploring machine learning potential for climate change risk assessment

Machine learning can provide customized policy solutions for climate change mitigation in urban areas, accelerating urban infrastructure provision and transforming urban infrastructure provision.

Machine learning for geographically differentiated climate change mitigation in urban areas

Machine learning can be a powerful tool in reducing greenhouse gas emissions and adapting society to a changing climate, from smart grids to disaster management.

Tackling Climate Change with Machine Learning

Machine learning and artificial intelligence can enhance climate change research and preparedness by aiding teleconnection identification and providing enhanced warnings of approaching weather features.

Machine learning and artificial intelligence to aid climate change research and preparedness

Machine learning methods will be a key feature in future weather forecasting, with topics like photovoltaic and wind energy, atmospheric physics, and climate change being key areas of interest.

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

Machine learning can accelerate climate change projections and help uncover early indicators of modelled long-term climate responses, enhancing climate change detection, predictability, and attribution.

Predicting global patterns of long-term climate change from short-term simulations using machine learning

Machine learning (ML) has rapidly become a competitive alternative to numerical knowledge-based alternatives in weather and climate modeling, with applications ranging from improved solvers to fully ML-based prediction models.

Machine learning for numerical weather and climate modelling: a review

Machine learning improves the prediction of corporate emissions, resulting in up to 30% accuracy gains compared to existing models, aiding financial regulators and investors in climate transition risk analysis.

Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach

Machine learning can improve the prediction of future cloud fractional cover, but the ConvLSTM model outperforms the regression equation in coastal and mountain areas.

Prediction of Cloud Fractional Cover Using Machine Learning

Machine Learning techniques show promise in improving short-term weather and medium-to-long-term climate forecasts, but their application in medium-to-long-term climate forecasting remains limited due to factors like data limitations.

Machine Learning Methods in Weather and Climate Applications: A Survey

Machine learning algorithms can improve climate change forecasting, providing insights into the difficulties and potential benefits of data-driven climate modeling.

Climate Change Forecasting using Machine Learning Algorithms

Machine learning techniques, such as artificial neural networks and support vector machines, can perform poorly when extrapolating climate change projections, highlighting the need for caution in their use.

A critical view on the suitability of machine learning techniques to downscale climate change projections: Illustration for temperature with a toy experiment

Machine learning can improve climate services by bridging the gap between climate data, projections, and socio-economic data, enhancing understanding of climate change impacts for society.

Machine learning helps to improve climate services

The SARIMA Model effectively forecasts climate change using existing datasets and simulations, aiding government, business, and public planning.

Climate Change Forecasting Using Machine Learning SARIMA Model

Machine learning can improve cloud models, enhancing weather and climate predictions by enhancing rain onset and frequency, while reducing computational costs.

Machine Learning the Warm Rain Process

AI algorithms can accurately predict climate change impacts, aiding in proactive adaptation and sustainable decision-making for policymakers and researchers.

Artificial Intelligence (AI) and the Prediction of Climate Change Impacts

Machine Learning algorithms, particularly linear regression, can accurately predict global warming and help reduce it within a few years.

CLIMATE CHANGE ANALYSIS USING MACHINE LEARNING

Machine Learning techniques can accurately predict air temperatures, with Support Vector Machines being preferred for global scale forecasting due to their simplicity and accuracy.

Air Temperature Forecasting Using Machine Learning Techniques: A Review

Deep learning may one day replace numerical weather models, but fundamental breakthroughs are needed before this goal becomes a reality.

Can deep learning beat numerical weather prediction?

The decision tree model outperforms the other two models in training and predicting meteorological parameters, with an average prediction of 71.5%.

EVALUATING THE PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR PREDICTING METEOROLOGICAL PARAMETERS

The potential of machine learning in improving climate change predictions.

These studies suggest that machine learning has significant potential in improving climate change predictions, enhancing weather forecasting, reducing greenhouse gas emissions, and providing customized policy solutions, although further research is needed for future scenarios and medium-to-long-term forecasts.