Machine Learning for Earth Observation and Prediction (ML4EOP)

✅   Improved forecasting of renewable energy production (such as solar) with physics-informed neural networks
✅  Live detection and prediction of extreme events (such as floods) with state-of-the-art iterative AI pipelines fusing EO data and modelling capabilities
✅  Use of generative AI techniques together with multivariate EO data sets (e.g. Climate Change Initiative) to develop and visualise the new generation of fused EO products supporting climate adaptation

• Renewable energy forecasting
• Physics-informed neural networks
• Earth observation data
• Extreme weather events
• Flood prediction
• Real-time detection
• Generative AI techniques
• Multivariate EO data sets
• Climate adaptation
• Land cover changes
• Synthetic images
• Machine learning
• Artificial intelligence

Introduction The advent of machine learning and artificial intelligence has enabled us to process and analyze large datasets in a faster and more efficient manner. In this report, we will be discussing three specific applications of AI that have shown promising results in the field of renewable energy forecasting and climate adaptation.

Improved Forecasting of Renewable Energy Production with Physics-Informed Neural Networks

Renewable energy production, such as solar energy, can be highly variable and dependent on external factors such as weather conditions. Accurately forecasting solar energy production is important for managing grid stability and energy prices. One approach that has shown promise in this regard is the use of physics-informed neural networks (PINNs). PINNs are a type of deep learning model that incorporates physical equations into the neural network architecture to improve predictions.

A study by Khatami et al. (2021) demonstrated the effectiveness of PINNs in forecasting solar energy production. They developed a PINN model that integrated weather data, geographical location, and solar panel characteristics to predict solar energy output. The results showed that the PINN model outperformed traditional machine learning models and achieved a high level of accuracy in predicting solar energy production.

Live Detection and Prediction of Extreme Events with State-of-the-Art Iterative AI Pipelines Fusing EO Data and Modelling Capabilities Extreme weather events, such as floods, can cause significant damage to infrastructure and communities. Accurately predicting and detecting these events can help mitigate their effects. One approach that has shown promise is the use of state-of-the-art iterative AI pipelines that fuse Earth observation (EO) data and modeling capabilities.

A study by Li et al. (2020) demonstrated the effectiveness of this approach in predicting and detecting floods in real-time. The study used a combination of satellite imagery and hydrological modeling to create an iterative pipeline that continuously updated flood predictions based on new data. The results showed that the pipeline was able to accurately detect and predict flood events in real-time.

Use of Generative AI Techniques Together with Multivariate EO Data Sets to Develop and Visualize the New Generation of Fused EO Products Supporting Climate Adaptation Climate change is an increasingly pressing issue, and accurately predicting its effects is crucial for effective adaptation strategies. One approach that has shown promise in this regard is the use of generative AI techniques together with multivariate EO data sets to develop and visualize the new generation of fused EO products supporting climate adaptation.

A study by Liu et al. (2020) demonstrated the effectiveness of this approach in predicting future land cover changes in China. The study used a generative adversarial network (GAN) to generate synthetic images of land cover based on current and historical EO data. The results showed that the GAN model was able to accurately predict future land cover changes, and the synthetic images provided a useful visualization tool for policymakers.

Conclusion 

In conclusion, the use of AI and machine learning has shown great promise in improving our ability to forecast renewable energy production, predict and detect extreme weather events, and support climate adaptation. These applications have the potential to greatly benefit society by improving energy efficiency, reducing damage from extreme weather events, and informing policy decisions related to climate change.