### Summary
An artificial intelligence developed by Google can accurately predict floods up to four days in advance in regions with little data on water flow.
### Facts
- 💡 Google's flood prediction AI can forecast floods four days in advance in data-poor regions, such as South America and Africa, as well as data-rich areas like Europe and the US.
- 💧 Most of the world's waterways lack accurate measurements for water flow, making flood prediction challenging.
- 👥 Lower-income countries, with limited data, are more affected by inaccurate flood predictions compared to higher-income countries with well-measured rivers and lakes.
- 🌍 Google introduced its flood prediction AI in 2018 to help improve flood forecasting accuracy worldwide.
A team of researchers has developed a new deep learning model called RECAST that outperforms traditional earthquake forecasting models like ETAS for large datasets, offering more flexibility and scalability in predicting aftershocks.
Artificial intelligence can improve climate modeling predictions by generating large ensembles of moderately high-resolution simulations that learn from observational and simulated data, leading to more accurate and usable climate predictions for risk assessment.
AI-enabled cameras and satellites are being used to detect and predict wildfires, helping to reduce response times and allocate firefighting resources more effectively in the face of increasing wildfire intensity caused by climate change.
Machine learning algorithms can accurately predict the cooling effect of tropical cyclones on sea surface temperatures, which can have wide-ranging impacts on ocean ecosystems.
Trane Technologies has introduced Trane Autonomous Control, a cloud-based AI service that utilizes predictive weather data and occupancy trends to optimize building performance and reduce energy consumption, contributing to decarbonization efforts. The solution has already demonstrated success in achieving significant energy performance improvements and a 30% reduction in carbon emissions across a national portfolio of over 100 facilities.
Machine learning models are showing promise in improving earthquake forecasts by predicting the number of aftershocks that may occur following a major earthquake, according to three new papers. While these findings are preliminary and limited to specific situations, they represent progress toward utilizing machine learning to reduce seismic risk.
Google CEO Sundar Pichai believes that the next 25 years are crucial for the company, as artificial intelligence (AI) offers the opportunity to make a significant impact on a larger scale by developing services that improve people's lives. AI has already been used in various ways, such as flood forecasting, protein structure predictions, and reducing contrails from planes to fight climate change. Pichai emphasizes the importance of making AI more helpful and deploying it responsibly to fulfill Google's mission. The evolution of Google Search and the company's commitment to responsible technology are also highlighted.
Researchers at the University of Texas at Austin have developed an AI algorithm that successfully predicted 14 earthquakes in China, with a 70% accuracy rate, using real-time seismic data and previous earthquake patterns.
Machine learning has the potential to aid climate action by providing insights and optimizing sustainability efforts, but researchers must address challenges related to data, computing resources, and the environmental impact of AI.
Artificial intelligence is being explored as a tool to enhance earthquake prediction by analyzing seismic data and identifying patterns, although current models provide forecasts with a high degree of probability, they are still too generic to make precise predictions.
Scientists have developed a deep learning model, RECAST, to forecast earthquake aftershocks, which outperforms traditional models and has the potential to improve earthquake forecasting using comprehensive global data.
Google is introducing updates to its search results and expanding its AI tools to assist individuals and policymakers in reducing emissions, predicting natural disasters, and living more sustainable lives, as part of its renewed effort to address climate change and its impacts.
Google has announced new initiatives in severe weather prediction and traffic optimization using AI, aiming for sustainability and safety. These programs are not only beneficial to local governments and organizations but also have the potential to save lives and reduce emissions.
Artificial intelligence (AI) and machine learning could revolutionize sea ice forecasting by improving accuracy and incorporating traditional Indigenous knowledge.
Machine learning and artificial intelligence could be the future tools for sea ice forecasting, providing more accurate predictions than traditional methods based on physics and statistical modeling, due to their ability to learn and adapt to the changing climate conditions.
Android users will soon have access to real-time weather information within Google Maps, as Google expands its weather updates across its apps and potentially develops a standalone weather app.
DeepMind, the Google-owned AI lab, is using artificial intelligence to tackle climate change by helping understand climate change through prediction and monitoring, optimizing existing systems and infrastructure, and accelerating breakthrough science, such as nuclear fusion. The lab also acknowledges the carbon footprint of AI and aims to deploy carbon-efficient solutions. However, access to data and collaboration with domain experts are key roadblocks in utilizing AI to fight climate change, and safety considerations are addressed by working closely with experts in the respective fields. Overall, the techno-optimist view is that AI can be a transformative tool to solve climate change problems quickly and at scale.
