Having Access to Data Can Lift AI to New Levels: The Era of Climate Predictions

You might have seen our previous article?

As Earth's climate changes at an alarming rate and throws complex challenges at humanity in regards to life, property and infrastructure, so is AI changing, becoming on the flipside, a beacon of hope for mitigation and adaptation. Now, while AI is poised to revolutionize climate predictions, its success hinges on one crucial factor: data.

This article delves into how data empowers AI for climate predictions, focusing specifically on flood forecasting.

What kind of data fuels AI for climate predictions?

AI thrives on a diverse data palette. Typical key data sources that empower AI for climate predictions are:

Earth Observation Data: Satellite images provide a constantly updated picture of our planet that AI can analyze to identify changes in factors like: a) sea levels: rising sea levels increase coastal flood risk, so, analyzing satellite altimetry data can tell us about sea level changes and predict potential inundation zones; b) snowpack: its levels inform us potential spring flooding events; and c) vegetation health: AI can identify areas at risk by analyzing vegetation health and indices which may be indicative of drought or flooding conditions.

Meteorological Data: weather datasets play a crucial role in flood prediction, especially the ones related to: a) precipitation, as rainfall patterns are a key factor for flood forecasting, and AI can make comparisons between real-time precipitation data and historical data, b) atmospheric pressure data, which may help us identify weather systems that might trigger floods, and c) wind direction, c) wind data, which AI can factor into its flood prediction models to understand whether wind is pushing water towards certain areas.

Ground-Based Data: Real-time information from ground-based sources provides crucial insights for flood forecasting. These include: a) river gauges, measuring water levels in real time to predict flood peaks and inundation zones; b) weather stations, which AI can combine satellite and meteorological data for improved predictions.

Social Media: Even social media may become a source of data, a rather unconventional one, but that can provide valuable insights during flooding events. For example is it possible to track the spread of floods and identify areas in need of immediate assistance.

In our previous article, we mentioned that the biggest challenge for AI to improve and further develop its climate prediction skills, is data access, quality, sharing and bias. Nonetheless, we believe that the greatest one of these is data access, as this is the lifeblood of AI based climate predictions. Therefore, open source reliable data is essential for it.

A lot of the climate data has been typically found within government agencies or private research institutions, limiting access to it to a reduced number of people. This has changed in the past few years, as open source data initiatives have increased and aim to make this data freely available to the general public and scientific community. This fosters collaboration, innovation, and ultimately, more accurate AI models.

The benefits of open source data are that it allows for independent verification and scrutiny of the data, improving transparency. Moreover, by making data readily available, researchers can reproduce and build upon each other’s work, which accelerates scientific progress and allows for democratization of knowledge by allowing a wide range of people of institutions to contribute and collaborate together to climate research and AI development.

Building a Collaborative Ecosystem: Sharing Data for a Sustainable Future

As we mentioned before, for data access and quality to make a better AI-powered climate prediction ecosystem, international collaboration and supportiveness are essential. The development of international agreements that facilitate the secure and responsible sharing of climate data across borders, is crucial, but it will require addressing the political and security we mentioned in part I of this article, to foster trust and collaboration.

Data quality control and standardization are also key elements of a healthy open source data ecosystem. Open-access platforms for storing, sharing, and accessing climate data will significantly improve data availability for AI research. Furthermore, it may enable the creation of tools for data control, standardization and verification.

This all sounds like a fairly feasible plan. But, how do we deal with data collection, access and quality in developing countries without the proper infrastructure and capacity to process data and develop technologies that may save lives?

Stay tuned for the next piece on the topic ;)