Data Science and Analytics for Environmental Good

When we think of a qualifier for the environment of our planet, the word alarming comes to mind. In the 2030 Agenda of the UN preamble, world leaders pledged to be “determined to protect the planet from degradation, including through sustainable consumption and production, sustainably managing its natural resources and taking urgent action against climate change, so that it can support the needs of present and future generations.” The significant environmental costs associated with unprecedented global economic expansion have apparently undermined the potential for social and sustainable development. We have yet to make sufficient progress towards environmental sustainability and the promotion of environmental sustainability is necessary to make the Sustainable Development Goals (SDGs) more achievable and to ensure the sustainability of our planet.

Globally, there is a need to manage and prevent the increase in risks and disasters due to the effects of climate change, air, soil and water pollution, loss of biodiversity and land degradation. In the absence of reforms, the situation seems hopeless. The “world’s ability to meet human needs” is also under threat due to a major extinction crisis.

The implementation of the SDGs requires a series of data, ideas and statistics. For decision-makers to develop strategies and make important choices, information must be relevant, thorough, timely, appropriate and sufficient. While the statistical volume still needs to be solidified, data literacy needs to advance at every stage of the decision-making process. It will take a lot of coordinated work from data users and developers to explore data-driven solutions. Innovative technology to produce and use data and statistics will also be needed to overcome the multilevel challenges of sustainable development.

Data science and artificial intelligence (AI) are helping to advance important environmental projects, from monitoring the climate to protecting wildlife and connecting people with nature, even if the technology is not not the only solution to the problem.

This article will talk about how data science and AI have helped achieve environmental goals in line with the SDGs.

Building a digital twin to revolutionize climate prediction

A “digital twin” is primarily a virtual representation, in this example, of land, water, air, and life on Earth. The European Union-led Destination Earth initiative simulates human and natural activity to monitor the state of the planet. The planetary-scale numerical model will collect continuous data in real time and offer incredibly accurate predictions of climate change, planetary resources, extreme weather conditions and natural disasters, such as fires, cyclones, droughts and flooding.

Over the next seven to ten years, Destination Earth (or, DestinE) will be rolled out gradually. The aim is to give decision makers, the scientific community and other users a way to assess different scenarios and better support environmental and sustainable development strategies. The ability to predict environmental changes and assess the effects of human activities on the globe are both possible with a digital Earth.

Control pollution

Large-scale pollution is a major problem in urban areas. The Internet of Things (IoT) collects information about urban pollution from a number of sources, such as vehicle emissions, airflow direction, pollen levels, weather, volumes traffic, etc. Following the extraction of all the required data, machine learning algorithms evaluate this data and modify the appropriate prediction models based on a number of variables including season, different city topologies, etc. Using this, machine learning algorithms can provide pollution forecasts for different areas of the city, alerting city officials to the onset of a problem. Google Maps and Waze are two examples of smart transportation apps where AI is already in use. These apps use machine learning algorithms to improve navigation, promote safety, and provide information about traffic flows and congestion (eg, Nexar).

Using AI for wildlife protection, ocean cleanup and other purposes

In seconds, artificial intelligence can classify many photos. The same large-scale classification of data could require hundreds of human hours to duplicate. Future applications of AI could be made to solve thousands of environmental problems. For example, researchers can spot patterns and track changes on land surfaces, such as shrinking sea areas and ice caps, which can be used to assess future hazards. This is made possible by artificial intelligence (AI) and NASA data. Another method to anticipate, plan and prepare for future floods has been developed by the environmental organization Chesapeake Conservancy. This is a detailed map that covers 10,000 square miles between New York and southern Virginia, focusing on areas that drain into the Chesapeake Bay. This map, which was created using AI and satellite imagery, is the most accurate available for flood preparedness as it can display features as small as 3 square feet.

Disaster response and weather forecasting

Drones, rugged adaptive platforms and other instruments can be used to monitor earthquakes, floods, windstorms, sea level changes and other potential natural hazards. Through the use of automatic triggers and the availability of real-time information, this technology can help the government and relevant agencies take quick action, enabling early evacuations whenever needed. The impact of extreme weather events on infrastructure and other systems is modeled by various weather companies, technology companies like IBM and Palantir, and insurance companies using AI in conjunction with conventional modeling techniques based on physics to provide advice on disaster risk management strategies. .

Future AI techniques could successfully develop a digital dashboard for the planet that would enable global monitoring, modeling, prediction and management of environmental processes. Monitoring deforestation, CO2 levels, sea levels, wildlife movements, illegal activities, pollution and improving the prediction of natural disasters are just a few examples.

Time and resources are running out globally, so this strategy must be implemented immediately in order to accomplish environmental improvements. Data and AI are empirically needed to bring about the changes our planet needs. For the benefit of our planet and the future quality of life, collaboration between research organizations, businesses, industries, governments and non-profit organizations around the world must be established. As Hilary Mason, data scientist and founder of Fast Forward Labs, said, “The main benefit of data is that it tells you something about the world that you didn’t know before.”

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives from the data science and analytics industry. To check if you are eligible for membership, please complete the form here.

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