Council Post: Data Science and Analytics for environmental good
When we think of a qualifier for our planet’s environment, the word alarming comes to mind. In the 2030 agenda of the UN preamble, the world leaders pledged that they are “Determined to protect the planet from degradation, including through sustainable consumption and production, sustainably managing its natural resources and taking urgent action on climate change, so that it can support the needs of the present and future generations”. Significant environmental costs associated with the unprecedented economic expansion globally have seemingly harmed the potential for social and sustainable development. We are yet to make adequate advances towards environmental sustainability and promoting environmental sustainability is necessary to make the Sustainable Development Goals (SDGs) more attainable and to ensure the sustainability of our planet. Globally, there is a need to manage and prevent increasing hazards and disasters due to the effects of climate change, air, land, and water pollution, biodiversity loss, and land degradation. In the absence of reforms, the situation appears hopeless. The world’s “capacity to meet human requirements” is also in jeopardy due to a significant extinction crisis. The implementation of SDGs calls for a range of data, insights, and statistics. For the decision-makers to develop strategies and make important choices, the information must be relevant, in-depth, timely, appropriate, and sufficient. Although statistical volume still has to be solidified, data literacy needs to advance at every stage of the decision-making process. It will take a lot of coordinated work by users and data developers to explore data-driven solutions. Innovative technology to produce and use data and statistics will also be needed to overcome the multi-layered challenges to sustainable development. Data science and artificial intelligence (AI) are assisting in the advancement of significant environmental projects, from climate monitoring to wildlife protection and connecting people with nature, even though technology isn’t the only solution to the issue. This article will be talking about how Data Science and AI has been helping achieve environmental goals in line with SDGs. Building a Digital Twin to Revolutionise Climate Prediction A ‘digital twin’ is primarily a virtual representation, in this example, of the land, water, air, and life on Earth. The ‘Destination Earth’ initiative, spearheaded by the European Union, simulates human and natural activity to monitor the state of the planet. The planetary-scale digital model will gather ongoing, real-time data and offer incredibly precise forecasts regarding climate change, planetary resources, extreme weather conditions, and natural disasters—such as fires, cyclones, droughts, and floods. Over the next seven to ten years, Destination Earth (or, DestinE) will be deployed gradually. The objective is to give decision-makers, the scientific community, and other users a way to assess various scenarios and better support environmental and sustainable development strategies. The ability to predict environmental change and assess the effects of human activities on the globe are both possible with a digital Earth. Controlling pollution Large-scale pollution is a major issue in urban areas. The Internet of Things (IoT) gathers information about city pollution from a number of sources, such as vehicle emissions, airflow direction, pollen levels, weather, traffic volumes, and so forth. Following the extraction of all the required data, machine learning algorithms evaluate this data and modify the appropriate prediction models in accordance with a number of variables, including the season, the different topologies of the city, and more. Using this, machine learning algorithms can provide pollution predictions for various city areas, preempting municipal officials about the occurrence of a problem. Google Maps and Waze are two examples of smart transportation applications where AI is already in use. These applications use machine learning algorithms to improve navigation, promote safety, and provide information on traffic flows and congestion (e.g., Nexar). Utilising AI for Wildlife Protection, Ocean Cleanup, and Other Purposes In a couple of seconds, artificial intelligence can classify numerous photos. The same big data scale classification could require hundreds of human hours to duplicate. Future applications of AI could be made to solve thousands of environmental problems. For instance, researchers can spot patterns and track changes to land surfaces, such shrinking sea areas and ice caps, which may be used to assess future dangers. This is made possible using artificial intelligence (AI) and data from NASA. Another method to anticipate, plan for, and get ready for future floods has been developed by the environmental organisation Chesapeake Conservancy. It is a detailed map that covers 10,000 square miles between New York and Southern Virginia, focusing on regions that drain into Chesapeake Bay. This map, which was created using AI and satellite pictures, is the most accurate one available for preparing floods because it can display items as small as 3 sq. ft. Disaster response and weather forecasting Drones, robust adaptive platforms, and other instruments can be used to monitor tremors, floods, windstorms, changes in sea level, and other potential natural hazards. With the use of automatic triggers and real-time information availability, this technology can assist the government and concerned agencies in taking prompt action, enabling early evacuations whenever necessary. The impact of extreme weather events on infrastructure and other systems is being modelled by a variety of meteorological companies, tech companies like IBM and Palantir, and insurance companies using AI in conjunction with conventional physics-based modelling techniques to provide advice on disaster risk management strategies. Future AI techniques might successfully develop a digital dashboard for the planet that would enable global monitoring, modelling, prediction, and management of environmental processes. Monitoring deforestation, CO2 levels, sea levels, wildlife movement, illicit activities, pollution, and improving natural catastrophe prediction are just a few examples. Time and resources are running out on a global scale, so this strategy must be implemented right away in order to accomplish environmental improvements. Data and AI are empirically required to bring about the changes our planet needs. For the benefit of our planet and the quality of life in the future, a collaboration between research organisations, businesses, industries, governments, and nonprofits across the globe must be established. As Hilary Mason, data scientist and founder of Fast Forward Labs said, “The core advantage 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 in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.
Data and AI are empirically required to bring about the changes our planet needs. For the benefit of our planet and the quality of life in the future, a collaboration between research organisations, businesses, industries, governments, and nonprofits across the globe must be established.
Data and AI are empirically required to bring about the changes our planet needs. For the benefit of our planet and the quality of life in the future, a collaboration between research organisations, businesses, industries, governments, and nonprofits across the globe must be established.
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["big data", "data science", "Go", "machine learning", "artificial intelligence", "AI", "Git", "Aim", "analytics", "R"]
["AI", "artificial intelligence", "machine learning", "data science", "analytics", "Aim", "R", "Go", "Git", "big data"]
With close to 15 years of professional experience, Anirban specialises in Data Sciences, Business Analytics, and Data Engineering, spanning various verticals of online and offline Retail and building analytics teams from the ground up. Following his Masters from JNU in Economics, Anirban started his career at Target and spent more than eight years developing in-house products like Customer Personalisation, Recommendation Systems, and Search Engine Classifiers. Post Target, Anirban became one of the founding members at Data Labs (Landmark Group) and spent more than 4.5 years building the onshore and offshore team of ~100 members working on Assortment, Inventory, Pricing, Marketing, eCommerce and Customer analytics solutions.