Artificial intelligence is based on machine learning. It allows AI to grow and adapt to new situations. This learning use algorithms to teach itself and soon achieve self-sufficiency in performance. Machine learning may now help specialists in environmental preservation and conservation with its vast potential and capability.
Natural catastrophes, global warming, and human impact all have different effects on the ecosystem. During storms, water infrastructure can get contaminated, and the climatic problem and human hunting constantly threaten animals. Any environmental issues must be addressed before they become more serious. And what better way to do so than to use contemporary technology?
While machine learning won’t be able to prevent each of these incidents, it can help to lessen their frequency and the damage they cause. But how?
How Machine Learning Can Help Save The Environment.
Pollution Control
In bigger cities, pollution is a huge problem. Also urgently required is an innovative urban pollution control mechanism internet of Things (IoT), and Machine Learning!
IoT receives data regarding city pollution from a variety of sources, including automobile emissions, airflow direction, pollen levels, weather, traffic levels, and so on. Following the extraction of all necessary data, Machine Learning algorithms assess the data while adjusting the appropriate prediction models depending on a variety of factors such as the current season, the city’s various topologies, and so on. Machine Learning algorithms can construct pollution estimates for various regions of the city using this study, alerting municipal officials in advance where a problem may arise.
Weather & Climate Prediction
An emerging field known as “Climate Informatics” is growing, employing artificial intelligence to alter weather forecasting and increase our knowledge of climate change’s consequences. This subject has historically necessitated high-performance, energy-intensive computation. Still, deep-learning networks can enable computers to run considerably quicker and integrate more complexity of the ‘real-world’ systems into calculations.
In just over a decade, developments in AI and computing power will enable household computers to have the same processing capability as today’s supercomputers, cutting research costs, increasing scientific output, and speeding up discoveries. AI approaches may also be used to correct model biases, retrieve the most important data to minimize data deterioration, anticipate severe occurrences, and simulate repercussions.
Agriculture & food systems
Automated data collecting, decision-making, plus corrective actions through robotics are used in AI-augmented agriculture. This allows early detection of crop illnesses and concerns, delivers scheduled nourishment to animals, and optimizes agricultural inputs & returns depending on supply and demand. This has the potential to improve the agriculture industry’s resource efficiency by reducing the use of water, fertilizer, and pesticides that harm vital ecosystems, as well as increasing resistance to climate extremes.
Disaster Prediction & Response
Machine Learning can predict many natural disasters, like hurricanes, earthquakes, and flash floods, as well as any man-made disasters, including oil spills.
Deep learning networks are used in an Earthquake Detection System, which is a good example. It was designed by Harvard & Google experts and can predict tremors after a major earthquake. This method works by detecting patterns in seismic data that were previously difficult to assess using conventional technology. The likelihood of aftershocks can be influenced by several factors, including ground composition, seismic plate contact, energy transfer through the ground, and so on.
Wildlife Protection
Many wild creatures are now becoming endangered or extinct in multiple countries. As a result, we must guarantee that these species are safeguarded in their natural habitats, ensuring that the woodlands and wild grasslands remain intact.
WildTrack is a company that specializes in this type of thing. They use a footprint identification technique (FIT) combined with IoT and ML algorithms to establish an animal’s species, age, and gender based on its unique footprint. Then, using this one-of-a-kind information, researchers may uncover specific patterns related to animal migrations, male-to-female ratio, animal populations, and so on, which can help save many endangered species.
Autonomous & Connected Electric Vehicles
Over several years or decades, AI-guided autonomous vehicles (AVs) will facilitate a shift to on-demand mobility. The route, as well as traffic optimization, eco-driving algorithms, controlled “platooning” of automobiles to traffic, and autonomous ride-sharing services, can all help to reduce greenhouse gas emissions in urban transportation. To achieve meaningful advantages, electric AV fleets will be needed.
Distributed Energy Grids
Machine learning can increase demand & supply forecasting for renewables across a distributed grid, enhancing energy storage, efficiency, also load management, and aid in renewables integration and dependability. It can also enable dynamic pricing and trading, generating market incentives.
Read Blog on: What are the common applications of supervised and unsupervised learning?
How AI/ML Impacts Sustainable Development Goals
AI is essential to accomplishing all of the Sustainable Development Goals, including eliminating poverty and hunger, accomplishing sustainable energy and gender equality, as well as safeguarding and preserving biodiversity. The graphs below show how AI may help achieve SDGs.
The United Nations has created 17 Sustainable Development Goals, which are divided into three categories: environment, economy, as well as society. The research explored how AI advancements may help some people while hindering others.
So, while AI offers new possibilities, it may not necessarily yield great results depending on the environment in which they are applied. For example, AI might enable nationalism, prejudice, and undemocratic election outcomes in a society with inadequate ethical oversight, transparency, or democratic controls. Better regulatory agencies are needed to supervise the development of AI, as it will have a significant impact on humanity’s future.
Earth Sciences Learnings
This emerging AI technology – which takes no input data, uses far less processing resources, and learns from itself – might soon advance to the point where it can be applied to real-world challenges in the natural sciences. Collaboration with Earth scientists is critical in identifying the systems that can be codified to apply reinforcement learning for scientific development and discovery in climate research, materials science, biology, and other domains.
Demis Hassabis, a co-founder of DeepMind, has claimed that a descendent of AlphaGo Zero may be used to hunt for a room temperature superconductor — a hypothetical substance that allows for extraordinarily efficient energy systems – in materials science.
Using AI/ML to offset climate change
While AI is already having a good influence on the environment throughout the world, businesses could do more. By offering chances to communicate what’s working, businesses and organizations might share data and maximize their use of natural resources.
Simultaneously, ML might help in the creation of a much more profitable framework for enterprises that use natural resources. Machine learning allows systems to detect minute changes in data, identify faults in real-time, and adjust to ensure that organizations spend as little as possible.
Machine Learning – Reshaping the World
To summarize, we are living in fascinating times. Developing technology like AI is now feasible to address some of the world’s most pressing issues. It’s time to put artificial intelligence to work for the world’s greater good.
Professionals with experience working with machine learning, deep learning, as well as natural language processing are in high demand. Every year, all 3 of these technologies are ranked in the top 10 talents that professionals should possess.
According to Fortune Business Insights, the machine learning industry will grow from $15.50 billion in 2021 to $152.24 billion in 2028, with a projected CAGR of 38.6% throughout that time. IT, healthcare, telecommunications, automotive, and BSFI are the primary areas where machine learning with its subcategories are being implemented, and they are driving this expansion.
Machine learning will become a critical component of the globe in the next years. Machine learning will see a considerable increase in demand, and its tools will undoubtedly aid in the battle against catastrophic climate change.
With this high need in mind, Cognixia has developed this machine learning online training to help professionals upskill in the domain and take advantage of the skills gap. This online, interactive, instructor-led machine learning and deep learning course include hands-on projects and assessments.
Upskill Yourself with Machine Learning Training!
Learn machine learning from Cognixia – the world’s leading digital talent transformation company. Our machine learning certification course covers all the important concepts, libraries, and techniques that would help the learners accelerate their careers in machine learning & deep learning.
Here’s what you will learn with this course –
- Python Basics
- Introduction to NumPy, Pandas, and Matplotlib
- Introduction to Machine Learning with Python
- Basic Statistics
- Supervised and Unsupervised Machine Learning Algorithms
- Reinforcement Learning
- Introduction to Artificial Neural Networks (ANN)
- Fundamentals of Natural Language Processing
