How Artificial Intelligence Can Solve Climate Change

Climate change is one of the greatest challenges facing humanity. Climate change occurs due to natural variability or a result of human activity. Artificial Intelligence (AI) and Deep Learning (DL) can help us to predict the natural variability in many ways to reduce the impact of climate change. Here we describe how deep learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a climate change. AI can unlock many new insights from the massive amounts of complex climate data generated by the field data from satellite and radar imaging technologies. Our AI based climate change system has six key modules: carbon emissions module, population growth module, energy module, deforestation module, economic module and global atmospheric circulation module.

AI Modules for Climate Change

Advance Artificial Intelligence (AI) techniques like deep reinforcement learning, Monte carol adaptive learning, and self-reflection deep learning techniques are very promising tool to solve climate change challenges.  Traditional machine learning techniques like kNN, K-Means, Random Forest, SVM, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Recursive Neural Networks, Transfer Learning, Long Short-Term Memory (LSTM) are also very useful for certain areas of climate change modeling.

Artificial Intelligence and Climate Modeling

Climate modeling is a complex science. Our AI techniques for climate modeling including NLP, supervised learning, active learning, and various re-generative and hybrid models. Climate models includes mathematical understandings of Earth and climate physics. With the rapid growth of the Internet of Things (IoT), satellite and radar imaging technologies, sensor technologies, and deep learning algorithms of AI,  it is possible to develop climate models, which integrates remote sensing (RS), Geographical Information Systems (GIS), and Global Positioning Systems (GPS).

Our AI based climate change system has six key modules: carbon emissions module, population growth module, energy module, economic module, deforestation module,  and global atmospheric circulation module. They are interlinked at various levels. Carbon emissions module includes heating, ventilation, and air conditioning (HVAC) systems. Carbon emissions requires model of electricity systems, transportation, buildings, industry, and land use.

Precision agriculture can reduce carbon release from the soil and improve crop yield, which in turn reduces the need for deforestation. With satellite images, we can estimate the amount of carbon sequestered in every parcel of land, as well as how much emission it releases.

Population growth and rapid economic development have played a key role in the process of industrial carbon emissions increasing influenced on climate change. Carbon emissions level is affected by fossil fuel combustion emissions, besides, it contributes to environment quality and carbon emissions reduction investment.

You can learn about the Earth by collecting data. To turn that data into useful predictions, you need to condense it into coherent, computationally tractable models. DL models are likely to be more accurate or less expensive than other models where: (1) there is huge data available, but it is hard to model systems with traditional statistics, or (2) there are good models, but they are too computationally expensive to use in production.

Focus Areas of AI For Climate Change

Rise of sea level, clean air and ocean  acidification and warming are the three key areas we are presently focusing.  Around 92% of the world’s people live in places that fail to meet World Health Organization (WHO) air quality guidelines. The WHO has reported that around 7 million people die annually from exposure to air pollution – one death out of every eight globally.

Climate Change Dynamics

The chemistry of the oceans is also changing more rapidly than at any time in perhaps 300 million years, as the water absorbs anthropogenic greenhouse gases. The resulting ocean  acidification and warming are leading to  unprecedented damage to fish stocks and corals.

Oceans are also rising around the world, causing dangerous flooding. Rising seas is one of those climate change effects. Average sea levels have swelled over 8 inches (about 23 cm) since 1880, with about three of those inches gained in the last 25 years. Every year, the sea rises another .13 inches (3.2 mm).

Conclusion:

The 2018 intergovernmental report on climate change estimated that the world will face catastrophic consequences unless global greenhouse gas emissions are eliminated within thirty years.  AI based models can provide climate change models where the results will become more robust and reliable for sustainability policy making.