environment pollution, garbages, bio-degradable and non-bio-degradables, sudden climate changes and many more reasons.

here is a list from Wikipedia :--

• Human overpopulation — Biocapacity climate change • Carrying capacity • Exploitation • Industrialisation • I = PAT • Land degradation • Land reclamation • Optimum population • Overshoot (population) • Population density • Population dynamics • Population growth • Projections of population growth • Total fertility rate • Urbanization • Waste • Water conflict • Water scarcity • Overdrafting

• Hydrology — Environmental impacts of reservoirs • Tile drainage • Hydrology (agriculture) • Flooding • Landslide

• Intensive farming — Agricultural subsidy • Environmental effects of meat production • Intensive animal farming • Intensive crop farming • Irrigation • Monoculture • Nutrient pollution • Overgrazing • Pesticide drift • Plasticulture • Slash and burn • Tile drainage

• Land use — Built environment • Desertification • Habitat fragmentation • Habitat destruction • Land degradation • Land pollution • Lawn-environmental concerns • Trail ethics • Urban heat island • Urban sprawl

• Nanotechnology — Impact of nanotechnology

• Natural disasters

• Nuclear issues — Nuclear fallout • Nuclear meltdown • Nuclear power • Nuclear weapons • Nuclear and radiation accidents • Nuclear safety • High-level radioactive waste management

• Ocean trash • Garbage Patch • Ghost net • Washed Ashore

• Water Pollution

• Air pollution

• land pollution

• sound pollution (will discuss about in sound sector)

we will do the following :-

• AI used for energy

• Making cities more livable and sustainable

• Smart agriculture (part of the agriculture sector)

• Protecting the oceans

• Better climate predictions (part of the agriculture sector)

How AI is used for energy ?

AI is increasingly used to manage the intermittency of renewable energy so that more can be incorporated into the grid; it can handle power fluctuations and improve energy storage as well.

We are also trying to use sound energy produced by sound pollution.(sound sector deals with it)

Making cities more livable and sustainable, how ?

AI can also improve energy efficiency on the city scale by incorporating data from smart meters and the Internet of Things (the internet of computing devices that are embedded in everyday objects, enabling them to send and receive data) to forecast energy demand. In addition, artificial intelligence systems can simulate potential zoning laws, building ordinances, and flood plains to help with urban planning and disaster preparedness. One vision for a sustainable city is to create an “urban dashboard” consisting of real-time data on energy and water use and availability, traffic and weather to make cities more energy efficient and livable.

Smart agriculture, sound amaging right let me assure it's true and how it will all work?

Hotter temperatures will have significant impacts on agriculture as well.

Data from sensors in the field that monitor crop moisture, soil composition and temperature help AI improve production and know when crops need watering. Incorporating this information with that from drones, which are also used to monitor conditions, can help increasingly automatic AI systems know the best times to plant, spray and harvest crops, and when to head off diseases and other problems. This will result in increased efficiency, enhanced yields, and lower use of water, fertilizer and pesticides.

Protecting the oceans , how ?

The Ocean Data Alliance is working with machine learning to provide data from satellites and ocean exploration so that decision-makers can monitor shipping, ocean mining, fishing, coral bleaching or the outbreak of a marine disease. With almost real time data, decision-makers and authorities will be able to respond to problems more quickly. Artificial intelligence can also help predict the spread of invasive species, follow marine litter, monitor ocean currents, keep track of dead zones and measure pollution levels.

Better climate predictions , you guessed it right ?

As the climate changes, accurate projections are increasingly important. However, climate models often produce very different predictions, largely because of how data is broken down into discrete parts, how processes and systems are paired, and because of the large variety of spatial and temporal scales. The Intergovernmental Panel on Climate Change (IPCC) reports are based on many climate models and show the range of predictions, which are then averaged out.

Averaging them out, however, means that each climate model is given equal weight. AI is helping to determine which models are more reliable by giving added weight to those whose predictions eventually prove to be more accurate, and less weight to those performing poorly. This will help improve the accuracy of climate change projections.

AI and deep learning are also improving weather forecasting and the prediction of extreme events. That’s because they can incorporate much more of the real-world complexity of the climate system, such as atmospheric and ocean dynamics and ocean and atmospheric chemistry, into their calculations. This sharpens the precision of weather and climate modeling, making simulations more useful for decision-makers.