How can machine learning save the environment?

It is essential to tackle any environmental problem before it escalates. And what better way to achieve this than by using current technology? Therefore, this article addresses the critical topic: “How can machine learning save the environment?

1. Intelligent pollution control

Pollution is a big problem in big cities. And a smart urban pollution control system based on IoT and machine learning is urgently needed!

City pollution data, such as car emissions, pollen levels, airflow direction, weather, traffic levels, etc., is received from many sources through the IoT. Following the collection of all relevant data, the machine learning algorithms automatically evaluate the data while modifying the appropriate prediction models based on many aspects such as the current season, various city topologies, etc. Using this research, machine learning algorithms can create pollution projections for different parts of the city, giving city officials advance notice of where a problem might be developing.

2. Intelligent wildlife protection

Many wild creatures are endangered or extinct in various countries. Therefore, it is also our duty to ensure that these species are protected in their original habitats, ensuring that the wild woods and grasslands remain as they are.

WildTrack is a company specialized in this field. They use a fingerprint identification technique (FIT) in conjunction with IoT and ML algorithms to determine an animal’s species, age and sex based on its distinctive fingerprint. Then, this unique data can be used to identify particular patterns related to animal migrations, male/female ratios, species population, etc., which contributes to the preservation of many endangered species.

3. Smart disaster prediction and response

Many natural disasters, such as hurricanes, flash floods, and earthquakes, as well as man-made disasters, such as oil spills, can be predicted using machine learning.

An earthquake detection system that uses deep learning networks is a prime example. It was created by experts from Harvard and Google and can anticipate aftershocks following a large earthquake. This technique works by identifying patterns in seismic data that were previously difficult to assess with existing technologies. Various elements, including soil composition, seismic plate contact, energy transmission through the soil, etc., can influence the likelihood of aftershocks.

4. Smart farming practices

There is enough food to meet everyone’s needs, but not everyone’s greed. Humans, on the other hand, are greedy. Nearly 815 million people in the world are hungry. This equates to one in ten people. And the majority of these people live in developing or underdeveloped countries. Machine learning can be used to alleviate the problem of malnutrition by combining contemporary agricultural technology with agricultural production to increase crop yields and reduce hunger.

5. Smart electric vehicles

In your city, do you inhale more carbon dioxide than oxygen!! If so, one of the main culprits is air pollution caused by cars. And smart electric vehicles are a “smart” way to save the environment and also our lungs! These vehicles will run on electricity, which will reduce air pollution in cities. It is estimated that a conventional gasoline-powered automobile emits twice as much carbon as an electric vehicle.

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Sherry J. Basler