For those of us with a slight inclination about mathematics and statistics, climate change is
measurable and as such, it’s been proven to be very
Yes, the boundaries within which climate change occurs can be blurry but when we look at the concentration of CO2 in the air, or when we look at the rates of deforestation, we are acutely aware of how bad the environment is changing.
Over the centuries, we’ve polluted and misused Natural Resources which is bringing about grave consequences. However, Data Science and Machine Learning are widely being used to support our efforts in measuring and fighting back against common climate change.
In what follows, I’ll go over a number of examples
1: Climate Change Prediction
The easiest way for machine learning and statistics to help climate change is by aiding prediction. Climate prediction reporting, weather models, and various other models are built using historical data and as many parts of our environment have been measured over a period of time, data exists for us to run models on.
The reports from these experiments are vital for various governments, charities and non-profits to best allocate resources so it’s imperative that the results are accurate. Data Science and Machine Learning specifically aim to model the
variance (amongst other things), so over the past 10 years as computational power and data storage/recording techniques have improved, there’s been a flood of new information about the environment.
2: Renewable Energy Optimisation
Solar, Wind and Water energies are fantastic options for renewable energies. They’re not costly and also not very difficult to harness. Using a number of data science techniques, different algorithms can be implemented so that processing a huge amount of data is easy and can lead to insights that can really make an impact on achieving sustainability goals.
Different Solar, Hydro and Wind Power Plants have been set up throughout the world and using data science, these plants are monitored and operated.
Bearing this in mind, the cost of producing renewable electricity is becoming cheaper every year and its only time for the optimisation models to prefer renewable energy over traditional sources.
AI, Machine Learning and Renewable Energy - Pexapark
it's telling that those were the three fields that Bill Gates advised college graduates to enter into when asked in May…
3: Carbon Emission Measurement
Managing the amount of carbon a company or entity produces is a big issue. Firstly, it’s near impossible to measure it exactly (where do you draw the line?) but also, what constitutes your responsibility. If you take the tube to work, should you offset the carbon produced there, or, should the tube operator?
A number of data science methods exist now which help in this endeavour. The systems can now implement carbon trackers which gathers data on the amount of carbon emission from different factories.
Now any data you collect in this space will be slightly noisy so various methods exist that try to model the correct distribution and then aim to minimise the amount being produced.
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4: Water Conservation
Despite the earth being mostly water, the amount we can actively drink and use is significantly less so we need to focus on the conservation of water. However, it’s apparent that capitalist ideals often don’t fall in line with what society needs.
Take the example of Avocados in Mexico. This ‘green gold’ requires a significant amount of fresh water and as such, farmers provide this. However, the towns near these farms end up going through periods of drought: farmers prefer their crops to be watered rather than other humans.
OK maybe that’s a bit facetious but the point stands — sustainable water provisions need to be more widely available and that’s where Data Science can come in.
A study has shown that an average American Household consumes about 320 gallons of water per day.
Using Smart Irrigation System, enabled by data science techniques, can save up to 8800 gallons water per home per year.
Smart irrigation model predicts rainfall to conserve water
A predictive model combining information about plant physiology, real-time soil conditions and weather forecasts can…
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Last year, Cape Town, South Africa, came within days of running out of water. This summer, Chennai, India, ran dry…
5: Heat Energy Conservation
NEST thermostats don’t just look cool — they offer a service to consumers that makes their lives both easier and more sustainable.
Nest Learning Thermostat
Learns your schedule, programs itself, and uses Wi-Fi to connect to your phone. It's better than smart. It's…
These thermostats are expensive but the use of Smart Thermostats can reduce minimum 10% energy on heating and 15% energy on cooling.
Smart Thermostats and Big Data: How Much Will They Help Consumers This Winter? | Articles | Big…
As colder weather approaches, cost-conscious consumers are starting to make plans to reduce the likelihood of…
If that’s not a fantastic use of Data Science; I’m not sure what is.
6: Extreme Weather Forecasting and Preventive Measures
Extreme weather forecasting is notoriously difficult. Often, these events are mathematically so unlikely but even then, they occur in new ways that catches everyone off guard.
Given that, the use of data from highly affected areas can be used to model the boundaries within which extreme weather occurs. Is it dependent on X or Y?
The awesome thing is that now, we spend so much time measuring various parts of the environmental ecosystem that our understanding of what drives these events improves every day. This technology, as you guessed, can save thousands of lives (easily).
As an example, the Montreal Institute of Learning Algorithms have been using GAN’s (General Adversarial Network) to model what it would look like if severe weather storms effected previously ‘safe’ areas.
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Using an advanced form of deep learning, researchers created a computer system that learned how to accurately predict…
There’s so much to read and understand in the world of machine learning but it’s great to see how it’s being used.
The environment really needs as much help as we can throw its way and thankfully, one day at a time, Data Science is helping to solve the problems facing our existence.
I’m particularly excited about developments on measuring CO2 emissions, along with improvements in extreme weather forecasting. To me, these two problems are vital in ensuring that our world remains stable, so we’ll just have to wait and see how we do.
Hopefully you guys found this interesting and keep in touch!