Predictive analytics, which combines real-time and historical data to define the probability of a certain event happening, is believed to play a key role in maintaining a healthy environment for the well-being of the current and future generations. Tatyana Korobeyko, Data Strategist, Itransition, shares how predictive analytics can be leveraged to enable greater environmental consciousness and sustainability.
The World Health Organization attributes 24% of all global deaths to environmental risks, such as air pollution, water contamination, land use patterns harmful to the ecosystem, etc. Let us look at examples of how companies turning to predictive analytics advisoryOpens a new window can successfully apply advanced analytics capabilities in the environmental sector.
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Six Key Use-cases Predictive Analytics in Sustainability
1. Deforestation modeling
Online forest monitoring systems like Global Forest WatchOpens a new window are regularly used by governments, the private sector, non-governmental organizations, and the general public to get the most recent information on the forest landscape status and monitor and analyze deforestation patterns. Additionally, this ML-based system uses drones, cameras, satellite images, etc., to send alerts in case any illegal logging activity is detected.Â
Even though such an approach provides valuable insights into deforestation trends and deforestation driving factors in certain areas, it is reactive since the identification of tree cover loss areas happens after the damage is already done. However, conservation groups can do much more when combining this information with historical data (e.g., past deforestation rates), conditions contributing to degradation at a specific place (e.g., infrastructure expansion plans), current carbon stocks, etc. Using well-tuned ML models, they can create deforestation risk maps, predict the regions that are at the highest risk of illegal logging within several months or even years, detect how commodity supply chains impact forest loss and forecast how human activities can affect future carbon emissions, and so on.Â
2. Sustainable product transportation
According to the 2020 climate change report by the US Environmental Protection Agency, product transportation, driven by the burning of fossil fuels, generates the largest share (27%) of greenhouse gas emissions. A key to reducing CO2 emissions, which lies within the companies’ direct control, is the optimization of product transportation processes. Such optimization implies:Â
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- Measuring and monitoring harmful emissions across different transportation modes with IoT devices. Â
- Utilizing predictive analytics models to detect unexpected conditions (weather conditions, inventory shortages, manufacturing promotions, etc.), predict future failure of vehicles and machine components, and forecast shipped volumes and weight for different transportation lanes accordingly.Â
- Operationalizing the discovered insights and building plans based on forecasts, including adjusting shipment patterns and optimizing vehicle flow, providing more efficient navigation, facilitating shared transportation, etc.Â
3. Wildfire forecasting
The European Union’s Copernicus Atmosphere Monitoring Service reports that in 2021, wildfires’ carbon emissions broke records worldwide, amounting to more than 1.7 billion metric tons of CO2. To mitigate the risks and minimize the consequences, scientists are now using predictive analytics software to:Â
- Forecast fire weather and the probability of a fire in a specific area: Since all fires need fuel, oxygen, and heat, and weather influences each of these factors, scientists and data experts generate their wildfire predictions by coupling atmospheric models with fire models. For training and running predictive models, they use atmospheric, terrain, and fuel data, as well as historical weather and wildfire data, satellite imagery, and more. The deployed model helps identify dangerous weather conditions and forecast wildfire potential. These forecasts and predictions are then passed on to utilities, response teams, fire crews, etc., to eliminate fire development triggers (e.g., works on lines that can cause sparks) and plan proper response actions.
- Model wildfire behavior: By combining current and historic fire information, scientists may predict the direction and timing of the fire spread. This way, response crews, public services, utilities, etc., get an advanced warning of where the fire will spread to once it has started, timely evacuate people and livestock from dangerous regions, and efficiently allocate response crews and resources. Â
4. Mineral deposit forecastingÂ
Mining companies are constantly looking for ways to make the mining process more environmentally friendly and ethical as mineral exploration adds significant carbon emissions to our environment. By collecting and combining various kinds of data from the mining site (color sensor data, X-ray data, historical drilling data, etc.), mining companies construct and train ML models to detect the sites with high mineralization potential and the type of minerals with great precision, which considerably reduces harmful emissions.
5. Energy consumption forecasting
Adapting energy production to the fluctuating demand is critically important since energy overproduction inevitably leads to high levels of waste, not to mention skyrocketing storage costs. To generate short-term and long-term energy consumption predictions, energy companies create models using the historical consumption data, weather data, performance data of a particular grid, etc.Â
6. Renewable energy generation predictionÂ
Fossil fuel power stations are responsible for a significant fraction of greenhouse gas emissions. In an effort to become carbon-neutral, many companies are now transitioning to low-carbon alternatives – renewable energy grids. However, the most popular renewable energy sources – solar and wind – have their power output fully dependent on weather conditions (irradiation, wind speed, temperature, humidity, cloud cover, etc.).Â
To deal with the volatility of weather patterns and subsequent power fluctuations, energy companies employ predictive analytics solutions and forecast changes in the energy to be generated in the near future based on historical performance, weather conditions, and other parameters.Â
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Sustainably Forward
These days, environmental sustainability is on the agenda of every company. Some companies are only driven by regulatory requirements or the ability to become environmentally responsible in the eyes of investors and customers, thus putting their environmental sustainability initiatives at the bottom of the list.Â
Others, on the contrary, have already discovered how green initiatives help them cut operating costs and stay ahead of competitors and are turning to technology vendors looking for ways to advance. Being serious about becoming more environmentally responsible in words and deeds, they consider advanced technologies as the key enabler for minimizing the consequences of carbon emissions, damaged ecosystems, and scarce natural resources on the existing value chains and business models.
What sustainability initiatives are you undertaking going forward? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!