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Big data and machine learning are two of the hottest topics in the tech industry right now. Big data is a term used for data sets so large and complex that traditional data processing techniques are not sufficient. Machine learning is a method of teaching a computer to learn from data without explicitly programming it.
Big data and machine learning are being used to solve various ecological problems. For example, it has been used to develop better methods for predicting and detecting environmental hazards such as oil spills.
It is also used to track the spread of invasive species and to understand the impact of climate change on ecosystems. The potential applications of big data and machine learning are enormous and are already beginning to transform the field of ecology.
These cutting-edge technologies will play an increasingly important role in protecting and preserving our planet, helping us adapt to climate change. The problem is that individual efforts to reduce our carbon footprint can only go so far. takes. Technology may be our salvation (most of the time).
Here are some of the ways big data and machine learning are being used to make a positive impact on the environment.
smarter farming
The world population is projected to reach 8.5 billion by 2030 and 9.7 billion by 2050.
The Food and Agriculture Organization of the United Nations estimates that food production will need to increase by 70% to keep up with demand.
The increase in demand is due to several factors such as population growth, changing diets and loss of agricultural land.
Meeting the needs of the world’s population requires a concerted effort by farmers, policy makers and consumers. And that’s where technology comes in to help.
Farmers are already using big data and machine learning to develop more efficient and sustainable farming practices. Efficiency is key here. Food production (animals and crops) is one of the main contributors to greenhouse gases. In addition to expanding water resources used for food and meat production.
By collecting data on weather conditions, soil quality and yields, farmers can better understand their land and how to manage it. Farmers can use this information to develop more efficient irrigation systems, optimize pesticide use, and predict yields. All of this can lead to reduced water and chemical usage and reduced greenhouse gas emissions.
wait, there’s more—Moore’s law.
According to Intel co-founder Gordon E. Moore, Moore’s Law states:
“The number of transistors on a microchip doubles about every two years, but the cost of computers halves.”
In short, computing power gets cheaper every year. Just think of your smartphone. It’s packed with technology that, just a few years ago, would have lost him an arm and a leg, and possibly one of his kidneys.
Now imagine all the wonderful and powerful technology in the hands of farmers. And not just the wealthy (they are in some of the more advanced). I’m talking about an ordinary farmhouse in rural America, a paddy field in Asia, or the highlands of Africa.
The applications and opportunities are endless.
Over time, technology will get better and cheaper, enabling farmers around the world to practice more climate-friendly farming. Communities are more resilient to drought and famine. Crop yields grow while maintaining biodiversity. Using less water means more resources are put to better use.
Addressing food waste
Big data and machine learning are offering new ways to tackle the food waste problem. Food waste accounts for about 8% of global greenhouse gas emissions, according to a recent study.
It is estimated that one-third of the food produced is not consumed.
This is a huge opportunity for businesses and organizations to reduce their environmental impact and save money. Big data and machine learning are used to identify food waste patterns and develop strategies to reduce them.
For example, one study used machine learning to analyze over 2,000 recipes and found that many common ingredients can be replaced with leaner alternatives. This kind of research helps identify new ways to reduce food waste and make food systems more efficient.
A better battery storage solution
The next destination is energy, the grandfather of progress and innovation. There are others, but right now they’re cutting electricity everywhere and things come to a halt pretty quickly.
The World Energy Council predicts that energy demand will increase as the world’s population grows to about 9.7 billion by 2050. The WEC forecasts that primary energy demand will grow by about 1.8% annually between now and 2030.
Although this growth rate has slowed compared to previous decades, it still represents a significant increase in absolute terms. Of course, meeting this increased demand will require a significant increase in energy production.
Again, we are lucky. The solution is right there… literally in our faces. Renewable energy sources such as the sun and wind.
Renewable energy accounts for just over 30% of the world’s primary energy supply, which is projected to grow to almost 50% by 2030 and nearly 90% by 2050.
In other words, renewable energy is expected to play an increasingly important role in meeting the world’s energy needs over the coming decades. But there is a problem.
The sun continuously emits 173,000 terawatts (trillion watts) of energy, enough to power all of our needs more than 10,000 times more, but collecting that energy saving as is still a problem.
We’ve gotten really good at capturing solar energy, and the technology continues to advance. (Read about the latest developments and breakthroughs in solar here.)
One of the biggest challenges facing renewable energy is storage.
When the sun isn’t shining or the wind isn’t blowing, we need to store energy in other ways for later use. That’s where big data and machine learning come into play.
By analyzing large data sets, scientists and engineers can develop better battery technology and discover new materials that can store more energy for longer.
These advances are essential to making renewable energy a viable long-term solution for powering the world.
automatic recycling system
Recycling has come a long way in recent years thanks to advances in big data and machine learning.
A University of Nebraska-Lincoln study found that these technologies are helping streamline the sorting process and improve the overall efficiency of recycling facilities. By analyzing data from sensors and cameras, Machine learning algorithms can identify different types of materials and classify them accordingly.
This saves time and labor costs, reduces pollution levels and improves the quality of recycled materials.
Additionally, big data is being used to track global trends in recycling and develop new ways to reduce waste. For example, a recent study by IBM found that analyzing large datasets can identify materials that are most likely to be reused or recycled. This information is then used to create a more efficient recycling program that reduces material waste and operating costs.
Improved public transport options
Transportation accounts for over 25% of greenhouse gas emissions. Fortunately, avoiding the worst of climate change requires rethinking and restructuring transportation.
We discussed public transport here. But, as I mentioned in that post, public transit has issues like predictability, convenience, and cost that commuters don’t want.
Public transport is smarter and more efficient than ever with the help of big data and machine learning.
By collecting data on traffic patterns and commuter behavior, transit agencies can adjust routes and schedules in real time to get people where they need to be in the most efficient way. air.
As more smart cities come online, we’re starting to taste the edge of what’s possible. increase. And on time!
Reduction of waste in manufacturing
The manufacturing industry accounts for the majority of the world’s greenhouse gas emissions. But manufacturers are using big data and machine learning to develop efficient processes that reduce waste and pollution.
For example, by analyzing data on material usage and production timelines, manufacturers can find ways to cut unnecessary steps, reduce material waste, and streamline production. All of this leads to a smaller environmental impact.
Leveraging the vast amount of data generated by industrial equipment and processes, manufacturers can gain insight into areas such as product quality, production line efficiency and maintenance requirements. Machine learning is then used to automate decisions and actions based on these insights to improve performance and reduce costs.
In addition, manufacturers are using big data and machine learning to develop predictive maintenance models that identify potential problems before they become more serious problems.
Protecting endangered species
The world is in danger of extinction and species are disappearing at an alarming rate. (For example, birds.)
One of the biggest challenges in protecting endangered species is gathering reliable data on their populations and habits. Traditional methods such as surveys and census are time consuming, costly and often produce inaccurate results.
However, big data and machine learning have provided new ways to collect and analyze data on endangered species. For example, sensors can be used to automatically count animals passing through an area, or track animal movements with GPS tags.
The data is then processed using machine learning algorithms to identify patterns and trends that are difficult to detect using traditional methods. As a result, big data and machine learning will play a key role in conservation efforts, helping us better understand and protect endangered species.
Conclusion
Big data and machine learning are not just developing new consumer technologies and improving business processes, they are changing the world as we know it. These cutting-edge technologies are also playing an important role in saving the planet.
From automating recycling processes to developing more efficient farming practices, big data and machine learning are being used in many ways to have a positive impact on the environment.
This is just the beginning. As these technologies continue to evolve, so do their abilities to help create a cleaner and brighter future for our planet.
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