The global warming crisis is worsening, and machine learning models could help solve it. (Image Credit: The DigitalArtist/pixabay)
Our world is faced with a great predicament affecting daily lives: climate change. We've already seen incredibly drastic effects, including severe weather, rising sea levels, melting ice caps, hurricanes, droughts, wildfires, world-breaking temperatures, and more rainfall accumulation in certain places, while others see less. For example, California has suffered from more wildfires over the past decade, and one-third of Pakistan was left underwater due to massive flooding. Meanwhile, the worst drought in 1000 years hit Spain and Portugal. France and other European countries suffered from intense forest fires. Then, just recently, Dhaka saw the hottest day in 58 years during a heatwave, with temperatures rising to 40.4°C. Of course, these events are becoming more widespread and intense, causing dire effects, posing a risk to public health and safety, and exacerbating drought and wildfires.
Humanity must mitigate climate change by keeping more greenhouse gasses out of the atmosphere. Thankfully, technologies like machine learning, deep learning, artificial intelligence, and data science can help us on the road to recovery.
Artificial intelligence, machine learning, deep learning, and data science have the potential to predict future weather events. (Image Credit: geralt/pixabay)
Various machine learning technologies can predict extreme precipitation. So far, climate models have made accurate weather predictions and climate patterns. But they still need more work for precise extreme precipitation predictions. So to predict the frequency in which a region gets hit with extreme rainfall or snowfall, data related to greenhouse gas emissions and climate change projections are inputted into a machine learning technique.
According to Dr. David Novak at NOAA's Weather Prediction Center, extreme rainfall typically has atmospheric disturbance and high moisture commonly found in a winter storm, tropical cyclone, or a warm/cold front. In that case, those conditions increase the chances of extreme rainfall occurring the longer they remain in a certain region. Plus, warmer air is basically moisture-ridden, which also means that a warm climate can produce extreme rainfall. So humanity should develop predictive machine learning models to forecast extreme rainfall by conducting hypothesis testing around warmer air and moisture, verifying the relationship between warm air, moisture, and precipitation. This concept can be applied to greenhouse gases, which contribute to a warmer atmosphere since it absorbs surface infrared radiation. Hypothesis testing can also help researchers understand the formation of greenhouse gases, allowing them to use that data to create machine-learning models.
Extreme rainfall can be predicted by machine via hypothesis testing. (Image Credit: Inge Maria/Unsplash)
Those machine learning models have the potential to predict the severity and frequency of droughts, flooding, wildfires, and landslides by associating precipitation and temperature with a certain hazard event. That can then be used to predict the process in which that hazard's frequency and severity change under future events. Monitoring ecosystems for potential impacts can be achieved through predictive modeling as well. In such circumstances, it can track species populations and determine how coral bleaching differs under varying environmental factors.
Machine learning can help predict the severity and frequency of wildfires in certain regions around the world. (Image Credit: Mike Newbry/Unsplash)
Machine learning can also make an impact by improving systems to consume resources in the best way possible. In one case, automated electrical grids predict and track energy supply and demand for energy generation optimization. Traffic data can then be used by machine learning to predict how much energy electric cars would require when charging the next night. That idea could help decrease the urban island heat effect through machine learning for urban planning optimization. Infrastructure and vegetation cover must be considered. In addition, machine learning models can simulate carbon sequestration and its impact. That can then lead to additional smart carbon capture systems.
Unfortunately, there are some limitations when it comes to using machine learning to tackle climate change. For example, errors may pop up even though high-quality measuring tools and automation make data collection more precise. Leaving those issues unsolved could cause the results produced by machine learning to become invalid. Sure, we can compare that data with previously collected datasheets, but that still doesn't prevent any errors. Inherent bias is also a point of concern during data collection. Since machines are taught based on input data, they simply cannot take in other factors outside those datasets. So it won't be included in the trained model.
Additionally, machine learning models may not have enough data. That's because satellite data has been around for under sixty years, so environmental machine-learning models can only learn from weather events that occurred in the past few decades. That means it cannot rely on datasets from interglacial periods or ice ages to predict any environmental changes under extreme weather. As a result, machine learning models have a higher chance of unsuccessfully validating feedback loops and relationships for other situations.
Therefore, machine learning techniques that use today's datasets aren't ideal for long-term weather predictions. We also must take environmental changes that will occur over centuries into account. Even then, some datasets aren't available for certain scenarios. In one example, researchers mapped under 25% of worldwide ocean floors. When the mapping reaches 100%, then the management of conservation and fisheries could start to see improvement.
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