Revealing the Relationship Between Beehives and Global Warming via Machine Learning

Bees play a critical role in crop pollination, which helps provide food that supports human populations. However, global warming presents several challenges for bees, including difficulty in surviving and reproducing. Temperature changes also impact the timing of plant blooms and food availability for bees, leading to a decline in bee populations. Extreme weather events such as heatwaves, droughts, and heavy rainfall also threaten bees’ survival. As a solution, machine learning has been utilized to help to address these issues by analyzing large amounts of data on weather patterns and temperature. And other factors that impact bee populations. Through this paper, we utilized machine learning algorithms to uncover the relationship between bees and temperature. Moreover, we also aim to see how global warming will affect the future. Three machine learning algorithms were blended for better performance. The results showed that many arrivals could help maintain the optimal humidity and temperature in beehives. Furthermore, the machine learning algorithm could predict the temperature of the beehive with high performance, proving the importance of bees for the beehive condition. Therefore, we could successfully prove the hypothesis about the relationship between the number of bees in the beehive and temperature, which was stated in the existing research. Furthermore, we also figured out that the continuous increase in global temperature due to global warming could make it harder for bees to survive, leading to a decline in the number of bees and affecting the biological system in a vicious cycle.