Design of global climate control based on fuzzy systems with concept of carbon emissions

PLOS One Design of global climate control based on fuzzy systems Fig 14. Response of Surface ocean (𝜉S) with and without fuzzy rules. https://doi.org/10.1371/journal.pone.0333846.g014 Fig 15. Greenhouse’s interior temperature and the fuzzy model’s prediction. https://doi.org/10.1371/journal.pone.0333846.g015 would have trouble capturing abrupt temperature changes. These might be caused by inac- curate sensors, abrupt changes in the environment, or restrictions in the model’s capacity to make predictions. Although the model works well in general, it might need to be improved in order to lessen the error spikes that are shown at specific times. These disparities might be reduced with the use of strategies like better data filtering, better model tuning, or more feature inclusion. Fig 15 shows that the dynamic model accurately forecasts the temperature outside the greenhouse. The data exhibits a sinusoidal pattern, indicating recurring variations that are probably impacted by environmental cycles like exposure to sunlight, meteorological circum- stances, or temperature regulation systems. There are slight variations between the expected PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 20/ 30