Machine Learning for Real-Time Environmental Monitoring: Heat Index Prediction with Fuzzy Logic

Abstrak

Abstract A fuzzy inference system was developed for predicting the heat index from temperature and relative humidity data. The effectiveness of fuzzy logic in using imprecise mapping of input to output to encode interconnectedness of system variables was exploited to uncover a linguistic model of how the temperature and humidity conditions impact the heat index in a growth room. The developed model achieved an R 2 of 0.974 and a RMSE of 0.084 when evaluated on a test set, and the results were statistically significant (F 1,5915 = 222900.858, p < 0.001). By providing the advantage of linguistic summarization of data trends as well as high prediction accuracy, the fuzzy logic model proved to be an effective machine learning method for heat control problems.

Identifier

• DOI: https://doi.org/10.1088/1742-6596/3139/1/012077
• OpenAlex: https://openalex.org/W4416385296