Fuzzy Inference Approach for PM₂.₅ Modelling with High Accuracy and Low Complexity
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Abstract
PM2.5 is a silent yet severe pollutant that accumulates in the human body, causing long-term health issues such as lung cancer. This study proposes a novel fuzzy inference system (FIS) for PM2.5 forecasting, addressing the nonlinear and dynamic nature of air pollution. Unlike complex, data-intensive black-box models, the proposed FIS is transparent, interpretable, and simple to implement. It uses only two lagged PM2.5 change rates and nine fuzzy rules for accurate prediction. The model requires no geographical or emission-source data, which are often costly and region-specific. Fuzzy rules are derived from natural PM2.5 rise-and-fall patterns, ensuring logical consistency and minimal inputs. Using data from Chiang Mai, Thailand —one of the most polluted cities —the model was benchmarked against MLR, MLP, LSTM, SVM, and Gradient Boosting. The FIS achieved up to 5% higher accuracy. Although the Diebold-Mariano test found no significant difference, FIS showed comparable robustness with 49% fewer parameters and 56% fewer FLOPs. Optimal performance occurred at three input lags and 27 fuzzy rules, balancing accuracy and complexity. Moreover, the Chiang Mai FIS generalized well to other PM2.5-affected cities —Bangkok, Jakarta, and Ho Chi Minh City—without modifications, and maintained reliability for both daily and extended hourly forecasts.
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