Application of computational intelligence techniques to forecast daily PM10 exceedances in Brunei Darussalam

Özet

Particulate matter (PM10) is the pollutant causing exceedances of ambient air quality thresholds, and the keyindicator of air quality index in Brunei Darussalam for haze related episodes caused by the recurrent biomassfires in Southeast Asia. The present study aims at providing suitable forecasts for PM10 exceedances to aid inhealth advisory during haze episodes at the four administrative districts of the country. A framework based onrandom forests (RFs), genetic algorithm (GA) and back propagation neural networks (BPNN) computationalintelligence techniques has been proposed in which the final prediction is made by the BPNN model. A hybridcombination of GA and RFs is initially applied to determine optimal set of inputs from the initial data sets oflargely available meteorological, persistency of high pollution levels, short and long term variations of emissionsrates parameters. The inputs selection procedure does not depend on the back propagation training algorithm.The numerical results presented in this paper show that the proposed model not only produced satisfactoryforecasts but also consistently performed better via several statistical performance indicators when comparedwith the standard BPNN and GA optimisation based on back propagation training algorithm. The model alsoshowed satisfactory threshold exceedances forecasts achieving for instance best true predicted rate of 0.800,false positive rate of 0.014, false alarm rate of 0.333 and success index of 0.786 at Brunei-Muara districtmonitoring station. Overall, the current study has profound implications on future studies to develop a real-timeair quality forecasting system to support haze management.