Application of computational intelligence techniques to forecast daily PM 10 exceedances in Brunei Darussalam

Abstract Particulate matter (PM10) is the pollutant causing exceedances of ambient air quality thresholds, and the key indicator of air quality index in Brunei Darussalam for haze related episodes caused by the recurrent biomass fires in Southeast Asia. The present study aims at providing suitable forecasts for PM10 exceedances to aid in health advisory during haze episodes at the four administrative districts of the country. A framework based on random forests (RFs), genetic algorithm (GA) and back propagation neural networks (BPNN) computational intelligence techniques has been proposed in which the final prediction is made by the BPNN model. A hybrid combination of GA and RFs is initially applied to determine optimal set of inputs from the initial data sets of largely available meteorological, persistency of high pollution levels, short and long term variations of emissions rates 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 satisfactory forecasts but also consistently performed better via several statistical performance indicators when compared with the standard BPNN and GA optimisation based on back propagation training algorithm. The model also showed 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 district monitoring station. Overall, the current study has profound implications on future studies to develop a real-time air quality forecasting system to support haze management.

[1]  Jean-Michel Poggi,et al.  PM10 forecasting using clusterwise regression , 2011 .

[2]  Visibility and Incidence of Respiratory Diseases During the 1998 Haze Episode in Brunei Darussalam , 2003 .

[3]  Liyanage C. De Silva,et al.  Evaluation of national emissions inventories of anthropogenic air pollutants for Brunei Darussalam , 2016 .

[4]  Statistical Estimation of Dose-response Functions of Respiratory Diseases and Societal Costs of Haze-related Air Pollution in Brunei Darussalam , 2003 .

[5]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[6]  Mohammad Iskandar bin Pg Hj Petra,et al.  Influence of Southeast Asian Haze episodes on high PM10 concentrations across Brunei Darussalam. , 2016, Environmental pollution.

[7]  Paola Zuccolotto,et al.  Variable Selection Using Random Forests , 2006 .

[8]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[9]  J. Kukkonen,et al.  Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki. , 2011, The Science of the total environment.

[10]  Kostas D. Karatzas,et al.  Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece , 2007, Simul. Model. Pract. Theory.

[11]  M. Radojević,et al.  Air quality in Brunei Darussalam during the 1998 haze episode , 1999 .

[12]  Christos Zerefos,et al.  Forecasting peak pollutant levels from meteorological variables , 1995 .

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  Mikko Kolehmainen,et al.  Evolving the neural network model for forecasting air pollution time series , 2004, Eng. Appl. Artif. Intell..

[15]  Georgios Grivas,et al.  Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece , 2006 .

[16]  J. Chow,et al.  A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile , 2008 .

[17]  Gary King,et al.  Amelia II: A Program for Missing Data , 2011 .

[18]  Mohamed Medhat Gaber,et al.  GARF: Towards Self-optimised Random Forests , 2012, ICONIP.

[19]  Davor Z Antanasijević,et al.  PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization. , 2013, The Science of the total environment.

[20]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[21]  M. Radojević,et al.  Chemical characterisation of the haze in Brunei Darussalam during the 1998 episode , 2000 .

[22]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[23]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[24]  Luca Scrucca,et al.  GA: A Package for Genetic Algorithms in R , 2013 .

[25]  P. Goyal,et al.  Development of artificial intelligence based NO 2 forecasting models at Taj Mahal, Agra , 2015 .

[26]  Stefan Fritsch,et al.  neuralnet: Training of Neural Networks , 2010, R J..

[27]  Yang Zhang,et al.  Real-time air quality forecasting, part I: History, techniques, and current status , 2012 .

[28]  S. I. V. Sousa,et al.  Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations , 2007, Environ. Model. Softw..

[29]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[30]  Giorgio Corani,et al.  Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning , 2005 .

[31]  Ahmad Zia Ul-Saufie,et al.  Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA) , 2013 .