Coupling of quantile regression into boosted regression trees (BRT) technique in forecasting emission model of PM10 concentration

Air pollution is currently becoming a significant global environmental issue. The sources of air pollution in Malaysia are mobile or stationary. Motor vehicles are one of the mobile sources. Stationary sources originated from emissions caused by urban development, quarrying and power plants and petrochemical. The most noticeable contaminant in the Peninsular of Malaysia is the particulate matter (PM10), the highest contributor of Air Pollution Index (API) compared to other pollution parameters. The aim of this study is to determine the best loss function between quantile regression (QR) and ordinary least squares (OLS) using boosted regression tree (BRT) for the prediction of PM10 concentration in Alor Setar, Klang and Kota Bharu, Malaysia. Model comparison statistics using coefficient of determination (R2), prediction accuracy (PA), index of agreement (IA), normalized absolute error (NAE) and root mean square error (RMSE) show that QR is slightly better than OLS with the performance of R2 (0.60–0.73), PA (0.78–0.85), IA (0.86–0.92), NAE (0.15–0.17) and RMSE (9.52–22.15) for next-day predictions in BRT model

Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better