Journal
JOURNAL OF HAZARDOUS TOXIC AND RADIOACTIVE WASTE
Volume 20, Issue 4, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)HZ.2153-5515.0000270
Keywords
Artificial neural network; Decision tree; Prediction; Meteorology; Traffic intersection; Air pollution; Ambient air quality
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The present study attempts to predict hourly ozone (O-3) and oxides of nitrogen (NOx) concentrations near a traffic intersection in megacity Delhi, India, using artificial neural network (ANN) with the Levenberg-Maquardt (LM) algorithm and decision tree algorithms [e.g., reduced error pruning tree (REPTree) and M5 P tree model]. The hourly averages of input variables of meteorological, traffic volume, and transport emissions along with target values of monitored ambient air concentrations of O-3 and oxides of nitrogen NOx were used for model development. The LM, REPTree, and M5 P algorithm models were developed by training, validation, and testing of input and target data. Statistical agreement between observed and predicted values is assessed by coefficient of correlation (CC), mean square error (MSE), root mean square error (RMSE), normalized mean square Error (NMSE), and Nash-Sutcliffe efficiency index (N-S Index). Results show that the performance of the M5 P model is superior to ANN and REPTree models studied for prediction of O-3 and NOx at a highly urbanized traffic intersection. (C) 2015 American Society of Civil Engineers.
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