Forecasting of daily and next day's particulate matter (PM2.5) concentration levels in Quezon City using models based on adaptive neuro fuzzy inference system (ANFIS) - Moreno, Imee E.; Berana, Menandro S. (Quezon City College of Engineering, University of the Philippines Diliman, 2018)
Advance assessment of the pollutant concentration levels in a busy urban city can provide precautionary measures to the public when appropriate health advice is to be issued. Thus, the aim of this study is to develop models based on adaptive neuro-fuzzy inference system applying the parameters such as meteorological elements, air pollutants such as PM10 pollution episodes, and historical data of PM2.5 concentration levels. The study site chosen for the study is Quezon City because of its vast land area, high population growth, extensive busy road networks, and a large number of commercial establishments. The data used for the analysis are from the city's sole real-time air quality monitoring station and one synoptic weather station. Statistical analyses like multicollinearity test and correlation analysis are applied to assess the relationship among the important parameters and to select only the significant parameters in developing the ANFIS models of the target output variable. The ANFIS model that best forecasted the daily PM2.5 concentration level is the model that used generalized bell shape of the MF for mapping the input values and used back propagation as the learning algorithm. The model has an average testing error (RMSE) of 0.13804, a correlation coefficient of 0.7215, index of agreement of 0.8214, and with the mean absolute and mean bias error of 0.0814 and 0.0138, respectively. The model has used four input variables with three numbers of MF's is used for each variable. Whereas, the best ANFIS model that forecasted the next day's PM2.5 concentration level is the model that used Gaussian shape of the MF with same learning algorithm (back propagation). The model has an average testing error (RMSE) of 0.14429, a correlation coefficient of 0.7405, index of agreement of 0.8264, and with the mean absolute and mean bias error of 0.0966 and 0.0141, respectively. The input variable is the previous day data of PM2.5 concentration level with three number of MF is used. It is good to note, that time step can affect the influence of selected variables to forecast the desired output variable. Thus, present variables can influence more likely the desired variable with the same time step than when the time step of the output variable is ahead. Moreover, the effect of the historical values of the same variable to be forecasted can give an equivalent forecasting power of combined influence of other variables as predictors.
Thesis (Master of Science in Environmental Engineering)--University of the Philippines Diliman
Keywords: Air -- Pollution -- Forecasting