A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

Abstract

A prognostic approach is proposed based on a fuzzy-logic model to estimate suspended dust concentrations, related to PM 10 , in a specific residential area in Kuwait with high traffic and industrial influences. Seven input variables, including four important meteorological parameters (wind speed, wind direction, relative humidity and solar radiation) and the ambient concentrations of three gaseous pollutants (methane, carbon monoxide and ozone) were fuzzified using a sytem with a graphical user interface (GUI) and an artificial intelligence-based approach. Trapezoidal membership functions with ten and fifteen levels were employed for the fuzzy subsets of each model variable. A Mamdani-type fuzzy inference system (FIS) was developed to introduce a total of 146 rules in the IF-THEN format. The product (prod) and the centre of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed FIS. The results obtained using uzzy-logic were compared with the outputs of an exponential regression model. The predictive performances of the models were compared based on various descriptive statistical indicators, and the proposed method was tested against additional observed data. The prognostic model presented in this work produced very small deviations from the actual results, and showed better predictive performance than the other model with regard to forecasting PM 10 levels, with a very high determination coefficient of over 0.99.

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

“…Yetilmezsoy and Abdul-Wahab [88] utilized CH 4 , CO, wind speed and direction, relative humidity and solar radiation as the input variables of fuzzy and linear regression techniques for the estimation of PM 10 in Khaldiya, Kuwait. The fuzzy method outperformed the linear technique (MLR: R 2 = 0.756; Fuzzy technique: R 2 = 0.997).…”

Particulate Air Pollution at Schools: Indoor-Outdoor Relationship and Determinants of Indoor Concentrations

“…Air quality is deteriorating in several developing countries (Naerher et al, 2000;Yetilmezsoy and Abdul-Wahab, 2012;Abdul-Wahab et al, 2015). According to the World Health Organization (WHO) report published in 2014 (WHO, 2014), many cities in developing countries are currently in breach of WHO guidelines on air pollution levels.…”

New explicit formulations for accurate estimation of aeration-related parameters in steady-state completely mixed activated sludge process

“…Other descriptive performance indices such as PSE and FV also revealed that the proposed empirical models gave very small residuals and demonstrated a noticeable estimation performance on forecasting of the studied the aeration-related parameters. Furthermore, the DW statistics (DWmodel-1 = 1.969, DWmodel-NEW EXPLICIT FORMULATIONS FOR ACCURATE ESTIMATION OF AERATION-RELATED PARAMETERS 155 2 = 1.892, and DWmodel-3 = 1.943) were determined to be very close to 2, indicating the goodness of fit of the derived models (Yetilmezsoy, 2016;Yetilmezsoy and Abdul-Wahab, 2012;Hewings et al, 2002).…”

“…Moreover, the p-value is used as a useful tool to check the significance of each of the coefficients. The variable with the larger tratio and with the smaller p-value is considered as the more significant parameter in the regression model (Yetilmezsoy and Sapci-Zengin, 2009;Yetilmezsoy and Sakar, 2008;Yetilmezsoy and Abdul-Wahab, 2012). It is also noted that values that yield Prob(t) factors (or p-values) of greater than 0.9 may be neglected until all remaining factors are calculated at once (Fingas and Fieldhouse, 2009).…”