Modelling and analysis of ozone episodes

The Prediction of Undersaturated Crude Oil Viscosity: An Artificial Neural Network and Fuzzy Model Approach

“…Tan-sigmoid A fuzzy set is characterized by a membership function f [0, 1], which associates each element with a grade of membership in the fuzzy set (Peton et al, 2000). The main purpose of a fuzzy system is to achieve a set of local input-output relationships describing a process .…”

Spatial and Temporal Analysis of Surface Ozone in Urban Area: A Multilevel and Structural Equation Model Approach

“…Artificial neural network approach is capable of modeling complex nonlinear phenomena, but its main drawback is that it results in a 'black box' model which it isn't easy to interpret or justify. Fuzzy logic also allows one to model complex nonlinear phenomena (Peton, 2000). Since fuzzy logic is based on a set of empirical rules, the inherent cause-effect relationships and interactions among factors of the ozone cannot be flexibly incorporated.…”

“…The model for the CA has the lowest GFI (AGFI) of 0.731 (0.533). Peton (2000) highlights that environmental data usually have some measurement and sampling errors. These errors may due to the disordered operation of measurement equipments, some missing observations, and some very small observed data that fluctuated around the detection limit of monitoring equipments and also sometimes irrelevant measurements.…”

Ozone prediction based on meteorological variables: a fuzzy inductive reasoning approach

“…In recent years other paradigms such as neural networks (NN) (Wieland and Wotawa, 1999;Abdul-Wahab and Al-Alawi, 2002;Wang et al, 2003;Lu, 2006a, 2006b), decision trees or association rules (Wotawa and Wotawa, 2001;Rohli et al, 2003) have been used for the same purpose. It can be found, also, modeling efforts that use fuzzy logic (Peton et al, 2000;Gómez et al, 2003;Onkal-Engin et al, 2004;Ghiaus, 2005) or hybrid NN and fuzzy logic approaches (Morabito and Versaci, 2003;Heo and Kim, 2004;Yildirim and Bayramoglu, 2006). Ozone is the pollutant that has received more attention from the modeling and prediction perspective, due to the harmful effects that cause in humans and its increasing levels in big cities.…”