In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO2 concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3. 5 ppbv and 0. 99, and 8. 9 ppbv and 0. 95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.
Bibliographical noteFunding Information:
Acknowledgments The authors would like to gratefully acknowledge the support of King Fahd University of Petroleum & Minerals in conducting this research.
- Fuzzy C-means
- Ozone modeling
- Subtractive clustering
ASJC Scopus subject areas
- Environmental Chemistry
- Health, Toxicology and Mutagenesis