Ozone levels in the empty quarter of Saudi Arabia—application of adaptive neuro-fuzzy model

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.

[1]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[2]  Rich Caruana,et al.  Multitask Learning , 1997, Machine Learning.

[3]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[4]  Renata M. C. R. de Souza,et al.  Fuzzy Kohonen clustering networks for interval data , 2013, Neurocomputing.

[5]  Àngela Nebot,et al.  Ozone prediction based on meteorological variables: a fuzzy inductive reasoning approach , 2008 .

[6]  Gerson Zaverucha,et al.  Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks , 2006, ESANN.

[7]  Laurence R. Rilett,et al.  Spectral Basis Neural Networks for Real-Time Travel Time Forecasting , 1999 .

[8]  Ujjwal Kumar,et al.  A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration , 2011 .

[9]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[10]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[11]  Hiroyuki Watanabe,et al.  Application of a fuzzy discrimination analysis for diagnosis of valvular heart disease , 1994, IEEE Trans. Fuzzy Syst..

[12]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[13]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[14]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[15]  Luís Torgo,et al.  Regression error characteristic surfaces , 2005, KDD '05.

[16]  Aldo Cipriano,et al.  Forecasting ozone daily maximum levels at santiago, chile , 1998 .

[17]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  C. Borrego,et al.  Emission and dispersion modelling of Lisbon air quality at local scale , 2003 .

[19]  Jan Jantzen,et al.  Neurofuzzy Modelling , 1998 .

[20]  Mihaela Oprea,et al.  Comparing statistical and neural network approaches for urban air pollution time series analysis , 2008 .

[21]  Mukesh Khare,et al.  Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways , 2010 .

[22]  Heinrich Braun,et al.  Evolving Neural Feedforward Networks , 1993 .

[23]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[24]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[25]  Mahmut Bayramoglu,et al.  Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak. , 2006, Chemosphere.

[26]  Saleh M. Al-Alawi,et al.  Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks , 2002, Environ. Model. Softw..

[27]  John Yen,et al.  Simplifying fuzzy rule-based models using orthogonal transformation methods , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[28]  Jinbo Bi,et al.  Regression Error Characteristic Curves , 2003, ICML.

[29]  Rich Caruana,et al.  Multitask Learning , 1997, Machine-mediated learning.

[30]  Kostas D. Karatzas,et al.  Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece , 2007, Simul. Model. Pract. Theory.

[31]  Dong-Sool Kim,et al.  A new method of ozone forecasting using fuzzy expert and neural network systems. , 2004, The Science of the total environment.

[32]  Fakhri Karray,et al.  Soft Computing and Tools of Intelligent Systems Design: Theory and Applications , 2004 .

[33]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[34]  Mohamad T. Musavi,et al.  On the Generalization Ability of Neural Network Classifiers , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Zsolt Csaba Johanyák,et al.  Fuzzy Model based Prediction of Ground-Level Ozone Concentration , 2011 .

[36]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[37]  James C. Bezdek,et al.  Fuzzy Kohonen clustering networks , 1994, Pattern Recognit..

[38]  J C M Pires,et al.  Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting , 2012, Environmental Science and Pollution Research.

[39]  S S Huang,et al.  Forecasts Using Neural Network versus Box-Jenkins Methodology for Ambient Air Quality Monitoring Data , 2000, Journal of the Air & Waste Management Association.

[40]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[41]  Peter Vincent,et al.  Saudi Arabia: An Environmental Overview , 2008 .

[42]  S. I. V. Sousa,et al.  Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations , 2007, Environ. Model. Softw..