Prediction of Dissolved Oxygen in Rivers Using a Wang-Mendel Method – Case Study of Au Sable River
Amount of dissolve oxygen in a river has a great direct affect on aquatic macroinvertebrates and this would influence on the region ecosystem indirectly. In this paper it is tried to predict dissolved oxygen in rivers by employing an easy Fuzzy Logic Modeling, Wang Mendel method. This model just uses previous records to estimate upcoming values. For this purpose daily and hourly records of eight stations in Au Sable watershed in Michigan, United States are employed for 12 years and 50 days period respectively. Calculations indicate that for long period prediction it is better to increase input intervals. But for filling missed data it is advisable to decrease the interval. Increasing partitioning of input and output features influence a little on accuracy but make the model too time consuming. Increment in number of input data also act like number of partitioning. Large amount of train data does not modify accuracy essentially, so, an optimum training length should be selected.
[1] W.A. Brock, D.A. Hsieh, B. LeBaron, Nonlinear Dynamics, Chaos and
Instability, MIT Press, Cambridge, MA, USA, 1991.
[2] O. Castillo, P. Melin, Automated mathematical modelling for financial
time series prediction using fuzzy logic, dynamical system theory and
fractal theory, in: Proceedings of CIFEr-96, IEEE Press, New York, NY,
USA, 1996, pp. 120-126.
[3] O. Castillo, P. Melin, A new fuzzy-genetic approach for the simulation
and forecasting of international trade non-linear dynamics, in:
Proceedings of CIFEr-98, IEEE Press, New York, NY, USA, 1998, pp.
189-196.
[4] O. Castillo, P. Melin, Automated mathematical modelling for financial
time series prediction combining fuzzy logic and fractal theory, in: Soft
Computing for Financial Engineering, Springer-Verlag, Heidelberg,
Germany, 1999, pp. 93-106.
[5] O. Castillo, P. Melin, Soft Computing and Fractal Theory for Intelligent
Manufacturing., Springer-Verlag, Heidelberg, Germany, 2003.
[6] H.-C. Fu, Y.-P. Lee, C.-C. Chiang, H.-T. Pao, Divide-and-conquer
learning and modular perceptron networks, IEEE Trans. Neural
Networks 12 (2) (2001) 250-263.
[7] L.K. Hansen, P. Salamon, Neural network ensembles, IEEE Trans.
Pattern Anal. Mach. Intell. 12 (10) (1990) 993-1001.
[8] S. Haykin, Adaptive Filter Theory, 3rd ed., Prentice Hall, 1996.
[9] J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-fuzzy and Soft Computing:
A Computational Approach to Learning and Machine Intelligence,
Prentice Hall, 1997.
[10] B. Lu and M. Ito, Task Decomposition and module combination based
on class relations: modular neural network for pattern classification,
Technical Report, Nagoya, Japan, 1998.
[11] G.S. Maddala, Introduction to Econometrics, Prentice Hall, 1996.
[12] R. Murray-Smith, T.A. Johansen, Multiple Model Approaches to
Modeling and Control, Taylor and Francis, UK, 1997.
[13] D.B. Parker, Learning Logic, Invention Report 581-64, Stanford
University, 1982.
[14] A. Quezada, Reconocimiento de Huellas Digitales Utilizando Redes
Neuronales Modulares y Algoritmos Geneticos, Thesis of Computer
Science, Tijuana Institute of Technology, Mexico, 2004.
[15] S.N. Rasband, Chaotic Dynamics of Non-Linear Systems, Wiley-
Interscience, 1990.
[16] Klir GJ, Yuan B. Fuzzy sets and fuzzy logic: theory and applications.
Englewood Cliffs (NJ): Prentice-Hall; 1995.
[17] Zimmermann HJ. Fuzzy set theory and its applications. 3rd ed. Boston:
Kluwer Academic Publishers; 1996.
[18] Jang, J.S.R., 1993. ANFIS: adaptive network based fuzzy inference
system. IEEE Transactions on Systems, Man and Cybernetics 23 (3),
665-684.
[19] L.X. Wang, J.M. Mendel, Generating fuzzy rules by learning from
examples, IEEE Transactions on Systems, Man, and Cybernetics 22:6
(1992) 1414-1427.
[20] Michigan Department of Natural Resources (MDNR): Forest, Mineral
and fire management division
[1] W.A. Brock, D.A. Hsieh, B. LeBaron, Nonlinear Dynamics, Chaos and
Instability, MIT Press, Cambridge, MA, USA, 1991.
[2] O. Castillo, P. Melin, Automated mathematical modelling for financial
time series prediction using fuzzy logic, dynamical system theory and
fractal theory, in: Proceedings of CIFEr-96, IEEE Press, New York, NY,
USA, 1996, pp. 120-126.
[3] O. Castillo, P. Melin, A new fuzzy-genetic approach for the simulation
and forecasting of international trade non-linear dynamics, in:
Proceedings of CIFEr-98, IEEE Press, New York, NY, USA, 1998, pp.
189-196.
[4] O. Castillo, P. Melin, Automated mathematical modelling for financial
time series prediction combining fuzzy logic and fractal theory, in: Soft
Computing for Financial Engineering, Springer-Verlag, Heidelberg,
Germany, 1999, pp. 93-106.
[5] O. Castillo, P. Melin, Soft Computing and Fractal Theory for Intelligent
Manufacturing., Springer-Verlag, Heidelberg, Germany, 2003.
[6] H.-C. Fu, Y.-P. Lee, C.-C. Chiang, H.-T. Pao, Divide-and-conquer
learning and modular perceptron networks, IEEE Trans. Neural
Networks 12 (2) (2001) 250-263.
[7] L.K. Hansen, P. Salamon, Neural network ensembles, IEEE Trans.
Pattern Anal. Mach. Intell. 12 (10) (1990) 993-1001.
[8] S. Haykin, Adaptive Filter Theory, 3rd ed., Prentice Hall, 1996.
[9] J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-fuzzy and Soft Computing:
A Computational Approach to Learning and Machine Intelligence,
Prentice Hall, 1997.
[10] B. Lu and M. Ito, Task Decomposition and module combination based
on class relations: modular neural network for pattern classification,
Technical Report, Nagoya, Japan, 1998.
[11] G.S. Maddala, Introduction to Econometrics, Prentice Hall, 1996.
[12] R. Murray-Smith, T.A. Johansen, Multiple Model Approaches to
Modeling and Control, Taylor and Francis, UK, 1997.
[13] D.B. Parker, Learning Logic, Invention Report 581-64, Stanford
University, 1982.
[14] A. Quezada, Reconocimiento de Huellas Digitales Utilizando Redes
Neuronales Modulares y Algoritmos Geneticos, Thesis of Computer
Science, Tijuana Institute of Technology, Mexico, 2004.
[15] S.N. Rasband, Chaotic Dynamics of Non-Linear Systems, Wiley-
Interscience, 1990.
[16] Klir GJ, Yuan B. Fuzzy sets and fuzzy logic: theory and applications.
Englewood Cliffs (NJ): Prentice-Hall; 1995.
[17] Zimmermann HJ. Fuzzy set theory and its applications. 3rd ed. Boston:
Kluwer Academic Publishers; 1996.
[18] Jang, J.S.R., 1993. ANFIS: adaptive network based fuzzy inference
system. IEEE Transactions on Systems, Man and Cybernetics 23 (3),
665-684.
[19] L.X. Wang, J.M. Mendel, Generating fuzzy rules by learning from
examples, IEEE Transactions on Systems, Man, and Cybernetics 22:6
(1992) 1414-1427.
[20] Michigan Department of Natural Resources (MDNR): Forest, Mineral
and fire management division
@article{"International Journal of Earth, Energy and Environmental Sciences:52253", author = "Mahmoud R. Shaghaghian", title = "Prediction of Dissolved Oxygen in Rivers Using a Wang-Mendel Method – Case Study of Au Sable River", abstract = "Amount of dissolve oxygen in a river has a great direct affect on aquatic macroinvertebrates and this would influence on the region ecosystem indirectly. In this paper it is tried to predict dissolved oxygen in rivers by employing an easy Fuzzy Logic Modeling, Wang Mendel method. This model just uses previous records to estimate upcoming values. For this purpose daily and hourly records of eight stations in Au Sable watershed in Michigan, United States are employed for 12 years and 50 days period respectively. Calculations indicate that for long period prediction it is better to increase input intervals. But for filling missed data it is advisable to decrease the interval. Increasing partitioning of input and output features influence a little on accuracy but make the model too time consuming. Increment in number of input data also act like number of partitioning. Large amount of train data does not modify accuracy essentially, so, an optimum training length should be selected.
", keywords = "Dissolved oxygen, Au Sable, fuzzy logic modeling, Wang Mendel.", volume = "4", number = "2", pages = "71-8", }
