A New Approach of Fuzzy Methods for Evaluating of Hydrological Data
The main criteria of designing in the most hydraulic
constructions essentially are based on runoff or discharge of water. Two of those important criteria are runoff and return period. Mostly,
these measures are calculated or estimated by stochastic data.
Another feature in hydrological data is their impreciseness.
Therefore, in order to deal with uncertainty and impreciseness, based
on Buckley-s estimation method, a new fuzzy method of evaluating hydrological measures are developed. The method introduces
triangular shape fuzzy numbers for different measures in which both
of the uncertainty and impreciseness concepts are considered. Besides, since another important consideration in most of the
hydrological studies is comparison of a measure during different
months or years, a new fuzzy method which is consistent with special form of proposed fuzzy numbers, is also developed. Finally, to
illustrate the methods more explicitly, the two algorithms are tested on one simple example and a real case study.
[1] Abolpour, B., Javan, M., Karamouz M.. 2007. Water
allocation improvement in river basin using adaptive
Neural Fuzzy Reinforcement Learning approach, Applied
Soft Computing 7, pp. 265-285.
[2] Agostino, R.B. D and Stephens, M.A., Eds. 1986.
Goodness-of-Fit Techniques, Marcel Dekker.
[3] Bankert, R., Hadjimichael, M. and Hansen, B. 2001.
Fuzzy logic in Environmental Sciences (http://
www.chebucto.ns.ca/Science/AIMET/fuzzy_environment/.
[4] Bardossy, A., Bronstert, A., and Merz, B. 1995. "1, 2, and 3 Dimensional Modeling of Water Movement in the
Unsaturated soil Matrix Using a Fuzzy Approach". Adv.
Wat. Resour. 18, pp. 237-251.
[5] Buckley, J.J., Eslami, E., 2003a. Uncertain probabilities I: the discrete case, Soft Computing, 7, pp. 500-505.
[6] Buckley, J.J., Eslami, E., 2003b.Uncertain probabilities
II: the continuous case, Soft Computing, 7, pp. 500- 505.
[7] Buckley, J.J., 2005. Fuzzy Statistics: hypothesis testing,
Soft Computing 9, pp. 512-518.
[8] Filliben, J.J. 1975. "The Probability Plot Correlation
Coefficient Test for Normality," Technometrics, 17, 111.
[9] Fontane, D.G., Timothy, K.G., and Moncado, E., 1997.
Planning reservoir operation with imprecise
objectives. Journal of water resources planning and
management. ASCE, 123, pp. 154-162.
[10] Jacquin, A. and Shawseldin, A. 2006. "Development of
Rainfall-Runoff Model Using Takagi-Sugeno, fuzzy
inference system". Journal of Hydrology, vol. 329, pp.
154-173.
[11] Kang, M.S., Kang, M.G., Park, S.W., Lee, J. J., Yoo,
K.H., 2006. Application of grey model and artificial
neural networks to flood forecasting. Journal of the
American water resources association, vol. 42, pp. 473-
489.
[12] Khan, E., 1999. Neural fuzzy based intelligent systems
and applications, in: Jain, L.C., and Martin, N.M. (Eds.),
Fusion of Neural Networks, Fuzzy Sets, and Genetic
Algorithms: Industrial Applications. CRC Press,
Washington, DC, p. 331.
[13] Lilliefore, H.W. 1967. "On the Kolmogorov−Smirnov
Test for Normality with Mean and Variance
Unknown," Journal of the American Statistical
Association, 62, pp. 399-402.
[14] Mathon, Bree R., Ozbek, Metin M., Pinder, George F.,
2008. Transmissivity and storage coefficient
estimation by coupling the Cooper-Jacob method and
modified fuzzy least-squares regression. Journal of
Hydrology vol. 353, pp. 267- 274.
[15] May, D., Sivakumar, M. 2008. Comparison of artificial
neural network and regression models in the Prediction
of urban storm water quality. Water environment
research, vol. 80, pp. 4-9.
[16] Montgomery, D.C. 2001, Introduction to Statistical
Quality Control, John Wiley & Sons, New York.
[17] Nayak, P.C., Svdheer, K. p., Ragan, D. M., Ramasaty, K.
S., 2004. A neuro-fuzzy computing technique for
modeling hydrological time series. Journal of Hydrology,
vol. 291, pp. 52-66.
[18] Rahimi Bondarabadi, S., Saghafian, B. Estimating Spatial
Distribution of rainfall by Fuzzy Set theory, Iran- Water
Resources Research.3, pp. 17-18.
[19] Razavi Toosi, S. L., Samani, J. M. V., Kooreshpazan
Dezfuli, A., 2007. Ranking Inter Basin Water
Resources Projects Using Fuzzy Multiple Attribute Group
Decision Making Method, Iran- Water Resources
Research. 3, pp. 12-13.
[20] Ramachandra Rao, A., Srinivas, V.V.,
2006.Regionalization of watersheds by fuzzy cluster
analysis. Journal of Hydrology 318, pp. 57-79.
[21] Srinivas V.V., Tripathi, Shivam, Ramachandra Rao, A.,
Govindaraju, Rao S. 2008. Regional flood frequency
analysis by combining self-organizing feature map and
fuzzy clustering, Journal of Hydrology, 348, pp. 148-
166.
[22] Tayfur, G., Ozdemir, S., Singh, V.P., 2003. Fuzzy logic
algorithm for runoff-induced sediment- transport from
bare soil surfaces, Advanced in Water Resources 26,
1249-1256.
[23] Viessman Warren J. r., Lewis, Gary L. Introduction to
Hydrology 1996, (Fourth edition), Harper Collins
Collage Publishers, 551, 48.
[24] Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8(3), pp. 338-353.
[25] Ziaee, H., 1990. The application of statistical concepts in
engineering Hydrology. Iranian Jahad Daneshgahi
puplisher. 12350.
[1] Abolpour, B., Javan, M., Karamouz M.. 2007. Water
allocation improvement in river basin using adaptive
Neural Fuzzy Reinforcement Learning approach, Applied
Soft Computing 7, pp. 265-285.
[2] Agostino, R.B. D and Stephens, M.A., Eds. 1986.
Goodness-of-Fit Techniques, Marcel Dekker.
[3] Bankert, R., Hadjimichael, M. and Hansen, B. 2001.
Fuzzy logic in Environmental Sciences (http://
www.chebucto.ns.ca/Science/AIMET/fuzzy_environment/.
[4] Bardossy, A., Bronstert, A., and Merz, B. 1995. "1, 2, and 3 Dimensional Modeling of Water Movement in the
Unsaturated soil Matrix Using a Fuzzy Approach". Adv.
Wat. Resour. 18, pp. 237-251.
[5] Buckley, J.J., Eslami, E., 2003a. Uncertain probabilities I: the discrete case, Soft Computing, 7, pp. 500-505.
[6] Buckley, J.J., Eslami, E., 2003b.Uncertain probabilities
II: the continuous case, Soft Computing, 7, pp. 500- 505.
[7] Buckley, J.J., 2005. Fuzzy Statistics: hypothesis testing,
Soft Computing 9, pp. 512-518.
[8] Filliben, J.J. 1975. "The Probability Plot Correlation
Coefficient Test for Normality," Technometrics, 17, 111.
[9] Fontane, D.G., Timothy, K.G., and Moncado, E., 1997.
Planning reservoir operation with imprecise
objectives. Journal of water resources planning and
management. ASCE, 123, pp. 154-162.
[10] Jacquin, A. and Shawseldin, A. 2006. "Development of
Rainfall-Runoff Model Using Takagi-Sugeno, fuzzy
inference system". Journal of Hydrology, vol. 329, pp.
154-173.
[11] Kang, M.S., Kang, M.G., Park, S.W., Lee, J. J., Yoo,
K.H., 2006. Application of grey model and artificial
neural networks to flood forecasting. Journal of the
American water resources association, vol. 42, pp. 473-
489.
[12] Khan, E., 1999. Neural fuzzy based intelligent systems
and applications, in: Jain, L.C., and Martin, N.M. (Eds.),
Fusion of Neural Networks, Fuzzy Sets, and Genetic
Algorithms: Industrial Applications. CRC Press,
Washington, DC, p. 331.
[13] Lilliefore, H.W. 1967. "On the Kolmogorov−Smirnov
Test for Normality with Mean and Variance
Unknown," Journal of the American Statistical
Association, 62, pp. 399-402.
[14] Mathon, Bree R., Ozbek, Metin M., Pinder, George F.,
2008. Transmissivity and storage coefficient
estimation by coupling the Cooper-Jacob method and
modified fuzzy least-squares regression. Journal of
Hydrology vol. 353, pp. 267- 274.
[15] May, D., Sivakumar, M. 2008. Comparison of artificial
neural network and regression models in the Prediction
of urban storm water quality. Water environment
research, vol. 80, pp. 4-9.
[16] Montgomery, D.C. 2001, Introduction to Statistical
Quality Control, John Wiley & Sons, New York.
[17] Nayak, P.C., Svdheer, K. p., Ragan, D. M., Ramasaty, K.
S., 2004. A neuro-fuzzy computing technique for
modeling hydrological time series. Journal of Hydrology,
vol. 291, pp. 52-66.
[18] Rahimi Bondarabadi, S., Saghafian, B. Estimating Spatial
Distribution of rainfall by Fuzzy Set theory, Iran- Water
Resources Research.3, pp. 17-18.
[19] Razavi Toosi, S. L., Samani, J. M. V., Kooreshpazan
Dezfuli, A., 2007. Ranking Inter Basin Water
Resources Projects Using Fuzzy Multiple Attribute Group
Decision Making Method, Iran- Water Resources
Research. 3, pp. 12-13.
[20] Ramachandra Rao, A., Srinivas, V.V.,
2006.Regionalization of watersheds by fuzzy cluster
analysis. Journal of Hydrology 318, pp. 57-79.
[21] Srinivas V.V., Tripathi, Shivam, Ramachandra Rao, A.,
Govindaraju, Rao S. 2008. Regional flood frequency
analysis by combining self-organizing feature map and
fuzzy clustering, Journal of Hydrology, 348, pp. 148-
166.
[22] Tayfur, G., Ozdemir, S., Singh, V.P., 2003. Fuzzy logic
algorithm for runoff-induced sediment- transport from
bare soil surfaces, Advanced in Water Resources 26,
1249-1256.
[23] Viessman Warren J. r., Lewis, Gary L. Introduction to
Hydrology 1996, (Fourth edition), Harper Collins
Collage Publishers, 551, 48.
[24] Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8(3), pp. 338-353.
[25] Ziaee, H., 1990. The application of statistical concepts in
engineering Hydrology. Iranian Jahad Daneshgahi
puplisher. 12350.
@article{"International Journal of Earth, Energy and Environmental Sciences:62771", author = "Nasser Shamskia and Seyyed Habib Rahmati and Hassan Haleh and Seyyedeh Hoda Rahmati", title = "A New Approach of Fuzzy Methods for Evaluating of Hydrological Data", abstract = "The main criteria of designing in the most hydraulic
constructions essentially are based on runoff or discharge of water. Two of those important criteria are runoff and return period. Mostly,
these measures are calculated or estimated by stochastic data.
Another feature in hydrological data is their impreciseness.
Therefore, in order to deal with uncertainty and impreciseness, based
on Buckley-s estimation method, a new fuzzy method of evaluating hydrological measures are developed. The method introduces
triangular shape fuzzy numbers for different measures in which both
of the uncertainty and impreciseness concepts are considered. Besides, since another important consideration in most of the
hydrological studies is comparison of a measure during different
months or years, a new fuzzy method which is consistent with special form of proposed fuzzy numbers, is also developed. Finally, to
illustrate the methods more explicitly, the two algorithms are tested on one simple example and a real case study.", keywords = "Fuzzy Discharge, Fuzzy estimation, Fuzzy ranking method, Hydrological data", volume = "5", number = "5", pages = "312-9", }
