**12. References**

[1] Zadeh L, (1994) Fuzzy Logic, Neural Networks and Soft Computing. Communications of the ACM 37 (3), 77–84.


**Figure 6.** RajOct Model results [13]

**Discharge (cum/s)**

**Figure 7.** ManNovJune Model results [13]

**0**

**1000**

**2000**

**3000**

**Discharge (cum/s)**

**4000**

**5000**

*shreel69@yahoo.com, prdxt11@gmail.com* 

the ACM 37 (3), 77–84.

Shreenivas N. Londhe and Pradnya R. Dixit

*Vishwakarma Institute of Information Technology, Kondhwa (bk), Pune, India* 

[1] Zadeh L, (1994) Fuzzy Logic, Neural Networks and Soft Computing. Communications of

**1 16 31 46 61 Days**

**Observed ANN GP MT**

**18/10/1994 31/10/1996**

**1 75 149 223 297 371 445 519 593 667 741**

**31/3/1995 31/5/1998**

**Days**

**Observed ANN GP MT**

**Author details** 

**12. References** 

	- [22] Charhate S, Deo M, Londhe S, (2008) Inverse modeling to derive wind parameters from wave measurements. Applied Ocean Research. 30. 120-129

**Chapter 10** 

© 2012 Sreekanth and Datta, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Sreekanth and Datta, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Genetic Programming: Efficient Modeling Tool** 

**in Hydrology and Groundwater Management** 

With the advent of computers a wide range of mathematical and numerical models have been developed with the intent of predicting or approximating parts of hydrologic cycle. Prior to the advent of conceptual or process based models, physical hydraulic models, which are reduced scale representations of large hydraulic systems, were used commonly in water resources engineering. Fast development in the computational systems and numerical solutions of complex differential equations enabled development of conceptual models to represent physical systems in almost all arenas of life including hydrological and water resources systems. Thus, in the last two decades large number of mathematical models was developed to represent different processes in the hydrological cycle. Hydrological models

Physical models are reduced scale representations of the actual hydrological system and the responses obtained from these models are up-scaled to estimate the responses of the real system. Conceptual models are based on different individual processes or components of a hydrological process. For example, in modelling the watershed response to a storm event a conceptual model make use of different equations to compute different components like subsurface flow, evapo-transpiration, channel flow, groundwater flow, surface run off etc. The third type of modelling involves using mathematical and statistical techniques to fit a model to a data set which then relates the dependent variable to the independent variables. This type of modelling includes regression models, response matrix, transfer functions, neural networks, support vector machine etc. The most widely used "black box" type modelling approach in hydrology and water resources literature is neural networks. Genetic

J. Sreekanth and Bithin Datta

can be broadly classified in to three.

3. Statistical / Black box models

http://dx.doi.org/10.5772/52102

**1. Introduction** 

1. Physical models 2. Conceptual models

Additional information is available at the end of the chapter

