Soft Computing tools in Rainfall-runoff Modeling

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@Article{Jothiprakash:2009:ISHjhe,

• author = "V. Jothiprakash and R. Magar",
• title = "Soft Computing tools in Rainfall-runoff Modeling",
• journal = "ISH Journal of Hydraulic Engineering",
• year = "2009",
• volume = "15",
• number = "sup1",
• pages = "84--96",
• keywords = "genetic algorithms, genetic programming",
• publisher = "Taylor \& Francis",
• organisation = "Indian Society for Hydraulics",
• DOI = "doi:10.1080/09715010.2009.10514970",
• abstract = "The use of rainfall-runoff models in the decision making process of water resources planning and management has become increasingly indispensable. Rainfall-runoff modeling in the broad sense started at the end of 19th century and till today there are various types of models based on their mechanism, input data and other modeling requirements. These type of models range from physical, conceptual, empirical models and more sophisticated models like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Genetic Programming (GP), Model Tree (MT), Support Vector Machine (SVM) and recently Chaos theory. The primary aim of this paper is to review the recent works on Rainfall-Runoff modeling using soft computing techniques.",

}

Genetic Programming entries for V Jothiprakash Rajendra B Magar

Citations