Artificial Intelligent Techniques in Rainfall-Runoff Process

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dc.contributor.authorMagar, Rajendra
dc.date.accessioned2014-07-24T05:56:14Z
dc.date.available2014-07-24T05:56:14Z
dc.date.issued2013-01-07
dc.description.abstractThe use of rainfall-runoff (R-R) models in the decision making process of water resources planning and management has become increasingly indispensable. R-R modeling is still one of the most difficult issues in hydrological sciences due to the dynamic, uncertain and non-linear characteristics and relationship among the processes. In the broad sense R-R modeling has started at the end of 19th century and till today various types of models have been developed and applied based on their mechanism, input data and other modeling requirements. Fairly a large number of empirical, conceptual and physically based models having their own merits and demerits have been developed and applied to map the R-R relationship. In the real world, temporal variations in data do not exhibit simple regularities and thus R-R process is difficult to analyze and model accurately by conventional modeling approach. Hence R-R modeling approach has been shifted from process based technique to data-driven based Artificial Intelligent (AI) techniques like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Genetic Programming (GP) and Model Tree (MT). The primary aim of this paper is to highlight the merits and demerits of those recent works on R-R modeling using AI techniques. As a value addition, a graphical user interface (GUI) has been developed as a decision support system.en_US
dc.identifier.citation5 International Conference on Water Resources and Arid Environments (ICWRAE 5): 55-60, 7-9 January 2013, Riyadh, Saudi Arabiaen_US
dc.identifier.urihttp://hdl.handle.net/123456789/1042
dc.language.isoenen_US
dc.publisher5 International Conference on Water Resources and Arid Environments (ICWRAE 5)en_US
dc.subjectStaff Publication - SoETen_US
dc.subjectStaff Publication - CE
dc.titleArtificial Intelligent Techniques in Rainfall-Runoff Processen_US
dc.typeArticleen_US
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