Abstract
In water resource management and planning the Rainfall-Runoff models play a crucial role and depends mainly on the data available for planning activities. The rainfall-runoff relationship comes under the nonlinear and complex hydrological Event. In the present study two data driven modeling approaches, Artificial Neural Network (ANN) and Gene Expression Programming (GEP) has been used for modeling of rainfall-runoff process as these methods does not consider the physical nature of the process, which is complex to understand. GEP and ANN are used to model rainfall-runoff relationship for Dindori catchment in upper Narmada River Basin. Daily hydro-meteorological data of Dindori gauging station and precipitation of the catchment for a period of eighteen years were used as input in the model design. Various combinations of input variables for training and testing of models were selected based on statistical parameters. The performance of model was evaluated in term of the root mean square error (RMSE), coefficient of determination, RMSE to standard deviation ratio (RSR) and Nash Sutcliffe Efficiency. The results obtained after applying the two techniques were compared. Which indicates that GEP performed better in all performance evaluation parameters (R2 is 0.92) then ANN (R2 0.90) and is able to give mathematical relationship for rainfallrunoff modeling.
Highlights
In response to these water challenges, hydrological phenomenon have been developed to examine, comprehend and develop solutions for water management
Analysis of result by statistical measures for different combination for Artificial Neural Network (ANN) model is shown in Table II through the root mean square error (RMSE), R2, RMSE to standard deviation ratio (RSR) and Nash Sutcliffe Efficiency (NSE) statistical indexes
In present paper two data driven techniques ANN and Gene Expression Programming (GEP) are accounted for developing Rainfall-Runoff model
Summary
In response to these water challenges, hydrological phenomenon have been developed to examine, comprehend and develop solutions for water management. Hydrological model is collection of several processes (e.g. precipitation, evapotranspiration, ground water, and runoff) which represent real world system and help in managing and predicting water resources. Experts in water assets have utilized information driven displaying approaches, as these have been found to conquer a portion of the troubles related with physical based model. Such approaches have the capacity to show the precipitation overflow process without the point by point comprehension of the complex physical qualities of catchment. In the most recent decade, Progression in the field of Artificial Intelligence (AI) has impacts numerous
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