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@article{165104,
        author = {Prof. Digvijay B. Saruk and Prof. Hariom K. Mapari},
        title = {Rainfall-Runoff Modelling: An Overview},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {501-505},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165104},
        abstract = {This is a review paper in which short-term memory (LSTM) networks, the Non-stationary Rainfall-Runoff Toolbox (NRRT), nonlinear autoregressive model with exogenous inputs lumped (LNARX), nonlinear autoregressive model with exogenous geomorphometrically processed inputs (GNARX), wavelet nonlinear autoregressive model with exogenous inputs (WLNARX), and nonlinear autoregressive model with exogenous geomorphometrically processed inputs (WGNARX), three neural network methods, Feed Forward Back Propagation (FFBP), Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN) were employed for rainfall-runoff modelling along with two numerical models including ANN and ANFIS used to model the rainfall runoff process and the best model was chosen. The rainfall-runoff correlograms proved to be effective in determining the appropriate number of nodes for the input layer. Also the whole R-R was calibrated using different mentioned tools with different correlations and whose who gave the best correlation was chosen for R-R model for respective catchments. The coefficient of correlation, RMSE and NSE were calculated for better correlation.},
        keywords = {R-R model, LSTM, ANN, ANFIS, GRNN, RBF, RMSE, NSE.},
        month = {},
        }