Streamflow Simulation In Poorly Gauged Of Pairi Basin Using Artificial Neural Networks
DOI:
https://doi.org/10.29284/gzyttx87Keywords:
Artificial Neural Network, poorly gauged, streamflow simulation, stream flow prediction, Pairi basin, Time-Lagged Recurrent Network, NARX.Abstract
Streamflow simulation in poorly gauged basins is still a major challenge, as a limited quantity of hydrometric observations and the complicated nature of nonlinear processes of rainfall runoffs arises. This research explores the validity and the relative effectiveness of Artificial Neural Network (ANN) models to simulate the streamflow in pairi basin in chattisgarh. This study presents a comparative analysis of streamflow simulation using two Artificial Neural Network (ANN) approaches Time-Lagged Recurrent Networks (TLRN) and Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) for the Pairi Basin of Chhattisgarh. A 30-year time series dataset (1 January 1980 to 31 December 2013) of observed streamflow was used for model development and evaluation. The dataset was divided into training (1980–May 2010), testing (May 2000–November 2008), and cross-validation (November 2008–December 2013) phases to assess learning capability and predictive power. Model performance was evaluated using Mean Squared Error (MSE), correlation coefficient (R), and Nash–Sutcliffe Efficiency (NSE). Results indicate that the NARX model outperformed TLRN, achieving lower MSE and higher R and NSE values during training and testing. The findings demonstrate that NARX provides superior accuracy and generalization for streamflow prediction in the Pairi Basin.
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