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@article{181882,
        author = {SEEMA NARAIN and ASHU JAIN},
        title = {River Discharge modeling using generalized artificial neuron model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {209-214},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181882},
        abstract = {Most applications of neural networks in hydrology employ the McCulloch and Pitts’ Artificial Neuron (MPAN) that was proposed in early 1940s.  This paper presents the results of a preliminary investigation of the use of a new artificial neuron called Generalized Neuron (GN) for hydrological modeling.  The GN model offers many advantages over the traditional MPAN including but not limited to: (a) capability to model the non-linearity in a physical system through non-linear discriminant function, and (b) there is no need to determine the number of hidden layers and consequently the number of hidden neurons as a single GN is capable of modeling a complex physical system.  Two neural network models are presented here: (a) a traditional feed-forward neural network model trained using back-propagation algorithm, and (b) a GN model.  The rainfall and flow data from Kentucky River catchment were employed to develop the neural network models.  A wide range of performance statistics was used to evaluate the ANN model performance.  The results of the study present here indicate that the GN model has tremendous potential for application in hydrological modeling.},
        keywords = {},
        month = {June},
        }