River Discharge modeling using generalized artificial neuron model

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SEEMA NARAIN; ASHU JAIN

River Discharge modeling using generalized artificial neuron model

Authors: SEEMA NARAIN, ASHU JAIN

Unique Paper ID: 181882
Volume: 12
Issue: 2
PageNo: 209-214

Keywords: No Keywords Found

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.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 2
PageNo: 209-214

River Discharge modeling using generalized artificial neuron model

Available:https://ijirt.org/article?manuscript=181882

28 Jun, 2025

Hydrological Modeling – An Approach with Advanced Neural Network Models and their Sensitivity to Initial Solution

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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