Advanced ANN Hydrological Models for Rainfall-Runoff Modeling

Seema Narain; Ashu Jain

Advanced ANN Hydrological Models for Rainfall-Runoff Modeling

Author(s)	Dr. Seema Narain, Ashu Jain
Country	India
Abstract	Hydrological modeling is a tool for the investigation of hydrologic system for both the hydrologists and practicing water resources engineers involved in the planning, development, and management of water resources systems. The Artificial Neural Network (ANN) solutions have been found promising in modeling the complex hydrological systems as compared to the traditional conceptual or empirical approaches. The basic building block of such ANN models used in hydrology employs an artificial neuron called McCulloch and Pitts Artificial Neuron (MPAN) proposed by McCulloch and Pitts in the early 1940s. Recently, some researchers have proposed the use of Generalized Neuron (GN) models in other branches of engineering and sciences but such attempts have been limited in hydrology so far. Neural system (NS) models presented here include: (a) a traditional feed-forward multi-layer perceptron (MLP) ANN model (employing MPAN) trained using back-propagation algorithm, and (b) three different GN models. This paper presents the results of an investigation aimed at developing NS models for the purpose of rainfall-runoff modeling. The performance of the developed GN models is compared with a traditional feed-forward neural network model developed using MPANs. The rainfall and flow data derived from the Kentucky River Basin, USA were used for the model development and validation. With their compact structure, less number of parameters, and, lesser training time, the GN models were found more promising for the rainfall-runoff modeling in the present study. The results obtained in this study indicate that the GN models have tremendous potential for application in hydrological development. It is hoped that future research efforts will focus on exploiting the strengths of such artificial neuron models for an effective and efficient operation and management of water resources and environmental systems.
Keywords	Hydrological Modeling, Generalized Neuron, Advanced Neuron Models, Artificial Neural Network, Neural System in Hydrology
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-30

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Advanced ANN Hydrological Models for Rainfall-Runoff Modeling

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