S. Mohan

Department Of Civil Engineering

D. P. Vijayalakshmi

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Genetic algorithm applications in water resources

ISH Journal of Hydraulic Engineering

The application of the Muskingum model to river and channel flood routing may have some limitations because of its inherent assumption of a linear relationship between channel storage and weighted flow. Although nonlinear forms of the Muskingum model have been proposed, an efficient method for parameter estimation in the calibration process is still lacking. In this paper, the objective approach of genetic algorithm is proposed for the purpose of estimating the parameters of two nonlinear Muskingum routing models. The performance of this algorithm is compared with other reported parameter estimation techniques. Results of the application of this approach to an example with high nonlinearity between storage and weighted-flow, show that the genetic algorithm approach is efficient in estimating parameters of the nonlinear routing models. A supplementary analysis of the sensitivity of the parameters during the performance of genetic algorithm shows that a unique set of parameters exists that would result in the best performance for a given problem. In addition, genetic algorithm does not demand any initial estimate of values of any of the parameters, and thus avoids the subjectivity and computation time associated with the traditional estimation methods.

Journal of Hydraulic Engineering

Parameter estimation of nonlinear Muskingum models using genetic algorithm

Choice Reviews Online

Genetic algorithms in search, optimization, and machine learning

EFFECT OF DATA LENGTH ON WATER RESOURCES ASSESSMENT

Editorial Board

Expert Systems with Applications

Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Journal	ISH Journal of Hydraulic Engineering
ISSN	09715010
Open Access	No