Parameter Estimation of Nonlinear Muskingum Models Using Genetic Algorithm

Mohan, S.

doi:10.1061/(asce)0733-9429(1997)123:2(137)

Cited by 186 publications

(96 citation statements)

References 11 publications

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“…The lateral inflow was calculated by an impulse response function approach. Mohan (1997) used genetic algorithm for parameter estimation of nonlinear Muskingum method and compared its performance with the approach by Yoon and Padmanabhan (1993). Samani and Jebelifard (2003) applied multilinear Muskingum method for hydrologic routing through circular conduits.…”

Section: Literature Reviewmentioning

confidence: 99%

Extended Muskingum method for flood routing

Kumar

Baliarsingh²,

Raju

2011

Journal of Hydro-environment Research

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Routing of floods is essential to control the flood flow at the flood control station such that it is within the specified safe limit. In this paper, the applicability of the extended Muskingum method is examined for routing of floods for a case study of Hirakud reservoir, Mahanadi river basin, India. The inflows to the flood control station are of two types e one controllable which comprises of reservoir releases for power and spill and the other is uncontrollable which comprises of inflow from lower tributaries and intermediate catchment between the reservoir and the flood control station. Muskingum model is improved to incorporate multiple sources of inflows and single outflow to route the flood in the reach. Instead of time lag and prismoidal flow parameters, suitable coefficients for various types of inflows were derived using Linear Programming. Presently, the decisions about operation of gates of Hirakud dam are being taken once in 12 h during floods. However, four time intervals of 24, 18, 12 and 6 h are examined to test the sensitivity of the routing time interval on the computed flood flow at the flood control station. It is observed that mean relative error decreases with decrease in routing interval both for calibration and testing phase. It is concluded that the extended Muskingum method can be explored for similar reservoir configurations such as Hirakud reservoir with suitable modifications.

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Section: Literature Reviewmentioning

confidence: 99%

Extended Muskingum method for flood routing

Kumar

Baliarsingh²,

Raju

2011

Journal of Hydro-environment Research

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“…The slope of the straight line fitted through the loop gives K. Although this approach has been used for decades, it is time consuming and prone to subjective interpretation. Furthermore, the visual judgment may not correctly identify the best among several nearly collapsed loops when all may appear acceptable [5,6]. Yoon and Padmanabhan [6] developed computer codes to estimate the best routing parameters, x and K, by minimizing the deviations of data from regression line in storageÀweighted flow plane.…”

Section: Background On Muskingum Flood Routingmentioning

confidence: 99%

“…Yoon and Padmanabhan [6] developed computer codes to estimate the best routing parameters, x and K, by minimizing the deviations of data from regression line in storageÀweighted flow plane. Routing parameters, x and K, can also be determined by minimizing objective functions such as Equations (5) or (6), which give a direct measure of the difference between observed and computed outflow hydrographs [5,7].…”

Section: Background On Muskingum Flood Routingmentioning

confidence: 99%

“…Advanced parameter estimation techniques are required to calibrate the parameters in non-linear Muskingum models [5,8] and to compute outflow hydrograph. These models are not frequently used in engineering practice because they do not appear to have significant advantage to offset their complexity.…”

Section: Background On Muskingum Flood Routingmentioning

confidence: 99%

“…This data set has been used by many investigators to study the level of accuracy of different calibration methods in linear and non-linear Muskingum models [5,6]. The reasons for choosing this data set are twofold (1) because the data present a non-linear storageÀdischarge relationship which makes the use of a non-linear or piecewise linear model necessary and (2) because the results can be compared with other researchers' results.…”

Section: Multiple-reach Muskingum Flood Routingmentioning

confidence: 99%

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Application of spreadsheets in developing flexible multiple‐reach and multiple‐branch methods of Muskingum flood routing

Hosseini

2009

Comp Applic In Engineering

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Muskingum method which is covered in most hydraulic engineering courses and textbooks is widely used for hydrologic flood routing in rivers. In this paper, computational and optimization capabilities of Microsoft Excel spreadsheet is used to perform multiple-reach Muskingum flood routing in a river for which observed inflow and outflow hydrographs are available and its storage-discharge relationship is highly non-linear. The employed method is straightforward and results in better accuracy compared with traditional methods. It is also illustrated that the proposed computational procedure in the spreadsheets can be extended to flood routing in more complex river systems such as branched systems. Considering all these, from educational point of view, a main conclusion is that the results of the study significantly improve the methodologies for teaching Muskingum flood routing in complex river systems

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Evolutionary Computing in Hydrological Sciences

Savić

Khu

2005

Encyclopedia of Hydrological Sciences

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With the advent of data acquisition techniques and data processing capabilities (computers + algorithms), evolutionary computing ( EC ) has found its way into a wide range of applications in hydrological science as demonstrated in this article. These applications range from the use of EC to assist in the understanding of hydrological processes, to improve the performance of simulation models for almost all aspects of hydrologic applications, and most recently, to quantify risk and uncertainty with the aim of providing decision support. In this article, an attempt is made to provide a detailed review of the applications of EC in different hydrological science topics. References are divided into six main categories: hydrologic processes research, rainfall‐runoff modeling, reservoir operations and control, groundwater systems, urban water systems, and water quality studies. These are broad categorizations, and many applications of EC span across multiple groupings. Evolutionary Computing is a continuously and rapidly growing area of academic research for computer science, mathematics, economics and management, engineering, physical science, and many others. As such, it is futile to try to provide a comprehensive overview of the currently available EC tools and techniques and therefore, no such attempt is made in this article.

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Parameter Estimation of Nonlinear Muskingum Models Using Genetic Algorithm

Cited by 186 publications

References 11 publications

Extended Muskingum method for flood routing

Extended Muskingum method for flood routing

Application of spreadsheets in developing flexible multiple‐reach and multiple‐branch methods of Muskingum flood routing

Evolutionary Computing in Hydrological Sciences

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