Genetic programming approach for flood routing in natural channels

Chandrasekaran, S.; Rathinasamy, Maheswaran; Venkatesh, Veena

doi:10.1002/hyp.6628

Cited by 85 publications

(37 citation statements)

References 15 publications

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“…Applications of genetic programming to water flows are given by: Drecourt (1999) towards rainfall-runoff modelling; Drounpob et al (2005) for streamflow rate prediction; and Sivapragasam et al (2008) for flood routing.…”

Section: Genetic Programmingmentioning

confidence: 99%

Comparison of data-driven modelling techniques for river flow forecasting

Londhe¹,

Charhate

2010

Hydrological Sciences Journal

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Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alterative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events.Key words streamflow; data-driven modelling; artificial neural networks; genetic programming; M5 model trees Comparaison de techniques de modélisation conditionnée par les données pour la prévision des débits fluviaux Résumé La prévision précise des débits est essentielle pour l'opération efficace des systèmes de ressources en eau. Le processus d'écoulement est complexe et fortement non-linéaire. Par conséquent, les chercheurs essaient de mettre au point des techniques alternatives pour prévoir le débit avec une facilité relative et une précision raisonnable, bien que des modèles traditionnels déterministes et conceptuels soient disponibles. Le présent travail utilise trois techniques conditionnées par les données, en l'occurence des réseaux de neurones artificiels (RNA), la programmation génétique (PG) et les arbres modèles (MT) pour prévoir les débits de rivière un jour à l'avance en deux stations du bassin versant de Narmada (Inde), et les résultats sont comparés. Tous les modèles sont assez performants en termes de précision de prévision. Les techniques RNA et MT ont des performances presque égales, mais PG donne de meilleurs résultats, bien que seulement de façon marginale en termes de précision de prévision pour des événements normaux et extrêmes.

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Section: Genetic Programmingmentioning

confidence: 99%

Comparison of data-driven modelling techniques for river flow forecasting

Londhe¹,

Charhate

2010

Hydrological Sciences Journal

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“…Genetic programming (GP) is an evolutionary algorithm based on Darwinian theories of natural selection and survival to approximate the equation, in symbolic form, that best describes how the output relates to the input variables (Sivapragasam et al 2008). The algorithm initializes with a randomly generated arithmetic operators (?, -, 9, 7) and mathematical functions (sin, cos, tan, exp, log, sigmoid) from population.…”

Section: Methodology Genetic Programmingmentioning

confidence: 99%

“…In this study, the GP based models have been employed for forecasting groundwater level. In fact, GP has been reported in variety of hydrologic and hydro-geologic modeling such as: flood routing (Sivapragasam et al 2008), evaporation , ground water remediation (Aly and Peralta 1999), suspended sediment modeling (Kisi et al 2012), stage discharge curve (Azamathulla et al 2011), short-term water level fluctuations (Shiri and Kisi 2011) and rainfall-runoff model (Kisi et al 2013). The detailed review of application of GP in water resources has been presented in ASCE Task Committee (2010).…”

Section: Introductionmentioning

confidence: 99%

Genetic programming based monthly groundwater level forecast models with uncertainty quantification

Kasiviswanathan

Saravanan

Balamurugan

et al. 2016

Model. Earth Syst. Environ.

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Modeling hydrogeologic processes facilitates in accurate prediction/forecasting of groundwater level variations. Still, the uncertainty in model prediction is a major concern that requires detailed investigation. There could be several factors which introduce uncertainty such as inherent assumption, various levels of model complexity and simplicity. In general, model inputs, parameters and structure are the major sources of uncertainty while quantifying model prediction uncertainty. In this study, a genetic programming (GP) based models have been employed for forecasting groundwater level variation along with prediction uncertainty quantification. Though various sources induce uncertainty in the model prediction, the input uncertainty quantification has received little attention. Hence, the input uncertainty has been considered for the analysis in this study. The proposed method is demonstrated using measured monthly values of rainfall and corresponding groundwater level data of Amarawathi basin, India. It is observed that the prediction along with uncertainty quantification improves the confidence level of models while making decisions, in particular for effective planning and management of groundwater resources.

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“…The potential of the GP-based model for flood routing between two river gauging stations on river Walla in USA was explored for single peaked as well as multi-peaked flood hydrographs by [32]. The accuracy of GP models was far superior than modified Muskingum method which is a traditional physics based hydrologic flood routing model which also showed time lag in predictions.…”

Section: Applications In Hydrologymentioning

confidence: 99%