GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING<sup>1</sup>

Khu, Soon‐Thiam; Liong, Shie‐Yui; Babovic, Vladan; Madsen, Henrik; Muttil, Nitin

doi:10.1111/j.1752-1688.2001.tb00980.x

Cited by 168 publications

(78 citation statements)

References 13 publications

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“…Hence, compared to other regression techniques, it is not required to choose the model structure a priori. In water-related studies, GP has been applied to model: flow over a flexible bed (Babovic & Abbott, 1997), the rainfall-runoff process (Savic et al, 1999;Whigham & Crapper, 1999), runoff forecasting (Khu et al, 2001), urban fracturedrock aquifer dynamics (Hong & Rosen, 2002), temperature downscaling (Coulibaly, 2004), and the rainfall-recharge process (Hong et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Modelling the dynamics of the evapotranspiration process using genetic programming

Parasuraman

Elshorbagy

Carey

2007

Hydrological Sciences Journal

125

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Evapotranspiration constitutes one of the major components of the hydrological cycle and hence its accurate estimation is of vital importance to assess water availability and requirements. This study explores the utility of genetic programming (GP) to model the evapotranspiration process. An important characteristic of GP is that both the model structure and coefficients are simultaneously optimized. The method is applied in modelling eddy-covariance (EC)-measured latent heat (LE) as a function of net radiation (NR), ground temperature (GT), air temperature (AT), wind speed (WS) and relative humidity (RH). Two case studies having different climatic and topographic conditions are considered. The performance of the GP model is compared with artificial neural network (ANN) models and the traditional Penman-Monteith (PM) method. Results from the study indicate that both the datadriven models, GP and ANNs, performed better than the PM method. However, performance of the GP model is comparable with that of the ANN model. The GP-evolved models are dominated by NR and GT, indicating that these two inputs can represent most of the variance in LE. The results show that the GP-evolved equations are parsimonious and understandable, and are well suited to modelling the dynamics of the evapotranspiration process.Key words eddy-covariance; evapotranspiration; modelling; genetic programming; artificial neural networks; Penman-Monteith method Modélisation de la dynamique du processus évapotranspiratoire par programmation génétique Résumé L'évapotranspiration est l'une des principales composantes du cycle hydrologique et son estimation précise est par conséquent d'une importance vitale pour estimer la disponibilité et les besoins en eau. Cette étude explore l'utilité de la programmation génétique (PG) pour modéliser le processus évapotranspiratoire. Une caractéristique importante de la PG est que la structure du modèle et les coefficients sont optimisés simultanément. La méthode est appliquée à la modélisation de la chaleur latente (CL) mesurée par la méthode des covariances turbulentes (CT) comme une fonction du rayonnement net (RN), de la température du sol (TS), de la température de l'air (TA), de la vitesse du vent (VV) et de l'humidité relative (HR). Deux études de cas sont considérées, qui présentent des conditions climatiques et topographiques différentes. La performance de la modélisation PG est comparée à celles de modèles de type réseaux de neurones artificiels (RNA) et de la méthode traditionnelle de Pennman-Monteith (PM). L'étude montre que les modélisations forcées par les données, PG et RNA, donnent de meilleurs résultats que la méthode PM. Cependant, la performance de la modélisation PG est comparable à celle de la modélisation RNA. Les modélisations PG évoluées sont dominées par RN et TS, ce qui indique que ces deux variables d'entrée peuvent représenter la plupart de la variance de CL. Les résultats montrent que les équations PG évoluées sont parcimonieuses et intelligibles, et sont bien adaptées à la mo...

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Section: Introductionmentioning

confidence: 99%

Modelling the dynamics of the evapotranspiration process using genetic programming

Parasuraman

Elshorbagy

Carey

2007

Hydrological Sciences Journal

125

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“…3). More details on GP can be obtained from (Koza 1992;Babovic and Keijzer 2000;Khu et al 2001). This study used DTREG software (Phillip 2012) to develop GP based groundwater fluctuation model.…”

Section: Methodology Genetic Programmingmentioning

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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“…This evolutionary method is continued for following generations and is influenced for obtaining symbolic expressions for describing the data. This leads to scientific interpretation to obtain knowledge about the method [37][38][39]. Table 4 summarizes the parameters used per run of GP.…”

Section: Genetic Programmingmentioning

confidence: 99%

Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

Mazinani

Ismail

Shamshirband

et al. 2016

Entropy

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This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 mˆ1.5 mˆ2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge.

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GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING¹

Cited by 168 publications

References 13 publications

Modelling the dynamics of the evapotranspiration process using genetic programming

Modelling the dynamics of the evapotranspiration process using genetic programming

Genetic programming based monthly groundwater level forecast models with uncertainty quantification

Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

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GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING1

Cited by 168 publications

References 13 publications

Modelling the dynamics of the evapotranspiration process using genetic programming

Modelling the dynamics of the evapotranspiration process using genetic programming

Genetic programming based monthly groundwater level forecast models with uncertainty quantification

Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

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GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING¹