EC-SVM approach for real-time hydrologic forecasting

Yu, Xiangyao; Liong, Shie‐Yui; Babovic, Vladan

doi:10.2166/hydro.2004.0016

Cited by 148 publications

(84 citation statements)

References 22 publications

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“…Another drawback is the difficulty in the selection of a kernel function and its specific parameters, C and ε. More recently, several other methods have been developed to identify the parameters, such as the stepwise search (Dong et al, 2005), genetic algorithms (Chen et al, 2004), the shuffled complex evolution algorithm (Yu et al, 2004), and the simulated annealing algorithms (Pai & Hong, 2005).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…The SCE-UA technique has been used successfully in the area of surface and subsurface hydrology for the calibration of rainfall-runoff models and identification of parameters of aquifer formation (Duan et al, 1994). Moreover, the SCE-UA technique has also been applied successfully to identify parameters of SVM (Yu et al, 2004). Hsu et al (2003) pointed out that a series of trial of C and ε in exponential space is a practical method to identify good parameters.…”

Section: Model Development and Testingmentioning

confidence: 99%

“…However, along with the introduction of Vapnik's ε insensitive loss function, SVMs have been extended to solve nonlinear regression estimation (Gunn, 1998;Smola & Schölkopf, 2004) and time series forecasting (Thissen et al, 2003). It is useful to note that the SVM is finding its way into the water sector (Liong & Sivapragasm, 2002;Bray & Han, 2004;Asefa et al, 2004) and a combination of SVM and evolutionary algorithm called EC-SVM has also been attempted recently (Yu et al, 2004). In the latter study, a shuffled complex evolution (SCE-UA) algorithm (Duan et al, 1992(Duan et al, , 1993(Duan et al, , 1994 was used to search phase space parameters (the time delay embedding dimension) and three SVM parameters.…”

Section: Introductionmentioning

confidence: 99%

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Using support vector machines for long-term discharge prediction

Lin

Cheng

Chau

2006

Hydrological Sciences Journal

522

247

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Accurate time-and site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive movingaverage (ARMA) models have been used in modelling water resource time series as a standard representation of stochastic time series. Recently, artificial neural network (ANN) approaches have been proven to be efficient when applied to hydrological prediction. In this paper, the support vector machine (SVM) is presented as a promising method for hydrological prediction. Over-fitting and local optimal solution are unlikely to occur with SVM, which implements the structural risk minimization principle rather than the empirical risk minimization principle. In order to identify appropriate parameters of the SVM prediction model, a shuffled complex evolution algorithm is performed through exponential transformation. The SVM prediction model is tested using the long-term observations of discharges of monthly river flow discharges in the Manwan Hydropower Scheme. Through the comparison of its performance with those of the ARMA and ANN models, it is demonstrated that SVM is a very potential candidate for the prediction of long-term discharges.Key words autoregressive moving-average (ARMA) models; long-term discharge prediction; neural networks; SCE-UA algorithm; support vector machine Utilisation de "support vector machines" pour la prévision de débit à long terme Résumé La gestion et la programmation efficaces d'un barrage hydroélectrique requièrent des prévisions précises, dans le temps et spécifiques pour chaque site, de débit de cours d'eau et de flux entrant dans le réservoir. Les modèles autorégressifs à moyenne mobile (ARMA) sont traditionnellement utilisés pour la modélisation de séries temporelles hydrologiques comme une représentation standard de séries temporelles stochastiques. Récemment des approches par réseaux de neurones artificiels (RNA) se sont révélées être efficaces pour la prévision hydrologique. Dans cet article, une "support vector machine" (SVM) est présentée comme une méthode prometteuse pour la prévision hydrologique. L'approche SVM devrait éviter le sur-apprentisage et les optima locaux, car elle s'appuie sur le principe de minimisation structurelle du risque plutôt que sur le principe de minimisation empirique du risque. Un algorithme "shuffled complex evolution" est utilisé via une transformation exponentielle pour identifier les paramètres appropriés du modèle SVM de prévision. Le modèle SVM de prévision est testé avec les longues séries d'observation des débits mensuels de cours d'eau du complexe hydroélectrique de Manwan. Il apparaît, par comparaison de ses performances avec celles de modèles ARMA et RNA, que l'approche SVM est très pertinente pour la prévision de débits à long terme.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Model Development and Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using support vector machines for long-term discharge prediction

Lin

Cheng

Chau

2006

Hydrological Sciences Journal

522

247

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“…Examples of one-step ahead forecasting of hydrological processes include Lambrakis et al (2000), Ballini et al (2001), Yu et al (2004), Yu and Liong (2007), Hong (2008), Koutsoyiannis et al (2008) and Tran et al (2015). Several studies performing multi-step ahead forecasting are Ballini et al (2001), Kim and Valdés (2003), Asefa et al (2005), Khan and Coulibaly (2006), Lin et al (2006), Cheng et al (2008), Guo et al (2011) and Valipour et al (2013).…”

Section: Time Series Forecasting In Hydrology and Beyondmentioning

confidence: 99%

“…Similarly, Yu et al (2004) compare several forecasting methods, including an ARIMA model and SVM, on two daily time series of runoff and Tongal and Berndtsson (2016) compare several stochastic and ML forecasting methods on three time series of streamflow processes. Additionally, in Chen et al (2012) the reader can find one of the few studies using RF for hydrological forecasting tasks.…”

Section: Right After the Introduction Of The Currently Classical Automentioning

confidence: 99%

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Papacharalampous¹,

Tyralis²,

Koutsoyiannis³

2017

Preprint

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Abstract:We perform an extensive comparison between 11 stochastic to 9 machine learning methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 110 values and the second time using time series of 310 values. Additionally, we conduct 92 real-world case studies using mean monthly time series of streamflow and particularly focus on one of them to reinforce the findings and highlight important facts. We quantify the performance of the methods using 18 metrics. The results indicate that the machine learning methods do not differ dramatically from the stochastic, while none of the methods under comparison is uniformly better or worse than the rest. However, there are methods that are regularly better or worse than others according to specific metrics.

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Rainfall–runoff modeling using least squares support vector machines

Okkan

Serbeş

2012

Environmetrics

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Over the past decade, artificial neural networks (ANN) have been widely used in the runoff modeling studies. In spite of a number of advantages, ANN models have some drawbacks, including the possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters, initialization of the weights in each simulation randomly, and the components of its complex structure. In the past decade, a new alternative kernel‐based technique called a support vector machine (SVM) has been found to be popular in modeling studies because of its advantages over ANN. Least squares version of support vector machines (LS‐SVM) provides a computational advantage over standard support vector machines by converting quadratic optimization problem into a system of linear equations. The LS‐SVM method is preferred in this study. The main purposes of this study are to examine the applicability and capability of LS‐SVM for the prediction of runoff values of Tahtali and Gordes watersheds, which are the major surface water resources for the city of Izmir in Turkey, and to compare its performance with ANN and other traditional techniques such as autoregressive moving average and multiple linear regression models. For these purposes, meteorological data (rainfall and temperature) and lagged data of runoff were used in modeling applications. Some favorite statistical performance evaluation measures were used to assess models. The results in study indicate that the LS‐SVM and ANN methods are successful tools to model the monthly runoff series of two study regions and can give better prediction performances than conventional statistical models. Although these two methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the same or higher accuracy. In terms of accuracy, the LS‐SVM models, which involve different normalization types, resulted in increased accuracy to that of the ANN models. Copyright © 2012 John Wiley & Sons, Ltd.

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EC-SVM approach for real-time hydrologic forecasting

Cited by 148 publications

References 22 publications

Using support vector machines for long-term discharge prediction

Using support vector machines for long-term discharge prediction

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Rainfall–runoff modeling using least squares support vector machines

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