Multi-time scale stream flow predictions: The support vector machines approach

Asefa, Tirusew; Kemblowski, M. W.; McKee, Mac; Khalil, Abedalrazq F.

doi:10.1016/j.jhydrol.2005.06.001

Cited by 253 publications

(142 citation statements)

References 14 publications

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“…The non-linear Radial Basis Function (RBF) is the most commonly used kernel function (e.g., Asefa et al, 2006;Tripathi et al, 2006). However, as the SVM method is in its infancy in hydrological applications, there is still some debate as to which kernel function is best suited for the purpose.…”

Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

“…Han et al (2007) compared the RBF and linear kernel functions and found that even within the same catchment, the ideal function can change under different circumstances. Despite being a relatively new approach to hydrological modelling, SVMs and SVRs have been applied to many of the same problems as ANNs, including rainfallrunoff modelling for water resources planning and flood forecasting at various lead-times (Asefa et al, 2006;Han et al, 2007;Behzad et al, 2009;Rasouli et al, 2012), hydraulic modelling (Liong and Sivapragasam, 2002) and downscaling of GCM output (Tripathi et al, 2006). Genetic Programming (GP; Koza, 1992) is another soft computing approach to non-linear modelling and is based on Darwin's theory of evolution by natural selection.…”

Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

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Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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“…SVM is a new learning system that has been developed based on the statistical learning theory aiming at minimizing the generalized model error rather than just minimizing the training error, which consequently increases SVM generalization ability [23,24].…”

Section: Model Descriptionmentioning

confidence: 99%

“…Recently, SVM have been successfully extended to apply in regression and prediction applications [11,25,26]. SVM has been applied in the time-series prediction of river flow by Samsudin, Saad [6]; in SF prediction under multiple time scales by Asefa, Kemblowski [27]; in the real-time forecasting of flood stage by Yu, Chen [25]; in flood forecasting by [28]; in long-term discharge prediction by Lin, Cheng [29]; in the long-range forecast of SF by [30]; and in the monthly forecasting of SF by Guo, Zhou [31], Noori, Karbassi [32], Shabri and Suhartono [33], and Ch, Anand [34].…”

Section: Model Descriptionmentioning

confidence: 99%

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

2017

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Abstract. Stream flow (SF) prediction is considered as a very complex due to the hydrological systems of surface water are complex and dynamic. The reliable prediction of stream flow (SF) can be performed by either conceptual or data-driven based models. In the modelling of hydrological processes, the support vector machine (SVM) is a novel, data-driven approach. Hence, six SVM-based models were generated in this study to predict real time hourly SF in the Selangor River Basin from the water level and rainfall of upstream stations. These models composed of six different combinations of input variables and were trained and tested under hourly records of SF, rainfall, and water level over one year (2011). Among the SVM-based models, SVM-M6, which has nine input variables, was the most effective. Under the training and testing data sets, its correlation coefficient and mean absolute error values were 0.992, 0.953, 0.061 and 0.253 respectively.

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“…Shortridge et al [14] compared a machine learning algorithm with a statistical approach-multiple regression to simulate streamflow over five rivers of Ethiopia. On the machine learning side, support-vector machines [15,16], regression tree based approach [5,17], and ANN [18,19] were used for rainfall-runoff modeling.…”

Section: Introductionmentioning

confidence: 99%

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

Bhowmik

Seo

Sahoo³

2018

Water

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Statistical models for hydrologic simulation are a common choice among researchers particularly when catchment information is limited. In this study, we adopt a new statistical approach, namely Bayesian regression with multivariate linear spline (BMLS) for long-term simulation of streamflow on a Hydroclimate Data Network (HCDN) site in the United States. The study aims to: (i) evaluate the performance of the BMLS model; (ii) compare the performance of climate model outputs as predictors in hydrologic simulation; and (iii) estimate the changes in streamflow caused by anthropogenic climate change which is defined as the projected change in precipitation and temperature under different greenhouse gas emission scenarios. Performance of the BMLS model is compared with climatology for the validation period. Results suggest that the BMLS model forced with observed monthly precipitation and average temperature exhibits information that is not presented in the climatology of the validation period. Later, we consider Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and hindcast runs to simulate streamflow at the HCDN site. The study found that sea-surface temperature-initialized decadal hindcast runs are performing no better than 20th century historical runs regarding hydrologic simulation. Finally, the changes in mean and variability in streamflow at the HCDN site are estimated by forcing the model with CMIP5 future projections for the period 2000-2049.

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Multi-time scale stream flow predictions: The support vector machines approach

Cited by 253 publications

References 14 publications

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

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