Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

Noori, Roohollah; Karbassi, Abdolreza; Moghaddamnia, A.; Han, Dawei; Zokaei-Ashtiani, M.H.; Farokhnia, Ashkan; Gousheh, M. Ghafari

doi:10.1016/j.jhydrol.2011.02.021

Cited by 327 publications

(135 citation statements)

References 60 publications

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“…In SVM, the map model is usually defined as the kernel function to yield the inner products in the feature space and keep the calculated load reasonable. There are four kinds of commonly used kernel functions, including linear kernel, polynomial kernel, radial basis function (RBF) kernel, and sigmoid kernel [30,41]. Unlike the linear kernel, the RBF kernel can easily handle the non-linear relation between class labels and attributes.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…Moreover, there are many noise levels in different time-series regions, which further increase the difficulty of forecasting models. Hence, it is hard for a single time-series forecasting model to capture the dynamic changing processes and features, which may encounter local under-fitting or over-fitting problems [29][30][31][32][33]. The accuracy of a single forecast method always has limited effects.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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Abstract:Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. OPEN ACCESSWater 2015, 7 4478

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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“…During SVM model development, the determination of the optimal combination of C and g is greatly important in constructing high-performance regression models. C is the regularization parameter that controls the degree of empirical error in optimization problem, and g is the RBF kernel parameter that significantly affects the generalization ability of SVM (Noori et al 2011;Singh et al 2011). In the present study, the fivefold CV and grid search method were employed to determine the optimal pairwise C and g during the construction of the SVM model.…”

Section: Results Of Svm Modelmentioning

confidence: 99%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

101

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Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the ruralsuburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

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“…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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Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

Cited by 327 publications

References 60 publications

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

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

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