Application of radial basis function neural networks to short-term streamflow forecasting

Kagoda, Paulo A.; Ndiritu, John; Ntuli, C.; Mwaka, B.

doi:10.1016/j.pce.2010.07.021

Cited by 68 publications

(25 citation statements)

References 22 publications

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“…The variance of the data in the test stages is higher than that in the calibration stage, making the training data much smoother, and thus better result in the calibration stage. This is consistent with effects of data variability [27]. Thus the generalization ability of the Model was tested, and the estimation errors are shown in Table 3.…”

Section: Svm Calibration and Testingsupporting

confidence: 76%

Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

Babangida

Mustafa

Yusuf

et al. 2016

MATEC Web of Conferences

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Abstract. Pore-water pressure (PWP) is influenced by climatic changes, especially rainfall. These changes may affect the stability of, particularly unsaturated slopes. Thus monitoring the changes in PWP resulting from climatic factors has become an important part of effective slope management. However, this monitoring requires field instrumentation program, which is resource and labour expensive. Recently, soft computing modelling has become an alternative. Low degree polynomial kernel support vector machine (SVM) was evaluated in modelling the PWP changes. The developed model used pore-water pressure and rainfall data collected from an instrumented slope. Wrapper technique was used to select input features and k-fold cross validation was used to calibrate the model parameters. The developed model showed great promise in modelling the pore-water pressure changes. High correlation, with coefficient of determination of 0.9694 between the predicted and observed changes was obtained. The one degree polynomial SVM model yielded competitive result, and can be used to provide lead time records of PWP which can aid in better slope management.

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Section: Svm Calibration and Testingsupporting

confidence: 76%

Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

Babangida

Mustafa

Yusuf

et al. 2016

MATEC Web of Conferences

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“…This luxury is lacking in the rainfall data and by extension in the model type 2 input features. Kagoda et al (2010) also echoed this sentiment on loss of accuracy due to data variability. Model type 3 on the other hand, has comparatively little to do with the data variability problem, as none of the input features dominated one another.…”

Section: Resultsmentioning

confidence: 91%

“…Time series studies of Liong and Sivapragasam (2002), Lin et al (2006), Gill et al (2006), Zakaria and Shabri (2012) and Yu et al (2004) have achieved good results using the RBF kernel. Hsu et al (2003), in their guide to the use of SVM, recommended the use of RBF; therefore, in this study of time series of PWP and rainfall, the use of RBF is considered without prejudice. The RBF kernel has one parameter that needs to be optimized along with the SVR parameters.…”

Section: Implementation Of Svrmentioning

confidence: 99%

Prediction of pore-water pressure response to rainfall using support vector regression

et al. 2016

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Nonlinear complex behavior of pore-water pressure responses to rainfall was modelled using support vector regression (SVR). Pore-water pressure can rise to disturbing levels that may result in slope failure during or after rainfall. Traditionally, monitoring slope pore-water pressure responses to rainfall is tedious and expensive, in that the slope must be instrumented with necessary monitors. Data on rainfall and corresponding responses of pore-water pressure were collected from such a monitoring program at a slope site in Malaysia and used to develop SVR models to predict pore-water pressure fluctuations. Three models, based on their different input configurations, were developed. SVR optimum metaparameters were obtained using k-fold cross validation and a grid search. Model type 3 was adjudged the best among the models and was used to predict three other points on the slope. For each point, lag intervals of 30 min, 1 h and 2 h were used to make the predictions. The SVR model predictions were compared with predictions made by an artificial neural network model; overall, the SVR model showed slightly better results. Uncertainty quantification analysis was also performed for further model assessment. The uncertainty components were found to be low and tolerable, with d-factor of 0.14 and 74 % of observed data falling within the 95 % confidence bound. The study demonstrated that the SVR model is effective in providing an accurate and quick means of obtaining pore-water pressure response, which may be vital in systems where response information is urgently needed.

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“…Recently, neural network models were successfully applied for discharge forecasting in terms of prediction of runoff, flood, streamflow and water level [17,18]. Practically, Jy [19] conducted a study to forecast watershed runoff and stream flow using multilayer feed-forward neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…Natural inflow is predicted at Aswan High Dam (AHD) utilizing the streamflow data of the monitoring stations upstream of the AHD by using two forecasting model approach based on radial basis function neural network (RBFNN) methods [20], and the results of the study showed that the developed ANN model for the natural inflow at AHD was very successful in predicting this natural inflow for few months ahead with very high accuracy. Kagoda [17] used radial basis function type of neural network for 1-day-ahead forecasting short-term stream flow. The authors suggested that a good enough length of data is necessary to get satisfactory results from ANN modeling.…”

Section: Introductionmentioning

confidence: 99%

RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia

Yaseen

El-Shafie

Afan

et al. 2015

Neural Comput & Applic

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Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.

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Application of radial basis function neural networks to short-term streamflow forecasting

Cited by 68 publications

References 22 publications

Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

Prediction of pore-water pressure response to rainfall using support vector regression

RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia

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