Streamflow forecasting using least-squares support vector machines

Shabri, Ani; Suhartono, Suhartono

doi:10.1080/02626667.2012.714468

Cited by 93 publications

(49 citation statements)

References 60 publications

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“…The optimal solution for β can be obtained by inverting the H as follows [70]: † = β H T (14) where † T = H PH is the Moore-Penrose generalized inverse of H, and…”

Section: Hβ Tmentioning

confidence: 99%

“…The MLMs included artificial neural network (ANN) [1,8], neuro-fuzzy (NF) [9], support vector machines (SVMs) (for regression, also called support vector regression (SVR)) [10,11], random forest (RF) [12], least squares support vector machine (LSSVM) (for regression, also called least squares support vector regression (LSSVR)) [13,14] and extreme learning machine (ELM) [15,16]. The MLMs are able to deal with nonlinearity and non-stationarity inherent in rainfall-runoff relationship and streamflow time series effectively.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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“…The optimal solution for β can be obtained by inverting the H as follows [70]: † = β H T (14) where † T = H PH is the Moore-Penrose generalized inverse of H, and…”

Section: Hβ Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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“…Mishra et al (2007), Abudu et al (2010), Kişi et al (2012) and Valipour et al (2013). The same applies to Khan and Coulibaly (2006), Lin et al (2006), Wang et al (2009), Shabri andSuhartono (2012), Belayneh et al (2014) and Patel and Ramachandran (2014), albeit the latter studies also include SVM methods in the comparisons.…”

Section: Right After the Introduction Of The Currently Classical Automentioning

confidence: 82%

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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“…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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Streamflow forecasting using least-squares support vector machines

Cited by 93 publications

References 60 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

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

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

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