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doi:10.1002/hyp.v22:26

Cited by 6 publications

(3 citation statements)

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“…However, Toth and Brath (2007) also found that the conceptual formulation may allow a significant forecasting improvement in comparison with the data-driven approach when focusing on the prediction of flood events, especially in the case of limited availability of calibration data. Mutlu et al (2008) developed two different neural network models, the multilayer perceptron (MLP) and the radial basis function neural network (RBFNN), to predict streamflow at four gauging stations. Their results showed that ANN is a useful tool in streamflow forecasting.…”

Section: Ai-based Data-driven Modelsmentioning

confidence: 99%

“…(2001);Cigizoglu (2005);Wu et al (2005);Wang et al (2006);Ahmed and Sarma (2007);Jain and Kumar (2007);Toth and Brath (2007);Mutlu et al (2008);Kagoda et al (2010); Yonaba et al (2010); Vafakhah (2012); Chen et al (2013); Awchi (2014); Rezaeianzadeh et al (2014); Sivapragasam et al (2014); Talaee (2014); Mehr et al (2015). SVM LSSVM AI-based datadriven Sivapragasam and Liong (2005); Asefa et al (2006); Lin et al (2006); Behzad et al (2009); Li et al (2010); Maity et al (2010); Guo et al (2011); Noori et al (2011); Samsudin et al (2011); Aggarwal et al (2012); He et al (2014).…”

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confidence: 99%

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Univariate streamflow forecasting using commonly used data-driven models: literature review and case study

Zhang

Singh

2018

Hydrological Sciences Journal

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Eight data-driven models and five data pre-processing methods were summarized; the multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition (WD) models were then used in short-term streamflow forecasting at four stations in the East River basin, China. The wavelet-artificial neural network (W-ANN) method was used to predict 1-month-ahead monthly streamflow at Longchuan station (LS). The results indicate better performance of MLR and wavelet-multiple linear regression (W-MLR) in analysing the stationary trained dataset. Four models showed similar performance in 1-day-ahead streamflow forecasting, while W-MLR and W-ANN performed better in 5-day-ahead forecasting. Three reservoirs were shown to have more influence on downstream than upstream streamflow and models had the worst performance at Boluo station. Furthermore, the W-ANN model performed well for 1-month-ahead streamflow forecasting at LS with consideration of a deterministic component.

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Section: Ai-based Data-driven Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

Univariate streamflow forecasting using commonly used data-driven models: literature review and case study

Zhang

Singh

2018

Hydrological Sciences Journal

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“…There has been a rapidly growing interest among water scientists to apply neural networks in water resources. Indeed, ANNs have been applied in forecasting long-term rainfall (Karamouz et al 2008, Mekanik et al 2013, forecasting sea level (Pashova and Popova 2011), forecasting floods (Tiwari and Chatterjee 2010), forecasting water quality (Robert et al 2008, Banerjee et al 2011, forecasting daily water demands (Bennett et al 2013), flow forecasting (Huo et al 2012, Kalteh 2013, Mohanty et al 2013, snowmelt-runoff forecasting (Yilmaz et al 2011), modelling rainfall-runoff processes (Tsung-yi and Wang 2004, Kisi 2008, Wu and Chau 2011, rainfall forecasting (Wu et al 2010), sediment transport prediction (Hamidi and Kayaalp 2008, Melesse et al 2011, Kisi et al 2012, Lafdani et al 2013, groundwater level forecasting (Krishna et al 2008, Nourani et al 2008) and determination of aquifer parameters (Samani et al 2007, Lin et al 2010.…”

Section: Introductionmentioning

confidence: 99%

Improvement of artificial neural networks to predict daily streamflow in a semi-arid area

Zemzami

Benaabidate

2016

Hydrological Sciences Journal

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The application of artificial neural networks (ANNs) has been widely used recently in streamflow forecasting because of their flexible mathematical structure. However, several researchers have indicated that using ANNs in streamflow forecasting often produces a timing lag between observed and simulated time series. In addition, ANNs under-or overestimate a number of peak flows. In this paper, we proposed three data-processing techniques to improve ANN prediction and deal with its weaknesses. The Wilson-Hilferty transformation (WH) and two methods of baseflow separation (one parameter digital filter, OPDF, and recursive digital filter, RDF) were coupled with ANNs to build three hybrid models: ANN-WH, ANN-OPDF and ANN-RDF. The network behaviour was quantitatively evaluated by examining the differences between model output and observed variables. The results show that even following the guidelines of the Wilson-Hilferty transformation, which significantly reduces the effect of local variations, it was found that the ANN-WH model has shown no significant improvement of peak flow estimation or of timing error. However, combining baseflow with streamflow and rainfall provides important information to ANN models concerning the flow process operating in the aquifer and the watershed systems. The model produced excellent performance in terms of various statistical indices where timing error was totally eradicated and peak flow estimation significantly improved. ARTICLE HISTORY

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