Daily suspended sediment load prediction using artificial neural networks and support vector machines

Lafdani, Elham Kakaei; Nia, Alireza Moghaddam; Ahmadi, Azadeh

doi:10.1016/j.jhydrol.2012.11.048

Cited by 194 publications

(31 citation statements)

References 44 publications

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“…This model can be any viable mathematical model. For example, Figure 2.1 illustrates such a model which is based on neural networks (Marwala, 2009;Kakaei Lafdani et al, 2013). Other models which can be used in this regard include support vector machines (He and Du, 2013;Liu et al, 2013;Bifengrong, 2013) and rough sets (Marwala, 2009).…”

Section: Auto-associative Memory Networkmentioning

confidence: 99%

What is a Correlation Machine?

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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This chapter describes what a correlation machine is as well as the types of correlations machines. Correlations machines considered in this chapter are the auto-associative memory machine trained using genetic algorithm (GA), Expectation Maximization (EM) trained Gaussian mixed models and the principal component analysis (PCA) machine. These methods were used to model the antenatal HIV database. From this chapter, auto-associative memory machine is chosen as a correlation machine to be considered for the rest of this book.

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Section: Auto-associative Memory Networkmentioning

confidence: 99%

What is a Correlation Machine?

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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“…The ANNs based-models have been effectively implemented in several hydrological modelling including runoff prediction, rainfall forecasting and water quality prediction [12][13][14]. In this context, the utilization of the ANN methods is widened to be applied for predicting the sediment transport [15][16][17][18]. In most of these researches, the consequences of the river streamflow and the suspended sediment load have been used and made available as input while the future value of the sediment load is considered as the desired output.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan

Liang

El-Shafie

et al. 2019

Water

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Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China-Guangyuan and Beibei-were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE). The data period covers 01/04/2007-12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

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“…Recently, the artificial intelligence methods are widely employed in different hydraulic engineering fields because of the flexibility of them in simulating the complex problems such as open channel velocity simulation (Gholami et al 2015;Sun et al 2014), local scour modelling (Najafzadeh et al 2016(Najafzadeh et al , 2017, water quality modelling (Heddam 2016), sediment transportation (Lagos-Avid and Bonilla 2017; Safari et al 2016), etc. SVR is one of the most popular fields of artificial intelligence methods that is used in various different fields of water resource engineering such as modeling the discharge coefficient as one of the most important processes in designing the side weirs Hossein Zaji et al 2015;Shamshirband et al 2016;Zaji and Bonakdari 2014;Zaji et al 2015), scour depth prediction (Goel 2011;Hong et al 2012;Neerukatti et al 2013;Pal et al 2011;Sharafi et al 2016), rainfall-runoff modelling (Lin et al 2013;Nikam and Gupta 2014;Seo et al 2014;Wang et al 2013), sediment transportation (Jain 2012;Kakaei Lafdani et al 2013;Kisi 2012), Lake water level prediction (Cimen and Kisi 2009;Khan and Coulibaly 2006), and evapotranspiration estimation (Chen 2012;Kisi 2013).…”

Section: Introductionmentioning

confidence: 99%

Optimum Support Vector Regression for Discharge Coefficient of Modified Side Weirs Prediction

Zaji

Bonakdari

2017

INAE Lett

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Designing procedure of economical and safe side weirs that are used in various hydraulic structures such as intakes and deviation systems, essentially needs the ability to predict the side weirs' discharge capacity accurately. In this paper, the discharge coefficient of a modified labyrinth side weir was modeled by employing the support vector regression (SVR) method. To find the optimum SVR scenario, eight different kernel functions and six different input combinations were investigated. The accuracy of the SVR models were compared with two nonlinear regression equations from other published studies. The results showed that the SVR model with Polynomial Kernel Function and w=L; Fr 1 = sin h 0 ; w=Y 1 and w sin h 0 =Y 1 as input parameters performs better than other models in predicting the discharge coefficient. Where w, L, h 0 , and Y 1 are the height of crest, weir length, oblique side-weir included angle, and upstream flow depth, respectively. Also, the results showed that the SVR model with mean square error (RMSE) of 0.050 performs much better than the two other nonlinear regression published equations with RMSE of 0.121 and 0.4270, respectively.

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Daily suspended sediment load prediction using artificial neural networks and support vector machines

Cited by 194 publications

References 44 publications

What is a Correlation Machine?

What is a Correlation Machine?

Prediction of Suspended Sediment Load Using Data-Driven Models

Optimum Support Vector Regression for Discharge Coefficient of Modified Side Weirs Prediction

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