2016
DOI: 10.1007/s12665-016-5435-6
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Modeling river discharge time series using support vector machine and artificial neural networks

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Cited by 57 publications
(24 citation statements)
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“…Finally, 10 sensitivity parameters were selected: runoff curve number (CN 2 ), evaporation compensation coefficient of the soil (ESCO), available water of the soil (SOL_AWC), basis flow coefficient (ALPHA_BF), groundwater re-evaporation coefficient (GW_REVAP), surface runoff coefficient (SURLAG), linear index of the silt re-carried (SPCON), effective hydraulic conductivity of main river channel (CH_K 2 ), residual decomposition factor (RSDCO), and the power index of the silt re-carried (SPEXP). On the basis of model sensitivity analysis, the SCE-UA algorithm proposed by DUAN [30][31][32] was applied to adjust the above sensitivity parameters and determine the numerical value of each parameter by satisfying the two-control standard for E ns and R 2 , which are greater than 0.85, respectively ( Table 6).…”
Section: Calibrating and Validating The Parametersmentioning
confidence: 99%
“…Finally, 10 sensitivity parameters were selected: runoff curve number (CN 2 ), evaporation compensation coefficient of the soil (ESCO), available water of the soil (SOL_AWC), basis flow coefficient (ALPHA_BF), groundwater re-evaporation coefficient (GW_REVAP), surface runoff coefficient (SURLAG), linear index of the silt re-carried (SPCON), effective hydraulic conductivity of main river channel (CH_K 2 ), residual decomposition factor (RSDCO), and the power index of the silt re-carried (SPEXP). On the basis of model sensitivity analysis, the SCE-UA algorithm proposed by DUAN [30][31][32] was applied to adjust the above sensitivity parameters and determine the numerical value of each parameter by satisfying the two-control standard for E ns and R 2 , which are greater than 0.85, respectively ( Table 6).…”
Section: Calibrating and Validating The Parametersmentioning
confidence: 99%
“…ANN and MT performed equally well for estimation of discharge however, MT appear to be more transparent than ANN. Ghorbani et al (2016) compared the performance of SVM, ANN, MLR and simple rating curve (RC) reporting SVM to be best among them for estimating the discharge. Maghrebi and Ahmadi (2017) proposed an innovative approach for modelling discharge rating curve using isovel contours with the corresponding hydro-geometric parameters of the cross sections and dimensional analysis to interrelate the discharges at two different stages.…”
Section: Materials and Methodologymentioning
confidence: 99%
“…The conventional support vector machine (SVM) is one of the machine learning methods [25]. The principle of machine learning is to minimize structural risk and achieve data classification or regression by applying kernel function and high-dimensional data simplification schemes (as presented in Equation (1)).…”
Section: Conventional Lssvm Modelmentioning
confidence: 99%