Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load

Nourani, Vahid; Alizadeh, Farhad; Roushangar, Kiyoumars

doi:10.1007/s11269-015-1168-7

Cited by 54 publications

(9 citation statements)

References 32 publications

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“…A well-advised application of this tool is to select an appropriate covariance function or kernel and tune related hyper parameters. A number of kernels are discussed by researchers, but studies suggest the effectiveness of radial basis kernel function in the case of machine learning approaches in the majority of civil engineering applications (Gill et al 2006;Goel and Pa 2009;Nourani et al 2016). In the present study different GPR structures were analyzed via various kernel functions and mentioned input combinations.…”

Section: Gpr Modeling Developmentmentioning

confidence: 99%

“…Artificial intelligences (AI), specially machine learning approaches, are remarkable forecasting tools which in the recent decade has been implemented in various fields of civil engineering studies (e.g., Sun et al 2014;Roushangar et al 2014a, b;Samui 2012;Koosheh 2015, Roushangar andAlizadeh 2015;Nourani et al 2016). The Gaussian process regressions (GPR) an effective kernel-based machine learning algorithm, is capable to be applied to probabilistic streamflow forecasting (Sun et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Daily Seepage Discharge of an Earth Dam Using Wavelet–Mutual Information–Gaussian Process Regression Approaches

2016

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Because of their sensitive structure, earth dams might face failure due to seepage phenomenon. In order to prevent such failure, some equipment like piezometers are installed in the body or foundation of earth dams. This study investigated the importance of piezometer installation level in dam body or foundation using mutual information-wavelet-Gaussian process regression. 27 Piezometers in three section along with reservoir level were employed to predict onestep-ahead seepage discharge of Zonouz earth dam. The daily data of 1 year of piezometer level and reservoir level were collected for this purpose. In order to find the best possible input combination, three groups of modeling scenarios were defined using piezometers and reservoir level time series. As some input combinations had more than two variables, decomposed time series were imposed into mutual information (MI) tool in order to decrement input variables and find the most correlated input-output features. Afterward, mentioned features were imposed into optimized Gaussian process regression (GPR) to be predicted. Different kernels were selected as core tool of GPR, but results demonstrated the capability of radial basis function (RBF) kernel. GPR-RBF structure were optimized using cross-validation technique. Results indicated that input combination including piezometer level and reservoir level of section II, especially piezometer 203 time series led to the best result among all scenarios.

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Section: Gpr Modeling Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Seepage Discharge of an Earth Dam Using Wavelet–Mutual Information–Gaussian Process Regression Approaches

2016

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“…SVM is a data-driven machine learning model that has been widely applied to hydrologic prediction, such as short-term or long-term streamflow and sediment yield forecasting [4,[15][16][17][18][19], water quality prediction [20,21], precipitation, temperature and evapotranspiration simulation [22,23], and the process of parameterization [12]. The essential characteristic of the SVM method is its ability to efficiently and accurately predict the nonlinear relationship between input and output variables without considering their internal physical links.…”

Section: Introductionmentioning

confidence: 99%

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Yuan

Forshay

2021

PLoS ONE

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Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is “Satisfactory”. This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.

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“…Due to the non-linear behavior of the suspended sediment problem and stochastic nature of the sediment particle movement in the flow, conventional computational methods may fail for accurate suspended sediment load prediction. To this end, AI approaches have been commonly implemented for sediment transport modeling in rivers (Nourani et al 2016;Kisi and Yaseen 2019). Applied AI techniques for suspended sediment load prediction can be classified as stand-alone and hybrid algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…As examples of application of stand-alone algorithms, Tayfur (2002), Alp and Cigizoglu (2007), and Mustafa et al (2012) investigated the efficiency of artificial neural networks (ANN) for suspended load prediction. Satisfactory performances of genetic algorithm (GA), neurofuzzy (NF), neural differential evolution (NDE), least square support vector regression (LSSVR), support vector machine (SVM), multivariate adaptive regression spline (MARS), and classification and regression tree (CART) as stand-alone models for suspended sediment load prediction were reported by Altunkaynak (2009), Rajaee et al (2009), Kisi (2010), Kumar et al (2016), Nourani et al (2016), Yilmaz et al (2018), andChoubin et al (2018), respectively. Hybrid models may be implemented for suspended sediment transport modeling to improve the computational performance of the stand-alone models.…”

Section: Introductionmentioning

confidence: 99%

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

Meshram

Safari

Khosravi

et al. 2020

Environ Sci Pollut Res

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Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period . The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R 2 ). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.

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Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load

Cited by 54 publications

References 32 publications

Forecasting Daily Seepage Discharge of an Earth Dam Using Wavelet–Mutual Information–Gaussian Process Regression Approaches

Forecasting Daily Seepage Discharge of an Earth Dam Using Wavelet–Mutual Information–Gaussian Process Regression Approaches

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

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