Simulation of daily suspended sediment load using an improved model of support vector machine and genetic algorithms and particle swarm

Rahgoshay, Mitra; Feiznia, Sadat; Arian, Mehran; Hashemi, Seyed Ali Asghar

doi:10.1007/s12517-019-4444-7

Cited by 31 publications

(12 citation statements)

References 44 publications

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“…Another selected method was the M5Tree model due to its large data handling capability and smaller computational cost than the ANN and SVM models [29]. In recent years, MARS and M5Tree have been applied successfully in modeling runoff and sediment load [31][32][33][34][35][36][37][38][39]. Malik et al [37] compared the performance of the MARS model with the SVM-based model (least square SVM) and two ANN models (radial basis and self-organizing map neural network) for estimating daily SSL at different gauging stations in Godavari catchment, India.…”

Section: Introductionmentioning

confidence: 99%

“…They predicted the daily sediment load of the Lighvanchai and Upper Rio Grande rivers by comparing the WM5Tree with ANN and standalone M5Tree models and found that the hybrid M5Tree model outperformed the other models. Rahgoshay et al [36] applied M5Tree, MARS, and hybrid of SVM with GA and particle swarm optimization (PSO) models to predict the sediment load of two earth dams. They found that SVM hybrid models (SVM with GA and SVM with PSO) provided more precise results than the MARS and M5Tree models.…”

Section: Introductionmentioning

confidence: 99%

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Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

et al. 2021

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The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

et al. 2021

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“…However, these models are mostly built upon the assumption that the process follows a normal distribution, however the streamflow process is generally non-linear and stochastic in nature [13]. Machine learning (ML) models, which have been widely used in recent decades to model many real-world problems [14][15][16][17][18][19][20], have the unique ability to identify the complex non-linear relationships between the predictors (inputs) and targets (outputs) without the need for the physical characterization of the system or the requirement of making any underlying assumptions. Many hybrid ensemble ML models with the integration of different data preprocessing techniques such as wavelet transformations, empirical mode decomposition, etc.…”

Section: Introductionmentioning

confidence: 99%

Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm

et al. 2021

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The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

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“…MARS is a nonparametric regression model that identifies the desired pattern between inputs and desired output in the form of piecewise cubical or linear splines. MARS, M5 model tree, and SVR are models used for the prediction of flows and sediment yields in water resources [38][39][40]. However, the use of MARS is comparatively rare for sediment yield predictions.…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads

Hussan

Shahzad

Seidel

et al. 2020

Water

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The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R2 value of 0.85 and 0.74 during the training and testing period, respectively.

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Simulation of daily suspended sediment load using an improved model of support vector machine and genetic algorithms and particle swarm

Cited by 31 publications

References 44 publications

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm

Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads

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