Application of Support Vector Machine in Lake Water Level Prediction

Khan, Mohammad Sajjad; Coulibaly, Paulin

doi:10.1061/(asce)1084-0699(2006)11:3(199)

Cited by 233 publications

(107 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can preferably resolve problems with small samples, and nonlinear and high dimensions. Hence, the method has been widely used in the fields of classification and regression (Oliveira et al, 2004;Khan et al, 2006). Recently, a few researchers have begun to try to apply the method in landslide and slope research.…”

Section: Introductionmentioning

confidence: 99%

Application of GA–SVM method with parameter optimization for landslide development prediction

Jiming

2014

Nat. Hazards Earth Syst. Sci.

109

View full text Add to dashboard Cite

Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multifactor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multifactor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of GA–SVM method with parameter optimization for landslide development prediction

Jiming

2014

Nat. Hazards Earth Syst. Sci.

109

View full text Add to dashboard Cite

show abstract

“…The SVM method, proposed by Vapnik (1995), is a promising method in the data-driven prediction field. Recently, SVM has been extensively applied to address various water resource problems, such as stream flow forecasting, waterlevel prediction, and water quality parameter simulation (Khan and Coulibaly 2006;Lin et al 2006;Noori et al 2015;Yoon et al 2011). However, studies related to DO prediction through the SVM model are few.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Shang

Dahlgren

et al. 2017

Environ Sci Pollut Res

101

View full text Add to dashboard Cite

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resources and controlling pollution. Artificial intelligence (AI) models have been successfully applied for modeling DO content in aquatic ecosystems with limited data. However, the efficacy of these AI models in predicting DO levels in the hypoxic river systems having multiple pollution sources and complicated pollutants behaviors is unclear. Given this dilemma, we developed a promising AI model, known as support vector machine (SVM), to predict the DO concentration in a hypoxic river in southeastern China. Four different calibration models, specifically, multiple linear regression, back propagation neural network, general regression neural network, and SVM, were established, and their prediction accuracy was systemically investigated and compared. A total of 11 hydro-chemical variables were used as model inputs. These variables were measured bimonthly at eight sampling sites along the ruralsuburban-urban portion of Wen-Rui Tang River from 2004 to 2008. The performances of the established models were assessed through the mean square error (MSE), determination coefficient (R 2 ), and Nash-Sutcliffe (NS) model efficiency. The results indicated that the SVM model was superior to other models in predicting DO concentration in Wen-Rui Tang River. For SVM, the MSE, R 2 , and NS values for the testing subset were 0.9416 mg/L, 0.8646, and 0.8763, respectively. Sensitivity analysis showed that ammonium-nitrogen was the most significant input variable of the proposal SVM model. Overall, these results demonstrated that the proposed SVM model can efficiently predict water quality, especially for highly impaired and hypoxic river systems.

show abstract

“…The ELM is free from the complications faced by gradient-based algorithms (e.g., learning rate, learning epochs and local minima) [ACHARYA et al 2014;BELAYNEH, ADAMOWSKI 2014;ŞAHIN et al 2014]. Despite their widespread use, ANNs suffer from difficulty in training predictors and may not, therefore, produce a unique solution over various runs due to different weights [COULIBALY, EVORA 2007;KHAN, COULIBALY 2006].…”

Section: Introductionmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

View full text Add to dashboard Cite

The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

show abstract

Application of Support Vector Machine in Lake Water Level Prediction

Cited by 233 publications

References 14 publications

Application of GA–SVM method with parameter optimization for landslide development prediction

Application of GA–SVM method with parameter optimization for landslide development prediction

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

Water demand forecasting using extreme learning machines

Contact Info

Product

Resources

About