Support vector machines in water quality management

Singh, Kunwar P.; Basant, Nikita; Gupta, Shikha

doi:10.1016/j.aca.2011.07.027

Cited by 274 publications

(141 citation statements)

References 44 publications

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“…During SVM model development, the determination of the optimal combination of C and g is greatly important in constructing high-performance regression models. C is the regularization parameter that controls the degree of empirical error in optimization problem, and g is the RBF kernel parameter that significantly affects the generalization ability of SVM (Noori et al 2011;Singh et al 2011). In the present study, the fivefold CV and grid search method were employed to determine the optimal pairwise C and g during the construction of the SVM model.…”

Section: Results Of Svm Modelmentioning

confidence: 99%

“…The water quality deterioration can be attributed to urbanization, population growth, excessive water consumptions, industrial wastewater discharge, and agricultural activities, while the system lacks adequate wastewater treatment facilities (Chen et al 2016;Gupta 2008;Singh et al 2011). The increasingly serious pollution causes low dissolved oxygen (DO) levels and worsens life conditions in aquatic systems.…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Results Of Svm Modelmentioning

confidence: 99%

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

show abstract

“…Singh et al [68] Widodo and Yang [70] developed an intelligent machine prognostics system using survival analysis (SA) and support vector machine (SVM). First, SA utilizes censored and uncensored data collected from condition monitoring (CM) routine and then estimates the survival probability of failure time of machine components.…”

Section: Quality Classificationmentioning

confidence: 99%

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Rostami

Dantan

Homri

2015

Int. J. Metrol. Qual. Eng.

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractIn many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases.Due to advances in data collection systems and analysis tools, data mining (DM)has widely been applied for quality assessment (QA) in manufacturing industries.In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set.Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

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“…(Buono et al, 2004) in their research entitled Classification of Land Cover on Multispectral Image Landsat TM using Probabilistic Neural Networks, the value of the accuracy was 64.2%. (Baret and Samuel, 2008;Discriminants, 2010;Lau et al, 2008;Santosa, 1995;Sharma et al, 2011;Wang et al, 2012) state that SVM is a technique to make predictions, both in classification and in regression where the SVM was in one class with Neural Network and both were in the supervised learning class. The concept of SVM can be explained simply as an attempt to find the best dividing line (hyperplane) of sharing the possible alternative hyperplane (Campbell and Ying, 2011;Hsu et al, 2008;Gao et al, 2012;Guan et al, 2013;Ibrikci et al, 2012;Liao et al, 2012;Pandey et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Identification of Characteristics of Land Cover in Mangkauk Catchment Area Using Support Vector Machine (SVM) And Artificial Neural Network (ANN)

Ridwan¹,

Bisri²,

Yusran³

et al. 2017

American Journal of Applied Sciences

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Land cover is anything that includes any types of appearance on the surface of the earth on a particular land. Information related to land cover can be used as at the parameter to determine the amount of runoff in a catchment area. This study was conducted in the Catchment Area (CA) of Mangkauk using Landsat 8 OLI/TIRS 2014 scene path/row 117/62 with the methods of Support Vector Machine (SVM) and Artificial Neural Network (ANN). The classification of land cover in Mangkauk catchment area included forests, plantations, shrubs, reeds/grasses, rice fields, open lands, settlements and water body. Based on the accuracy test of land cover classification using SVM, the value of the overall accuracy was 97.22% with Kappa Coefficient 0.96, while using ANN 86.33% with Kappa Coefficient 0.79.

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Support vector machines in water quality management

Cited by 274 publications

References 44 publications

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

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

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Identification of Characteristics of Land Cover in Mangkauk Catchment Area Using Support Vector Machine (SVM) And Artificial Neural Network (ANN)

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