Prediction of water quality time series data based on least squares support vector machine

Tan, Guohua; Yan, Jianzhuo; Gao, Chen; Yang, Shaohua

doi:10.1016/j.proeng.2012.01.1162

Cited by 97 publications

(42 citation statements)

References 5 publications

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“…Besides ANNs, SVMs have also been extensively employed for prediction of river DO concentration [46][47][48]. Liu et al [34] optimized the parameters of SVM using PSO to develop an SVM-PSO model for the prediction of DO concentration.…”

Section: Introductionmentioning

confidence: 99%

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ehteram

Sharafati

et al. 2018

Water

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Abstract:The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.

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Section: Introductionmentioning

confidence: 99%

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ehteram

Sharafati

et al. 2018

Water

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show abstract

“…As one of the choices to describe a future trend, time series analysis can be applied to reflect dynamic variable from one time to another [3]. From the previous studies by using Least Square [4][5][6][7][8] and Moving Average [3] [9][10][11][12][13][14] method, the data and analysis showed the future prediction. It was defined as a management process in making decision.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Poor Inhabitant Number Using Least Square and Moving Average Method

Ekawati¹

2015

ijacsa

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Abstract-The number of poor inhabitant in South Kalimantan decreased within the last three years compared with the previous years. The numbers of poor inhabitant differs from time to time. This scaled dynamical number has been a problem for the local government to take proper polices to solve this matter. It will then be necessary to predict a potential number of poor inhabitants in the next year as the basis on subsequent policy making. This research will apply both Least Square and Moving Average method as the measurement to count prediction values. From the results of the study, the prediction analysis by using those two methods is valid for predicting acquired number of poor inhabitant for the next period according to the data from the previous year. Based on the study, the validity of Least Square was 98.35% and Moving Average was 98.79% by using the data in the last seven years.

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“…Prediction utilizing LSSVM in nonlinear water quality time series data has been presented by Tan, Yan, Gao and Yang [27]. The purpose of the study is to predict the total phosphorus.…”

Section: Time Series Forecastingmentioning

confidence: 99%

Forecasting Model Based on LSSVM and ABC for Natural Resource Commodity

Yusof¹,

Kamaruddin²,

Husni³

et al. 2013

IJCTE

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Prediction of water quality time series data based on least squares support vector machine

Cited by 97 publications

References 5 publications

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Prediction of Poor Inhabitant Number Using Least Square and Moving Average Method

Forecasting Model Based on LSSVM and ABC for Natural Resource Commodity

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