Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree

Heddam, Salim; Kişi, Özgür

doi:10.1016/j.jhydrol.2018.02.061

Cited by 157 publications

(68 citation statements)

References 60 publications

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“…In addition, as can be observed from Figure 3, the three selected stations are located at different regions which can allow us to examine the performance of the proposed models under different geographical and climatic conditions. Furthermore, Heddam and Kisi (2018) developed a model for the prediction of DO concentration at the same stations [51]. In order to carry out a rational comparison with the previous research findings, the proposed models are developed for the prediction of DO concentration at the same selected stations.…”

Section: Case Studymentioning

confidence: 99%

“…An excellent employment of LSSVM for prediction of DO in crab ponds of China was conducted by [50]; the authors found higher accuracy of LSSVM compared with RBFNN. Recently, a new study compared the performance of LSSVM, multivariate adaptive regression splines (MARS), and M5 model trees in the prediction of DO concentration and reported the performance of LSSVM as being very close to that of MARS [51].…”

Section: Introductionmentioning

confidence: 99%

“…M5 and MARS have been used for the comparison of the performance of LSSVM-BA as they have been found to be highly efficient in the prediction of hydrological variables in a number of recent studies [51,66]. Heddam and Kisi (2018) employed both MARS and M5 for the prediction of DO concentration at the same locations to compare their performance with LSSVM [51]. Therefore, comparison of performance of LSSVM-BA with MARS and M5 will help to assess the improvement of the performance of LSSVM after integration with BA.…”

Section: Introductionmentioning

confidence: 99%

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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: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

“…This method takes water temperature, pH, specific conductance and discharge as input data and inputs them into three respective models. The experimental results showed that the three models had the best prediction performance for dissolved oxygen in water and the prediction accuracy of the three models was different at different stations [25]. Wu, et al used a modular artificial neural network (MANN) and data preprocessing by singular spectrum analysis (SSA) to eliminate the lag effect.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression for Prediction of Sulfate Content in Lakes of China

Zhao

Guo

Han

et al. 2019

J. Eng. Technol. Sci.

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In recent years, environmental pollution has become more and more serious, especially water pollution. In this study, the method of Gaussian process regression was used to build a prediction model for the sulphate content of lakes using several water quality variables as inputs. The sulphate content and other variable water quality data from 100 stations operated at lakes along the middle and lower reaches of the Yangtze River were used for developing the four models. The selected water quality data, consisting of water temperature, transparency, pH, dissolved oxygen conductivity, chlorophyll, total phosphorus, total nitrogen and ammonia nitrogen, were used as inputs for several different Gaussian process regression models. The experimental results showed that the Gaussian process regression model using an exponential kernel had the smallest prediction error. Its mean absolute error (MAE) of 5.0464 and root mean squared error (RMSE) of 7.269 were smaller than those of the other three Gaussian process regression models. By contrast, in the experiment, the model used in this study had a smaller error than linear regression, decision tree, support vector regression, Boosting trees, Bagging trees and other models, making it more suitable for prediction of the sulphate content in lakes. The method proposed in this paper can effectively predict the sulphate content in water, providing a new kind of auxiliary method for water detection.

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“…The methods further demonstrated the ability of DDMs to predict DO in complex, urbanised watersheds, and had the ability to predict the risk of low DO events as well. Heddam and Kisi (2018) implemented three types of artificial intelligence techniques, least square support vector machine, multivariate adaptive regression splines, and M5 Model Tree in order to predict the concentration of DO in different stations in different rivers in the USA. Three indices including R, RMSE and mean absolute error (MAE) were used to assess and evaluate the model performance.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Two Data-Driven Models to Predict Hypolimnetic Dissolved Oxygen Concentration: A Case Study of the Seymareh Reservoir in Iran

Nokhandan¹,

Snieder²,

Khan

et al. 2019

J ENVIRON INFORM LET

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Dissolved oxygen concentration (DO) is a crucial factor in maintaining aquatic ecosystem health. In this research, two data-driven modelling (DDM) techniques, multiple linear regression (MLR) and artificial neural networks (ANN), were developed, implemented and compared to predict the DO in the hypolimnetic layer of Seymareh Reservoir in Iran. Low DO in this Reservoir lead to a fish kill event and thus, this reservoir is of interest to water managers in the region. Water quality monitoring data from the Reservoir and an upstream river were used for training the models. In addition, two input variable selection methods, linear correlation analysis and combined neural pathway strength analysis (CNPSA, a nonlinear variable selection method) were developed and compared to determine the most significant inputs to predict hypolimnetic DO. A systematic method to select the optimum architecture of the network is proposed and tested. While these two approaches have been investigated previously, this research focuses on creating a systematic approach to combining two sources of uncertainty of DDM models. Additionally, the performance of CNPSA has not been compared to linear variable selection techniques. This research demonstrates the importance of using systematic input selection and network design for improved DO prediction in a large Reservoir. The performance of the models was quantified using the Nash-Sutcliffe efficiency and root mean squared error, which demonstrated that the ANN approach had better performance compared to the MLR model. The approach demonstrates that by using a systematic input variable selection approach combined with an optimised network architecture, a high performance of DO prediction can be achieved using easily measured upstream input data.

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Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree

Cited by 157 publications

References 60 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

Gaussian Process Regression for Prediction of Sulfate Content in Lakes of China

A Comparison of Two Data-Driven Models to Predict Hypolimnetic Dissolved Oxygen Concentration: A Case Study of the Seymareh Reservoir in Iran

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