Multi-class cluster support vector machines

Wang, Zhiyi; Yang, Yifan

doi:10.3724/sp.j.1087.2010.00143

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…However, some methods for water quality assessment are usually employed with incapacity due to complicated relationships between water quality monitoring parameters and qualitative status. The methods used for the water quality monitoring, are: Matter Element Model, Fuzzy Synthetic Evaluation, Gray Analysis Method, Logistic Curve Model, Attribute Recognition Model and Fuzzy Logic (Liu & Zou, 2012 ;Wang et al, 2019;ZOU et al, 2006;Jin et al, 2003;Wang, Wang & Zou, 2008;WANG & YANG, 2010). The Artificial Neural Networks (ANN) method is frequently considered as a solution to this kind of modeling process among others the water quality monitoring (Areerachakul & Sanguansintukul, 2010).…”

Section: Introductionmentioning

confidence: 99%

A decision fusion method based on classification models for water quality monitoring

Ladjal

Bouamar

Brik

et al. 2022

Environ Sci Pollut Res

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-Monitoring of water quality is one of the world's main intentions of countries. In this paper we present the use of Principal Component Analysis (PCA) combined with Support Vector Machines (SVM) and Artificial Neural Network (ANN) based on Decision Templates combination data fusion method. SVM and ANN are employed in classification stage. Decision Templates is applied to increase accuracy of the water quality classification compared to others combination data fusion methods. This work concerned the water quality assessment from Tilesdit dam (Algeria) that it permitted us to acquire additional knowledge and information about study area and to obtain an intelligent monitoring system. The Multi-Layer Perceptron network (MLP) and the One-Against-All strategy for SVM method are have been widely used. The training step is performed in this paper using these techniques to classify water quality from various physicochemical parameters such as temperature, pH, electrical conductivity and turbidity, etc. Eight of them were collected in the period 2009-2018 from the study area. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected data set corresponding to the accuracy and running time of training and test phases, and robustness, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without features selection of the input data. The combination by Decision Templates of two classifiers enhanced expressively the results of the proposed monitoring framework that had prove a considerable ability in surface water quality assessment.

show abstract

Section: Introductionmentioning

confidence: 99%

A decision fusion method based on classification models for water quality monitoring

Ladjal

Bouamar

Brik

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

A Decision Fusion Method Based on Classification Models for Water Quality Monitoring

Ladjal

Bouamar

Brik

et al. 2021

Preprint

View full text Add to dashboard Cite

Monitoring of water quality is one of the world's main intentions of countries. In this paper we present the use of Principal Component Analysis (PCA) combined with Support Vector Machines (SVM) and Artificial Neural Network (ANN) based on Decision Templates combination data fusion method. SVM and ANN are employed in classification stage. Decision Templates is applied to increase accuracy of the water quality classification compared to others combination data fusion methods. This work concerned the water quality assessment from Tilesdit dam (Algeria) that it permitted us to acquire additional knowledge and information about study area and to obtain an intelligent monitoring system. The Multi-Layer Perceptron network (MLP) and the One-Against-All strategy for SVM method are have been widely used. The training step is performed in this paper using these techniques to classify water quality from various physicochemical parameters such as temperature, pH, electrical conductivity and turbidity, etc. Eight of them were collected in the period 2009-2018 from the study area. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected data set corresponding to the accuracy and running time of training and test phases, and robustness, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without features selection of the input data. The combination by Decision Templates of two classifiers enhanced expressively the results of the proposed monitoring framework that had prove a considerable ability in surface water quality assessment.

show abstract

Multi-class cluster support vector machines

Cited by 2 publications

References 3 publications

A decision fusion method based on classification models for water quality monitoring

A decision fusion method based on classification models for water quality monitoring

A Decision Fusion Method Based on Classification Models for Water Quality Monitoring

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