scholarly journals A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction

2013 ◽  
Vol 58 (3-4) ◽  
pp. 458-465 ◽  
Author(s):  
Shuangyin Liu ◽  
Haijiang Tai ◽  
Qisheng Ding ◽  
Daoliang Li ◽  
Longqin Xu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Water Quality ◽  
Support Vector Regression ◽  
Hybrid Approach ◽  
Support Vector ◽  
Quality Prediction ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Water Quality Prediction ◽  
Aquaculture Water

Water Quality Prediction Based on Grey-Support Vector Regression

2012 ◽  
Vol 433-440 ◽  
pp. 263-267
Author(s):  
Jing Du ◽  
Tao Tao
Keyword(s):  
Water Quality ◽  
Support Vector Regression ◽  
Hybrid Method ◽  
Development Trend ◽  
Grey Model ◽  
Support Vector ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Input And Output ◽  
Nerual Network

Water quality prediction has a great significance to evalute the quality development trend of water and make the planning of water processing. In the study, a novel hybrid method of grey model and support vector regression is proposed to improve the prediction accuracy of sypport vector regression. COD is the important composition in the polluting water. Thus, COD is used as evaluation index of polluting water in the paper. The quality prediction values of input and output water of BP nerual network and the hybrid method of grey model and support vector regression are computated respectively. It is indicated that the water quality prediction by using the hybrid method of grey model and support vector regression is superior to BP nerual network.

Hybrid Non-Dominated Sorting Genetic Algorithm

2018 ◽  
pp. 264-286 ◽  
Author(s):  
Sankhadeep Chatterjee ◽  
Sarbartha Sarkar ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Soumya Sen
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Quality Prediction ◽  
Learning Methods ◽  
Nsga Ii ◽  
Water Quality Prediction ◽  
Machine Learning Methods ◽  
Precautionary Measures

Water pollution due to industrial and domestic reasons is highly affecting the water quality. In undeveloped and developed countries, it has become a major reason behind a number of water borne diseases. Poor public health is putting an extra economic liability in order to deploy precautionary measures against these diseases. Recent research works have been directed toward more sustainable solutions to this problem. It has been revealed that good quality of water supply can not only improve the public health, it also accelerates economic growth of a geographical location as well. Water quality prediction using machine learning methods is still at its primitive stage. Besides, most of the studies did not follow any national or international standard for water quality prediction. In the current work, both the problems have been addressed. First, advanced machine learning methods, namely Artificial Neural Networks (ANNs) supported by a well-known multi-objective optimization algorithm called the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been used to classify the water samples into two different classes. Secondly, Indian national standard for water quality (IS 10500:2012) has been utilized for this classification task. The hybrid NN-NSGA-II model is compared with another two well-known meta-heuristic supported ANN classifiers, namely ANN trained by Genetic Algorithm (NN-GA) and by Particle Swarm Optimization (NN-PSO). Apart from that, the support vector machine (SVM) has also been included in the comparative study. Besides analysing the performance based on several performance measuring methods, the statistical significance of the results obtained by NN-NSGA-II has been judged by performing Wilcoxon rank sum test with 5% confidence level. Results have indicated the ingenuity of the proposed NN-NSGA-II model over the other classifiers under current study.

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