scholarly journals A New Comprehensive Evaluation Method for Water Quality: Improved Fuzzy Support Vector Machine

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1303 ◽  
Author(s):  
Wei Shan ◽  
Shensheng Cai ◽  
Chen Liu
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Membership Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Domestic Water ◽  
Machine Method ◽  
The Government

With the pressure of population growth and environmental pollution, it is particularly important to develop and utilize water resources more rationally, safely, and efficiently. Due to safety concerns, the government today adopts a pessimistic method, single factor assessment, for the evaluation of domestic water quality. At the same time, however, it is impossible to grasp the timely comprehensive pollution status of each area, so effective measures cannot be taken in time to reverse or at least alleviate its deterioration. Thus, the main propose of this paper is to establish a comprehensive evaluation model of water quality, which can provide the managers with timely information of water pollution in various regions. After considering various evaluation methods, this paper finally decided to use the fuzzy support vector machine method (FSVM) to establish the model that is mentioned above. The FSVM method is formed by applying the membership function to the support vector machine. However, the existing membership functions have some shortcomings, so after some improvements in these functions, a new membership function is finally formed. The model is then tested on the artificial data, UCI dataset, and water quality evaluation historical data. The results show that the improvement is meaningful, the improved fuzzy support vector machine has good performance, and it can deal with noise and outliers well. Thus, the model can completely solve the problem of comprehensive evaluation of water quality.

scholarly journals Research on support vector machine method for comprehensive evaluation after compression

2020 ◽  
Vol 145 ◽  
pp. 02072
Author(s):  
Wang Chengwang ◽  
Chen Shuai ◽  
Chen Gaojie ◽  
Lu Han
Keyword(s):  
Support Vector Machine ◽  
Hydraulic Fracture ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Kernel Functions ◽  
Support Vector ◽  
Treatment Technology ◽  
Machine Method ◽  
Fracture Width ◽  
Fracture Closure

Hydraulic fracturing is one of the important measures to increase production of oil and gas reservoirs. Pressure after the assessment of the existing technology is usually divided into direct and indirect diagnosis of hydraulic fracture. The diversity of evaluation results is due to the uncertainty and immaturity of the technology itself. Up to now, we have not found an economical and accurate hydraulic fracture evaluation method in the development and application of fracturing technology. In this paper, the pressure treatment technology after the comprehensive evaluation of boundary conditions is studied. By introducing the support vector regression theory, an evaluation model and a solution for correcting fracturing parameters are proposed. Fracturing parameters include fracture length, fracture height, fracture width, integrated fracturing fluid leakage coefficient, fracture conductivity, fracture closure pressure, and so on. For the optimization of various parameters, objective and scientific comprehensive evaluation results can be obtained by selecting different kernel functions. The results show that the model and method based on support vector machine are effective and practical.

Comprehensive Evaluation on Power Quality of Tidal Current Convertors

2019 ◽  
Vol 53 (3) ◽  
pp. 46-53
Author(s):  
Caixia Xue ◽  
Xiang-nan Wang ◽  
Ning Jia ◽  
Yuan-fei Zhang ◽  
Hai-nan Xia
Keyword(s):  
Power Generation ◽  
Support Vector Machine ◽  
Power Quality ◽  
Tidal Current ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Support Vector ◽  
Operational Data ◽  
Testing And Evaluation

AbstractWith the continuous development of testing and evaluation of tidal current convertors, power quality assessment is becoming more and more critical. According to the characteristics of Chinese tidal current power generation and power quality standards, this paper proposes a comprehensive evaluation method of power quality based on K-means clustering and a support vector machine. The fundamental purpose of the method is to automatically select the weights of various indicators in the comprehensive assessment of power quality, by which the influence of subjective factors can be eliminated. In order to achieve the above purpose, K-means clustering is used for automatically classifying the operational data into five different categories. Then, a support vector machine is used to study and estimate the relationship of the operational data and categories. Using the method proposed in the paper, the analysis of operational data of a tidal current power generation shows that calculation results can objectively reflect the power quality of the device, and the influence of subjective factors is eliminated. The method can provide a reference for the testing and evaluation of a large amount of tidal current convertors in the future.

Water Quality Evaluation Based on Multiclass Fuzzy Support Vector Machine

2010 ◽  
Vol 113-116 ◽  
pp. 708-711 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Training Model ◽  
Assessment Method ◽  
Complex Problem ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Quality Assessment Method

It has been a more complex problem for water quality assessment. And its aim is to well and truly evaluate its degree of pollution for bodies of water, which will be easy to provide some principled projects and criterions for water resource’s protection and their integration application. So, a water quality assessment method based on Multiclass Fuzzy Support Vector Machine is put forward. and a two-step cross-validation was used to search for the best combination of parameters to obtain an optimal training model. The test results show that the method proposed in this paper has an excellent performance on correct ratio compared to BP. It indicated that the performance of the proposed model is practically feasible in the application of water quality assessment.

A Support-Vector-Machine Method for Precisely Evaluating Planar Straightness Error

2012 ◽  
Vol 220-223 ◽  
pp. 1611-1614
Author(s):  
Jiang Meng
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Evaluation Method ◽  
Support Vector ◽  
Learning Approach ◽  
Support Vector Regression Model ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Machine Learning Approach ◽  
Straightness Error

To present a precise and efficient algorithm to solve planar straightness error problems, a machine-learning approach to evaluate planar straightness error was presented in this paper. According to the similarity between the least envelope zone and the support vector regression model, the SVR-based method was developed to solve the problem of straightness error. The evaluation method was compared to some existing techniques. According to the results from three datasets, it is shown that the SVR-based method can provide precise and exact values of planar straightness error.

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