Abstract
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.
Highlights
Water is an essential resource for human survival and development, and it has become more and more important because of the growth of population and the deterioration of environmental pollution [1,2,3,4,5]
Existing water quality assessment methods can be broadly divided into three categories: traditional assessment method, evaluation method based on fuzzy mathematics and machine learning method
This paper finds that the problem of noise points that may be formed during water quality evaluation has not been solved
Summary
Water is an essential resource for human survival and development, and it has become more and more important because of the growth of population and the deterioration of environmental pollution [1,2,3,4,5]. It helps managers to rationally allocate and utilize water resources, and brings a more comprehensive understanding of water pollution in various areas at the same time. Existing water quality assessment methods can be broadly divided into three categories: traditional assessment method, evaluation method based on fuzzy mathematics and machine learning method. Traditional water quality assessment methods such as the single factor assessment, grading score method, function evaluation method, etc. Used a series of calculations to obtain a comprehensive score to evaluate water quality [6]. Since the classification boundaries and pollution levels are both fuzzy
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