Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model
Bibliographic Data
| ID | 15465476 |
|---|---|
| Authors | Yan An (0000-0003-4223-5708, Beihang University), An Yan (0000-0003-2368-4858, Beihang University), Zhihong Zou (0000-0003-4828-3145, Beihang University, corresponding author), Ranran Li (0000-0003-4236-7209, Beihang University) |
| Year | 2014 |
| Volume | 11 |
| Issue | 4 |
| Pages | 3507-3520 |
| Publication date | 2014-03-26 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph110403507 |
| PMID | 24675643 |
| PMCID | PMC4025034 |
| OpenAlex | W1969374506 |
| Language | EN |
| Citations received | 3 |
| References cited | 22 |
A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD5, NH3-N, TP, and F. coli (Reduct A). Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD5, NH3-N, TP, TN, F, and F. coli (Reduct B), was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system
Data mining · Fuzzy logic · Reduct · Rough set · Water quality · Computer Science · Hydrological Forecasting Using AI · Mathematics · Rough Sets and Fuzzy Logic · Water Quality and Pollution Assessment · Artificial Intelligence
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| Citations per year | 0.27 |
|---|---|
| Citation span | 2015 - 2020 (6) |
| Highly cited | No |