| 000 | 02442nam a22002657a 4500 | ||
|---|---|---|---|
| 003 | ZW-GwMSU | ||
| 005 | 20230502102331.0 | ||
| 008 | 230502b |||||||| |||| 00| 0 eng d | ||
| 040 |
_aMSU _cMSU _erda |
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| 100 | _aMA, Rong | ||
| 245 |
_aFRFI model application in groundwater non-point source pollution evaluation _ba case study in the Luoyang Basin of North Henan province, China |
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| 264 |
_aVerlag _bSpringer _c2013 |
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| 336 |
_2rdacontent _atext _btxt |
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| 337 |
_2rdamedia _aunmediated _bn |
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| 338 |
_2rdacarrier _avolume _bnc |
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| 440 |
_aEnvironmental earh sciences _vVolume , number , |
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| 520 | _aThe traditional non-point source (NPS) pollution models mainly focus on the flow path of NPS pollutants and attenuation during the flow. Extensive data set preparation and complex results analysis for these models are the most common problems encountered by the model user. In this study a new model, fuzzy-rough sets and fuzzy inference (FRFI), was introduced to evaluate groundwater NPS pollution. The proposed model involves two steps: the algorithm of fuzzy-rough sets attribute reduction (FRSAR) was applied to yield minimal decision rules from the fuzzy information system (FIS); the fuzzy inference technique was then used to forecast a groundwater synthesis pollution index based on the minimal decision rules. This model was applied in the Luoyang Basin, examining NPS pollution factors and hydrochemical variables data to validate the effectiveness of this model. The results indicate that it is only required to collect five NPS pollution factors or three hydrochemical variables; the groundwater synthesis pollution index can be predicted using the FRFI model. The prediction error is restricted to 2.9–6.1 % and 0.8–1.6 %, respectively. Therefore, the costs of computation and monitoring can be decreased, and the user is not required to prepare massive model parameters for the FRFI model. According to analyze the correlation between NPS pollution factors and hydrochemical variables, prevention measures are provided for treatment of the endemic disease and eutrophication. The FRFI model can be suitable for groundwater NPS pollution evaluation systems. | ||
| 650 | _anon-point source pollution | ||
| 650 | _afuzzy-rough sets | ||
| 650 | _afuzzy inference technique | ||
| 700 | _aSHI, Jiansheng | ||
| 700 | _aLIU, Jichao | ||
| 856 | _uhttps://doi.org/10.1007/s12665-012-1712-1 | ||
| 942 |
_2lcc _cJA |
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| 999 |
_c161918 _d161918 |
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