MARC details
000 -LEADER |
fixed length control field |
02442nam a22002657a 4500 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
ZW-GwMSU |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20230502102331.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
230502b |||||||| |||| 00| 0 eng d |
040 ## - CATALOGING SOURCE |
Original cataloging agency |
MSU |
Transcribing agency |
MSU |
Description conventions |
rda |
100 ## - MAIN ENTRY--PERSONAL NAME |
Personal name |
MA, Rong |
245 ## - TITLE STATEMENT |
Title |
FRFI model application in groundwater non-point source pollution evaluation |
Remainder of title |
a case study in the Luoyang Basin of North Henan province, China |
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
Place of production, publication, distribution, manufacture |
Verlag |
Name of producer, publisher, distributor, manufacturer |
Springer |
Date of production, publication, distribution, manufacture, or copyright notice |
2013 |
336 ## - CONTENT TYPE |
Source |
rdacontent |
Content type term |
text |
Content type code |
txt |
337 ## - MEDIA TYPE |
Source |
rdamedia |
Media type term |
unmediated |
Media type code |
n |
338 ## - CARRIER TYPE |
Source |
rdacarrier |
Carrier type term |
volume |
Carrier type code |
nc |
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
Title |
Environmental earh sciences |
Volume/sequential designation |
Volume , number , |
520 ## - SUMMARY, ETC. |
Summary, etc. |
The 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
non-point source pollution |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
fuzzy-rough sets |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
fuzzy inference technique |
700 ## - ADDED ENTRY--PERSONAL NAME |
Personal name |
SHI, Jiansheng |
700 ## - ADDED ENTRY--PERSONAL NAME |
Personal name |
LIU, Jichao |
856 ## - ELECTRONIC LOCATION AND ACCESS |
Uniform Resource Identifier |
https://doi.org/10.1007/s12665-012-1712-1 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
Library of Congress Classification |
Koha item type |
Journal Article |