Hybrid Soft Computing Approach for Determining Water Quality Indicator: Euphrates River
Recent approaches toward solving the regression problems which are characterized by dynamic and nonlinear pattern such as machine learning modeling (including artificial intelligence (AI) approaches) have proven to be useful and successful tools for prediction. Approaches that integrate predictive m...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Springer
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/ http://umpir.ump.edu.my/id/eprint/18339/1/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River.pdf http://umpir.ump.edu.my/id/eprint/18339/2/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River%201.pdf |
Internet
http://umpir.ump.edu.my/id/eprint/18339/http://umpir.ump.edu.my/id/eprint/18339/
http://umpir.ump.edu.my/id/eprint/18339/
http://umpir.ump.edu.my/id/eprint/18339/1/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River.pdf
http://umpir.ump.edu.my/id/eprint/18339/2/Hybrid%20soft%20computing%20approach%20for%20determining%20water%20quality%20indicator-%20Euphrates%20River%201.pdf