Ranking water quality variables using feature selection algorithms to improve generalization capability of artificial neural networks
Küçük Resim Yok
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Academic Journals
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Artificial neural networks (ANNs) have been recently used intensively in the water resource management and planning. As the demand on the clean water increases, the estimation of important parameters using ANNs to evaluate the surface water quality has become an importance practice. In this study, feature selection methods, which are important tools in data mining, are used to shed light into a further understanding in the correlation between measured parameters of water quality variables and dissolved oxygen level. The aim is to enhance the generalization ability of ANNs in prediction of dissolved oxygen level. Three different feature selection methods are utilized to discern the inherent correlation in the water quality variables in a given data set collected from Melen River, Turkey. Results show that, a small set of variable can be identified, which effectively improves the generalization ability of ANNs.
Açıklama
WOS: 000279559800005
Anahtar Kelimeler
Water quality, artificial neural networks, Melen river, ranking
Kaynak
Scientific Research And Essays
WoS Q Değeri
Q3
Scopus Q Değeri
N/A
Cilt
5
Sayı
11
