Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)

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Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)

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作者：Ata Allah Nadiri^a ; ^{nadiri@tabrizu.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; Maryam Gharekhani^b ; ^{m_gharakhani92@ms.tabrizu.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; Rahman Khatibi^c ; ^{gtev.rex@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Sina Sadeghfam^d ; ^{s.sadeghfam@maragheh.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; Asghar Asghari Moghaddam^e ; ^{moghaddam@tabrizu.ac.ir" class="auth_mail" title="E-mail the corresponding author}
关键词：Ardabil aquifer ; Artificial intelligence models ; Nitrate ; Supervised Intelligent Committee Machine ; Vulnerability index
刊名：Science of the Total Environment
出版年：2017
出版时间：1 January 2017
年：2017
卷：574
期：Complete
页码：691-706
全文大小：2576 K

文摘

class="label">•: Use DRASTIC model to assess groundwater vulnerability of Ardabil plain aquifer.
class="label">•: Improve DRASTIC vulnerability index by artificial intelligence (AI): SVM, NF, GEP.
class="label">•: Use Supervised Intelligent Committee Machine (SICM) to improve on AI-techniques.
class="label">•: Use nitrate-N data to condition vulnerability indices (CVI) by SICM, SVM, NF, GEP.
class="label">•: Produce CVI maps of the study area by each model and SICM performs consistently.

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