Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)
详细信息 查看全文
- 作者：Ata Allah Nadiria ; nadiri@tabrizu.ac.ir" class="auth_mail" title="E-mail the corresponding author ; Maryam Gharekhanib ; m_gharakhani92@ms.tabrizu.ac.ir" class="auth_mail" title="E-mail the corresponding author ; Rahman Khatibic ; gtev.rex@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Sina Sadeghfamd ; s.sadeghfam@maragheh.ac.ir" class="auth_mail" title="E-mail the corresponding author ; Asghar Asghari Moghaddame ; 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
- •
Use DRASTIC model to assess groundwater vulnerability of Ardabil plain aquifer.
- •
Improve DRASTIC vulnerability index by artificial intelligence (AI): SVM, NF, GEP.
- •
Use Supervised Intelligent Committee Machine (SICM) to improve on AI-techniques.
- •
Use nitrate-N data to condition vulnerability indices (CVI) by SICM, SVM, NF, GEP.
- •
Produce CVI maps of the study area by each model and SICM performs consistently.