Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6923102 | Computers & Geosciences | 2013 | 13 Pages |
Abstract
⺠We predict water table depth fluctuations by using genetic programming (GEP). ⺠GEP results are compared with neuro-fuzzy (ANFIS) and neural networks (NN) methods. ⺠Comparison results show that the GEP models perform better than the other models. ⺠The precipitation is found to be an effective variable on water table depth.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Jalal Shiri, Ozgur Kisi, Heesung Yoon, Kang-Kun Lee, Amir Hossein Nazemi,