Application of neural network model to evaluate hydro-geological parameters

Chih Yung Horng, Cheng Haw Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The study proposes to apply the artificial intelligence combining neural network control system and groundwater flow control equations to investigate the hydro-geological structure and hydraulic parameters in the area of Chou-Shui alluvial fan. We set up the closed-loop control system to simulate the variations of groundwater level in the such area .The close-loop control system collects the feedback data generated from the neural network model and conveys them to the sensor plant (groundwater flow control equation) in the control system . It is found that when the control system responses, the minimum error ( within 5.1×10-4 to 9.2×10-3 in test and predict stage ) of both the neural network model and the sensor plant is occur the results also indicate optimum hydraulic parameters that the innovative method works better than just applied neural network model and MODFLOW software to simulate the groundwater flow.

Original languageEnglish
Title of host publication2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
Pages1570-1573
Number of pages4
DOIs
Publication statusPublished - 2009 Dec 1
Event2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009 - Kaohsiung, Taiwan
Duration: 2009 Dec 72009 Dec 9

Publication series

Name2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009

Other

Other2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
CountryTaiwan
CityKaohsiung
Period09-12-0709-12-09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Software

Fingerprint Dive into the research topics of 'Application of neural network model to evaluate hydro-geological parameters'. Together they form a unique fingerprint.

Cite this