| Issue |
E3S Web Conf.
Volume 81, 2019
The 1st International Symposium on Water Resource and Environmental Management (WREM 2018)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/e3sconf/20198101017 | |
| Published online | 30 January 2019 | |
A model for soil moisture dynamics estimation based on artificial neural network
1
State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, School of Water Resources and Hydroelectric Engineering, Xi’an University of Technology, Xi’an 710048, China ;
2
Xiaolangdi Project Construction & Management Center, Ministry of Water Resources, Zhengzhou 450000, China ;
3
Henan Institute of Science and Technology, Xinxiang 453600, China
* Corresponding author: 232771432@qq.com
Research on soil moisture estimation models can effectively improve the growth environment of crops. In this paper, the author studied the artificial neural network and variation pattern of soil moisture. Then, application of the model for water diversion estimation was explored based on artificial neural network. On this basis, an optimization algorithm was presented to simulate water diversion. Furthermore, a model for remote sensing of soil moisture dynamics was applied to artificial neural network. It has been proven that the research can optimize the application of the proposed model, laying a solid foundation for future study.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.