Water Quality Monitoring Method Based on TLD 3D Fish Tracking and XGBoost

Shuhong Cheng, Shijun Zhang, Leihua Li and Dianfan Zhang

Mathematical Problems in Engineering, 2018, vol. 2018, 1-12

Abstract:

Aiming at the problem of water quality monitoring, this paper presents a method of biological water quality monitoring based on TLD (Tracking-Learning-Detection) framework and XGBoost (eXtreme Gradient Boosting). Firstly, under the framework of TLD, an independent tracking system is designed; TLD captures 3D coordinate information of fish based on video and calculates the behavior of fish movement parameters which can reflect the change of water quality via processing the coordinate information of the fish body. The data of coordinate information will be more prominent via the data processing. The integration of all built XGBoost water quality monitoring model which is based on characteristic parameters; the model was used to analyze and evaluate fish behavior parameters under unknown water quality to achieve the purpose of water quality monitoring.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2018/5604740.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2018/5604740.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5604740

DOI: 10.1155/2018/5604740

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().