Open AccessIntelligent monitoring method of water quality based on image processing and RVFL‐GMDH model
Author(s) -
Chen Junde,
Zhang Defu,
Yang Shuangyuan,
Nanehkaran Yaser Ahangari
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0254
Subject(s) - water quality , computer science , creatures , artificial intelligence , support vector machine , artificial neural network , aquaculture , machine vision , image processing , machine learning , environmental science , image (mathematics) , fish <actinopterygii> , ecology , archaeology , fishery , biology , natural (archaeology) , history
The water quality, contaminant migration characteristics, and emissions quantity of pollutants in the basin would have a great impact on aquatic creatures, agricultural irrigation, human life, and so on. In the aquaculture industry, because water colour can reflect the species and number of phytoplankton in the water, the water quality type can be obtained by analysing the colour of the aquaculture water using image processing techniques. Therefore, this study proposes an intelligent monitoring approach for water quality. The critical features of water colour images are extracted, and then using the machine learning methods, an intelligent system for water quality monitoring is established based on the fused random vector functional link network (RVFL) and group method of data handling (GMDH) model. The proposed approach presents a superior performance relative to other state‐of‐the‐art methods, and it achieves an average predicting accuracy of 96.19% on the feature dataset. Experimental findings demonstrate the validity of the proposed approach, and it is accomplished efficiently for the monitoring of water quality.