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.
Highlights
In the era of Industrial 4.0, highly automated and intelligent manufacturing technology will gradually occupy the field of human society in the field of industrial development
Many scholars such as Kim et al obtain the parameters of motion behavior of fish by computer vision technology [3–8]; only a few scholars put forward the method of water quality anomaly monitoring according to these characteristic parameters
This paper presents the water quality monitoring methods under the framework of TLD and XGBoost; based on 3D fish tracking, we use TLD method to get the fish body coordinate in different water conditions and determine the characteristic parameters based on the calculation results of fish movement parameters
Summary
In the era of Industrial 4.0, highly automated and intelligent manufacturing technology will gradually occupy the field of human society in the field of industrial development. In the monitoring of biological water quality, fish, as an important indicator organism, with its movement characteristics, physiological characteristics, and other information directly reflect the changes in the water environment and the current situation of environmental pollution. Serra-Toro et al, Lai and Chiu, and Zhangzan et al, respectively, analyze the fish swimming behavior to get the relationship between exercise behavior parameters and water quality via recursive algorithm, fuzzy reasoning method, and data evaluation, so as to achieve the purpose of monitoring the abnormal water quality [9–11] These methods can be used for monitoring, there are a lot of defects in the selection of feature parameters, the length of operation, the accuracy of monitoring, and the processing of individual differences. XGBoost is adopted to analyze and evaluate the feature parameter, establishing semantic mapping, and the movement of fish behavior characteristic parameters model, and it realized the abnormal water quality monitoring
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