Water quality monitoring method based on data fusion technology

2017 ◽  
Vol 78 (1) ◽  
pp. 71-82
Author(s):  
Chunjiang Liu ◽  
Changlu Qiao
Keyword(s):  
Water Quality ◽  
Data Fusion ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Fusion Technology ◽  
Monitoring Method

scholarly journals Abnormal Water Quality Monitoring Based on Visual Sensing of Three-Dimensional Motion Behavior of Fish

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1179
Author(s):  
Shuhong Cheng ◽  
Kaopeng Zhao ◽  
Dianfan Zhang
Keyword(s):  
Water Quality ◽  
Three Dimensional ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Support Vector ◽  
Motion Trajectory ◽  
Integrated Learning ◽  
Monitoring Method ◽  
Data Set ◽  
Extreme Gradient Boosting

In the context of the problem of water pollution, the movement characteristics and patterns of fish under normal water quality and abnormal water quality are clearly different. This paper proposes a biological water quality monitoring method combining three-dimensional motion trajectory synthesis and integrated learning. The videos of the fish movement are captured by two cameras, and the Kuhn-Munkres (KM) algorithm is used to match the target points of the fish body. The Kalman filter is used to update the current state and find the optimal tracking position as the tracking result. The Kernelized Correlation Filters (KCF) algorithm compensates the targets that are lost in the tracking process and collision or occlusion in the movement process, reducing the errors caused by illumination, occlusion and water surface fluctuation effectively. This algorithm can directly obtain the target motion trajectory, avoiding the re-extraction from the centroid point in the image sequence, which greatly improves the efficiency. In order to avoid the one-sidedness of the two-dimensional trajectory, the experiment combines the pixel coordinates of different perspectives into three-dimensional trajectory pixel coordinates, so as to provide a more authentic fish swimming trajectory. We then select a representative positive and negative sample data set; the number of data sets should have symmetry. The base classifier capable of identifying different water quality is obtained by training. Finally, support vector machine(SVM), eXtreme Gradient Boosting (XGBoost) and pointnet based classifiers are combined into strong classifiers through integrated learning. The experimental results show that the integrated learning model can reflect the water quality effectively and accurately under the three-dimensional trajectory pixel coordinates of fish, and the recognition rate of water quality is above 95%.

scholarly journals MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1469-1475
Author(s):  
C. Liu ◽  
X. Zhou ◽  
Y. Zhou ◽  
A. Akbar
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Environment ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Ecological Environment ◽  
Monitoring Methods ◽  
Large Area ◽  
Monitoring Method ◽  
Multi Temporal

Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This paper proposes a multi-spectral water quality monitoring method based on UAV platform, which can quickly monitor an entire urban water area and conduct multi-temporal observation for key indices of water quality within one day. It is helpful to find and locate the polluted areas which affect the water environment quickly. Also, it can show the changes of water quality on the time axis. The result can provide a decision-making basis for water environment treatment.

scholarly journals STUDY OF WATER POLLUTION EARLY WARNING FRAMEWORK BASED ON INTERNET OF THINGS

2016 ◽  
Vol XLI-B8 ◽  
pp. 335-338
Author(s):  
H. Chengfang ◽  
X. Xiao ◽  
S. Dingtao ◽  
C. Bo ◽  
W. Xiongfei
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Internet Of Things ◽  
Early Warning ◽  
High Speed ◽  
Water Quality Monitoring ◽  
Pollution Monitoring ◽  
Quality Monitoring ◽  
Monitoring Method ◽  
Application Systems

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shuhong Cheng ◽  
Shijun Zhang ◽  
Leihua Li ◽  
Dianfan Zhang
Keyword(s):  
Water Quality ◽  
Tracking System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Gradient Boosting ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Biological Water ◽  
Extreme Gradient Boosting ◽  
Movement Parameters

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.

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