A Digital Twin Lake Framework for Monitoring and Management of Harmful Algal Blooms
Abstract
:1. Introduction
- (1)
- Browser-based front ends, instead of programs deployed on network servers as usual, are used to execute the video-based process of HAB monitoring. As a result, the problem of low efficiency in the traditional methods is completely solved;
- (2)
- DTLF is constructed by modelling a precise 3D model of the lake body, and the water environment of the lake can be realistically represented using monitoring data on the water quality, as well as precisely analyzed in a 3D manner;
- (3)
- Based on the constructed DTLF, present conditions of HABs and water quality in lakes can be grasped comprehensively by integrating monitoring data from satellite remote sensing, video devices and in situ stations.
2. Results
2.1. Research Area and Data Source
2.2. Satellite Remote Sensing of HABs in the Whole Lake
2.3. Video-Based Real-Time Monitoring of HABs in Nearshore Areas
2.4. Integrated Representation and Analysis of Multi-Source Monitoring Data
2.5. Evaluation Results of Efficiency, Effect and Accuracy
2.5.1. Efficiency Evaluation
2.5.2. Effect Evaluation
2.5.3. Accuracy Evaluation
3. Discussion
3.1. Advantages of DTLF
3.2. Potential Applications of DTLF
3.3. Uncertainties in the DTLF and Future Work
4. Conclusions
5. Materials and Methods
5.1. Construction of the DTLF
- (1)
- Dynamically determine the lake boundary and the lake surface. It can be inferred that the lake boundary and the lake surface vary with the changes of water level. In this work, a planar model is firstly generated according to the dynamically monitored water levels of the lake; the lake boundary is then determined by calculating the intersection line of the determined planar model and the 3D terrain data for the watershed. And the enclosed space of the determined lake boundary is considered as the lake surface;
- (2)
- Generate the 3D model of the underwater topography of the lake. A batch program is implemented and applied which can assign heights for all points in the underwater topography according to their elevations. And then the 3D model of the underwater terrain can be generated by fitting a curved surface based on all processed points;
- (3)
- Generate the final DTLF. Merge the obtained lake surface and the 3D model of the underwater terrain of the lake to generate the final DTLF. Considering the dynamic character of the water level, an algorithm is designed to refine the generated DTLF, i.e., (i) if the boundary of the 3D model of the underwater terrain is lower than the lake surface, i.e., there are empty areas between them, mend the empty areas with the 3D terrain data for the watershed, and (ii) if the boundary of the 3D model of the underwater terrain is higher than the lake surface, clip the 3D model of the underwater terrain based on the lake surface, i.e., only keep 3D points with heights less than or equal to the height of the lake surface.
5.2. HAB Monitoring throughout the Whole Lake via Satellite Remote Sensing
5.3. Video-Based Real-Time Monitoring of HABs in Nearshore Area of Lake
5.4. Integration, Visualization and Analysis of Multi-Source Monitoring Data
5.5. Performance Validation
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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CPU | RAM | Bandwidth | OS | Hard Disk Space |
---|---|---|---|---|
Intel Core i7-11370H | 32 GB | 8 Mbps | Windows Server 2019 | 2048 GB |
ID | Test Case | Consumed Time | Remarks |
---|---|---|---|
1 | Land-based video monitoring | 0.1 s | Contains the processes of real-time image capturing, HAB pixels identification and HAB monitoring results expression |
2 | Satellite remote sensing | 176 min | Contains the processes of satellite imagery download, data processing, result storing and data distribution. And the actual efficiency may be different in different network environments. |
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Qiu, Y.; Liu, H.; Liu, J.; Li, D.; Liu, C.; Liu, W.; Wang, J.; Jiao, Y. A Digital Twin Lake Framework for Monitoring and Management of Harmful Algal Blooms. Toxins 2023, 15, 665. https://doi.org/10.3390/toxins15110665
Qiu Y, Liu H, Liu J, Li D, Liu C, Liu W, Wang J, Jiao Y. A Digital Twin Lake Framework for Monitoring and Management of Harmful Algal Blooms. Toxins. 2023; 15(11):665. https://doi.org/10.3390/toxins15110665
Chicago/Turabian StyleQiu, Yinguo, Hao Liu, Jiaxin Liu, Dexin Li, Chengzhao Liu, Weixin Liu, Jindi Wang, and Yaqin Jiao. 2023. "A Digital Twin Lake Framework for Monitoring and Management of Harmful Algal Blooms" Toxins 15, no. 11: 665. https://doi.org/10.3390/toxins15110665