Estimating Spatiotemporal Fishing Effort of Trawlers with Vessel-Monitoring System Data: A Case Study of the Sea Area of the Bohai Sea and the Yellow Sea, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Pre-Processing and Labeling
2.3. Feature Extraction
2.4. Fishing Behavior Recognition Based on the Slime Mould Algorithm-Optimized Light Gradient-Boosting Machine Algorithm
2.4.1. An Overview of the Fishing Behavior Recognition Model
2.4.2. Principle of SMA-LightGBM
- LightGBM Algorithm
- 2.
- Slime Mould Algorithm
- 3.
- The Slime Mould Algorithm-Optimized LightGBM
Algorithm 1 Pseudo-code of SMA-LightGBM |
Inputs: The population size and maximum number of iterations The upper and lower boundaries of the nine parameters of LightGBM Outputs: The best solution Initialize the positions of slime mould () while iteration do Calculate the cross-validation score of LightGBM as fitness Update , Calculate the by Equation (5) for each search portion do Update , , Update positions by Equation (6) Return and |
2.4.3. Training and Testing Phases of the Fishing Behavior Classifier
2.4.4. Performance Evaluation of the Fishing Behavior Classifier
2.5. Calculation Method of the Fishing Effort
3. Results
3.1. Experiment and Evaluation of Fishing Behavior Recognition
3.2. Estimating Spatiotemporal Fishing Effort of Trawlers
4. Discussion
4.1. Model and Fishing Effort Estimation
4.2. Model Performance for Fisheries’ Management and Policy Implementation
5. Conclusions
- (1)
- The presented method showed a remarkable generalization ability and high accuracy, sensitivity, specificity, and Matthews correlation coefficient in the test, with scores of 98.23%, 98.75%, 97.75%, and 0.9646, respectively. The MAE of the fishing effort of the trawlers was 0.3031 kW·h, and the R2 score was 0.9772.
- (2)
- The spatial distribution of fishing effort was primarily concentrated in three key areas: 121° E~124° E, 35.5° N~39° N; 119.7° E~122.7° E, 33.8° N~35.5° N; and 123° E~124° E, 33.5° N~35° N.
- (3)
- The temporal distribution exhibited periodic variations, which were closely associated with human activities such as the celebration of the Lunar New Year and the periods closed to fishing. The slope of the change in fishing effort concerning latitude distribution was approximately 2, whereas for longitude distribution, it was approximately −1.25.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Value | Parameter | Value | Parameter | Value |
---|---|---|---|---|---|
Number of iterations | 394 | Learning rate | 0.2393 | Max depth | 97 |
Minimum leaf node instance weight | 82.0298 | Regularization parameters γ | 0.5233 | Regularization parameters λ | 0.3453 |
Instance sampling rate | 0.2869 | Feature sampling rate | 0.8381 | Number of leaf nodes | 353 |
Methods | Test Result | |||
---|---|---|---|---|
ACC | MCC | SN | SP | |
ELM | 0.9417 | 0.8844 | 0.9187 | 0.9652 |
RF | 0.9821 | 0.9643 | 0.9925 | 0.9723 |
XGBoost | 0.9820 | 0.9640 | 0.9892 | 0.9752 |
LightGBM | 0.9811 | 0.9622 | 0.9860 | 0.9766 |
GA-LightGBM | 0.9809 | 0.9619 | 0.9859 | 0.9763 |
HHO-LightGBM | 0.9819 | 0.9638 | 0.9879 | 0.9763 |
SMA-LightGBM | 0.9823 | 0.9646 | 0.9875 | 0.9775 |
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Li, D.; Lu, F.; Xu, S.; Liu, H.; Xue, M.; Cui, G.; Ma, Z.; Fang, H.; Wang, Y. Estimating Spatiotemporal Fishing Effort of Trawlers with Vessel-Monitoring System Data: A Case Study of the Sea Area of the Bohai Sea and the Yellow Sea, China. J. Mar. Sci. Eng. 2024, 12, 64. https://doi.org/10.3390/jmse12010064
Li D, Lu F, Xu S, Liu H, Xue M, Cui G, Ma Z, Fang H, Wang Y. Estimating Spatiotemporal Fishing Effort of Trawlers with Vessel-Monitoring System Data: A Case Study of the Sea Area of the Bohai Sea and the Yellow Sea, China. Journal of Marine Science and Engineering. 2024; 12(1):64. https://doi.org/10.3390/jmse12010064
Chicago/Turabian StyleLi, Dan, Feng Lu, Shuo Xu, Huiyuan Liu, Muhan Xue, Guohui Cui, Zhenhua Ma, Hui Fang, and Yu Wang. 2024. "Estimating Spatiotemporal Fishing Effort of Trawlers with Vessel-Monitoring System Data: A Case Study of the Sea Area of the Bohai Sea and the Yellow Sea, China" Journal of Marine Science and Engineering 12, no. 1: 64. https://doi.org/10.3390/jmse12010064
APA StyleLi, D., Lu, F., Xu, S., Liu, H., Xue, M., Cui, G., Ma, Z., Fang, H., & Wang, Y. (2024). Estimating Spatiotemporal Fishing Effort of Trawlers with Vessel-Monitoring System Data: A Case Study of the Sea Area of the Bohai Sea and the Yellow Sea, China. Journal of Marine Science and Engineering, 12(1), 64. https://doi.org/10.3390/jmse12010064