Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
Abstract
:1. Introduction
2. Related Work
3. Approach
3.1. Data Preprocessing
3.2. Recurrent Neural Networks with Confidence Measure
3.3. Multi-Scale Recurrent Neural Networks
3.4. Data Set
4. Results
4.1. Representative Trajectory Segments
4.2. Effectiveness of the Proposed RNN-CM Model
4.3. Understanding Representative Trajectory Segments
4.4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Confidence Level | Top 10% | Top 20% | Top 30% | All |
---|---|---|---|---|
RNN-CM | 91.1% [100%] | 85.2% [100%] | 79.4% [99.5%] | 54.6% [59.0%] |
Random Forest | 83.5% [99.5%] | 78.4% [94.4%] | 72.1% [85.8%] | 57.0% [65.4%] |
Linear SVM | 85.6% [100.0%] | 78.8% [100.0%] | 75.2% [100.0%] | 49.8% [48.3%] |
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Share and Cite
Peng, C.; Duarte, C.M.; Costa, D.P.; Guinet, C.; Harcourt, R.G.; Hindell, M.A.; McMahon, C.R.; Muelbert, M.; Thums, M.; Wong, K.-C.; et al. Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species. Appl. Sci. 2019, 9, 2935. https://doi.org/10.3390/app9142935
Peng C, Duarte CM, Costa DP, Guinet C, Harcourt RG, Hindell MA, McMahon CR, Muelbert M, Thums M, Wong K-C, et al. Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species. Applied Sciences. 2019; 9(14):2935. https://doi.org/10.3390/app9142935
Chicago/Turabian StylePeng, Chengbin, Carlos M. Duarte, Daniel P. Costa, Christophe Guinet, Robert G. Harcourt, Mark A. Hindell, Clive R. McMahon, Monica Muelbert, Michele Thums, Ka-Chun Wong, and et al. 2019. "Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species" Applied Sciences 9, no. 14: 2935. https://doi.org/10.3390/app9142935