# Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species

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## 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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**Figure 1.**The recurrent neural network with confidence measure (RNN-CM) process with sample inputs. Left side contains the input segments from marine animals with multiple age or gender groups, which are processed by RNN-CM. The processed segments can be divided into high and low confidence ones. The patterns of low confidence segments are shared by different animal groups, and thus, RNN-CM has low confidence in identifying which group they are extracted from. High confidence segments can be further divided according to animal age or gender groups, with relatively high confidence.

**Figure 2.**The network architecture of the recurrent neural network with confidence measure (RNN-CM). The boxes in the first row are long short-term memory (LSTM) cells, and the hidden state of the last cell is fed to a fully connected neural network. The superscripts are element indices of vectors.

**Figure 3.**Histograms of distances and angles for trajectory segments with different confidences. Representative segments are those with high confidences, while common segments are those with low confidences. t= 0,1,2,3,4,5 are time units of one segment. (

**a**) Representative segments; (

**b**) Common segments.

**Figure 4.**Locations of the representative trajectory segments (Approximate area size), https://www.google.com/maps Map data ©2018 Imagery ©2018 NASA ©2018 Google Terms of Use.

**Table 1.**The accuracy of classification based on the proposed representative segments (male segment fraction of all the high confidence segments) as determined by different algorithms on segments extracted from a trajectory using a T-hour ($T=6$) sliding window.

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%] |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Peng, 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