Machine Learning Models for Behavioural Diversity of Asian Elephants Prediction Using Satellite Collar Data
DOI:
https://doi.org/10.32890/jict2023.22.3.3Keywords:
Machine learning, XGBoost algorithm, Satellite collar data, Behavior classificationAbstract
Analysis of animal movement data using statistical applications and machine learning has developed rapidly in line with the development
and use of various tracking devices. Location and movement data at temporal and spatial scales are collected using the Global Positioning
System (GPS) to estimate the location of animals. In contrast, installing a satellite collar can ensure continuous monitoring, as the received
data will be sent directly to the electronic mailbox. Nevertheless, identifying an exact pattern of elephant activity from satellite collar data is still challenging. This study aimed to propose a machine learning model to predict the behavioural diversity of Asian elephants. The study involved four main phases, including two levels of model development, to produce initial and primary classification models. The phases were data collection and preparation, data labelling and initial classification model development, all data classification, and primary classification model development. The elephant behaviour data were collected from the satellite collars attached to five elephants, three males and two females, in forest reserves from 2018 to 2020 by the Department of Wildlife and National Parks, Malaysia. The study’s outcome was a novel classification model that can predict the behaviour of the Asian elephant movement. The findings showed that the XGBoost method could produce the predictive model to classify Asian elephants’ behaviour with 100 percent accuracy. This study revealed the capability of machine learning to identify behaviour classes and decision-making in setting initiatives to preserve this species in the future.
References
Bishop, W., Ryu, S. I., Chestek, C., Nuyujukian, P., & Yu, B. M. (2012). Semi-supervised classification for intracortical brain-computer interfaces. https://www.ml.cmu.edu/research/dap-papers/dap_bishop.pdf.
Brewster, L. R., Dale, J. J., Guttridge, T. L., Gruber, S. H., Hansell, A. C., Elliott, M., Cowx, I. G., Whitney, N. M., & Gleiss, A. C. (2018). Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data. Marine Biology, 165(4), 1–19. https://doi.org/10.1007/s00227-018-3318-y
Browning, E., Bolton, M., Owen, E., Shoji, A., Guilford, T., & Freeman, R. (2018). Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds. Methods in Ecology and Evolution, 9(3), 681–692. https://doi.org/10.1111/2041-210X.12926
Camargo, G., Bugatti, P. H., & Saito, P. T. M. (2020). Active semi-supervised learning for biological data classification. PLoS ONE, 15(8 August), 1–20. https://doi.org/10.1371/journal. pone.0237428
Cargnelutti, B., & Coulon, A., Hewison, A. M., Goulard, M., & Angibault, J. M., & Morellet, N. (2007). Testing global positioning system performance for wildlife monitoring using mobile collars and known reference points. The Journal of Wildlife Management. https://doi.org/10.2193/2006-257
Carroll, G., Slip, D., Jonsen, I., & Harcourt, R. (2014). Supervised accelerometry analysis can identify prey capture by penguins at sea. Journal of Experimental Biology, 217(24), 4295–4302. https://doi.org/10.1242/jeb.113076
Chandrakar, A. K. (2018, September). Wildlife: An introduction [PowerPoint slides, Department of Forestry, Wildlife & Environmental Sciences, Guru Ghasidas Vishwavidyalaya University]. https://doi.org/10.13140/RG.2.2.23194.08649
Chen, R., Little, R., Mihaylova, L., Delahay, R., & Cox, R. (2019). Wildlife surveillance using deep learning methods. Ecology and Evolution, 9(17), 9453–9466. https://doi.org/10.1002/ ece3.5410 Journal of ICT, 22, No. 3 (July) 2023, pp: 363-
Cooke, S. J., Hinch, S. G., Wikelski, M., Andrews, R. D., Kuchel, L. J., Wolcott, T. G., & Butler, P. J. (2004). Biotelemetry: A mechanistic approach to ecology. Trends in Ecology & Evolution, 19(6), 334–43. https://doi.org/10.1016/j.tree.2004.04.003
Dey, N., Ashour, A. S., & Nguyen, G. N. (2017). Deep learning for multimedia content analysis. Mining Multimedia Documents, 1(4), 193–203. https://doi.org/10.1201/b21638
Fontecchio, M. (2013). Spatial data. TechTarget. https://s e a r c h s q l s e r v e r. t e c h t a rg e t. c o m / d e f i n i t i o n / s p a t i a l - data?_ga=2.262667908.945866828.1606294096-1330996966.1594392127
Hamdan, A. R., Abu Bakar, A., & Ahmad Nazri, M. Z. (2018). Sains data - penerokaan pengetahuan dari data raya (1st ed.). Penerbit Universiti Kebangsaan Malaysia.
Han, W., Coutinho, E., Ruan, H., Li, H., Schuller, B., Yu, X., & Zhu, X. (2016). Semi-supervised active learning for sound classification in hybrid learning environments. PLoS ONE, 11(9), 1–19. https://doi.org/10.1371/journal.pone.0162075
Li, D., Wang, S., & Li, D. (2016). Spatial data mining: Theory and application. In Spatial Data Mining: Theory and Application (Issue July 2017). https://doi.org/10.1007/978-3-662-48538-5
Mathis, M. W., & Mathis, A. (2020). Deep learning tools for the measurement of animal behaviour in neuroscience. Current Opinion in Neurobiology, 60, 1–11. https://doi.org/10.1016/j. conb.2019.10.008
Mattisson, J., Andrén, H., Persson, J., & Segerström, P. (2010). Effects of species behavior on global positioning system collar fix rates. The Journal of Wildlife Management, 74(3), 557–563. http://www.jstor.org/stable/27760485
Mohamed, R., Shah Zainudin, M. N., Sulaiman, M. N., Perumal, T., & Mustapha, N. (2018). Multi-label classification for physical activity recognition from various accelerometer sensor positions. Journal of Information and Communication Technology, 17(2), 209–231. https://doi.org/10.32890/ jict2018.17.2.8252
Ochiai, T., Suehiro, Y., Nishinari, K., Kubo, T., & Takeuchi, H. (2013). A new data-mining method to search for behavioral properties that induce alignment and their involvement in social learning in medaka fish (oryzias latipes). PLoS ONE, 8(9), e71685. https://doi.org/10.1371/journal.pone.0071685 Journal of ICT, 22, No. 3 (July) 2023, pp: 363-
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., & Zhang, X. (2019). Deep learning resolves representative movement patterns in a marine predator species. Applied Sciences, 9(14), 2935. https://doi.org/10.3390/ app9142935
PERHILITAN. (2021). Gajah (Elephas maximus) di Semenanjung Malaysia. Department of Wildlife and National Parks (PERHILITAN) Peninsular Malaysia. http://wildlife.gov.my/ images/stories/penerbitan/kertas_maklumat/gajah.pd
Rast, W., Kimmig, S. E., Giese, L., & Berger, A. (2020). Machine learning goes wild: Using data from captive individuals to infer wildlife behaviours. PLoS ONE, 15(5), 1–25. https://doi.org/10.1371/journal.pone.0227317
Rew, J. (2019). Animal movement prediction based on predictive recurrent neural network. Sensors (Switzerland), 19(20), 4411. https://doi.org/10.3390/s19204411
Rui, L., & Yu, R. (2020, May). An animal behavior state estimation method using CCNN and BN based system. In CSSE ‘20: Proceedings of the 3rd International Conference on Computer Science and Software Engineering (pp. 158–163). https://doi.org/10.1145/3403746.3403921
Sarkar, D., Chapman, C. A., Griffin, L., & Sengupta, R. (2015). Analysing animal movement characteristics from location data. Transactions in GIS, 19(4), 516–534. https://doi.org/10.1111/ tgis.12114
Sharma, A., Jat, H., & Gupta, R. (2017). A survey of spatial data mining approaches: Algorithms and architecture. International Journal of Computer Technology and Electronics Communication, April, 15–22. http://earthjournals.org/ijctecpaper/IJCTEC_102. pdf
Somayeh, D., Gil, B., & Rolf, W. (2012, September). MoveBank track annotation project: Linking animal movement data with the environment to discover the impact of environmental change in animal migration. In Conference on Geographic Information Science 2012 (GIScience) (pp. 35–41).
Tanha, J., Van Someren, M., De Bakker, M., Bouteny, W., Shamoun-Baranesy, J., & Afsarmanesh, H. (2012, November). Multiclass semi-supervised learning for animal behaviour recognition from accelerometer data. In Proceedings - International Conference on Tools with Artificial Intelligence (ICTAI) (Vol. 1, pp. 690–697). https://doi.org/10.1109/ICTAI.2012.98 Journal of ICT, 22, No. 3 (July) 2023, pp: 363-
Teimouri, M., Indahl, U. G., Sickel, H., & Tveite, H. (2018). Deriving animal movement behaviours using movement parameters extracted from location data. ISPRS International Journal of Geo-Information, 7(2), 78. https://doi.org/10.3390/ijgi7020078
Tomkiewicz, S. M., Fuller, M. R., Kie, J. G., & Bates, K. K. (2010). Global positioning system and associated technologies in animal behaviour and ecological research. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1550), 2163–2176. https://doi.org/10.1098/RSTB.2010.0090
Torre, J. A., Lechner, A. M., Wong, E. P., Magintan, D., Saaban, S., & Campos-Arceiz, A. (2019). Using elephant movements to assess landscape connectivity under Peninsular Malaysia’s central forest spine land use policy. Conservation Science and Practice, 1(12), 1–14. https://doi.org/10.1111/csp2.133
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017). Applications of machine learning in animal behaviour studies. Animal Behaviour, 124, 203–220. https://doi.org/10.1016/j. anbehav.2016.12.005
Wang, G. (2019). Machine learning for inferring animal behaviour from location and movement data. Ecological Informatics, 49(December), 69–76. https://doi.org/10.1016/j. ecoinf.2018.12.002
Wijeyakulasuriya, D. A., Eisenhauer, E. W., Shaby, B. A., & Hanks, E. M. (2020). Machine learning for modelling animal movement. PLoS ONE, 15(7 July), 1–30. https://doi.org/10.1371/journal. pone.0235750
Wu, A., Buchanan, E. K., Whiteway, M., Schartner, M., Meijer, G., Noel, J.-P., Rodriguez, E., Everett, C., Norovich, A., Schaffer, E., Mishra, N., Salzman, C. D., Angelaki, D., Bendesky, A., Cunningham, J., & Paninski, L. (2020). Deep graph pose: A semi-supervised deep graphical model for improved animal pose tracking. BioRxiv, 2020.08.20.259705. http://biorxiv.org/ content/early/2020/08/22/2020.08.20.259705.abstract
Yadav, S., & Bist, A. S. (2019). Residual nets for understanding animal behaviour. Journal of Animal Behaviour and Biometeorology, 7(2), 97–103. https://doi.org/10.31893/2318-1265jabb.v7n2p97-103
Zhu, J., Rogers, T., Qian, R., & Kalish, C. (2009, October). Humans perform semi-supervised learning too. In Proceedings of the National Conference on Artificial Intelligence (Vol. 22, Issue 1, pp. 864–869.
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