skip to main content
10.1145/3605423.3605426acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicctaConference Proceedingsconference-collections
research-article

Seawater Temperature Prediction that Adapts to Changes in Water Depth

Published: 20 August 2023 Publication History

Abstract

Changes in seawater temperature affect the daily decisions of aquaculture farmers. To make optimal decisions, farmers need highly accurate predictions of seawater temperature at specific depths in their farms. Nevertheless, few studies have focused on the prediction of seawater temperature at the depths required by farmers, in contrast to the prediction of sea surface temperature (SST), which has been the focus of much research. The purpose of this paper is to establish these undersea predictions as a branch of seawater temperature prediction alongside SST prediction. We focus on undersea temperature data measured by moored buoys at five points with different characteristics to handle multiple depths and provide a careful description of the extensive analysis and pre-processing involved in making predictions. We then use these data to evaluate short-term and long-term predictions for 24 hours and seven days, respectively. As a predicting model, we proposed a deep learning model with gated recurrent units (GRUs) adaptive to water depth and compared it with standard mathematical models such as LightGBM and CatBoost. One of the results is that our model is more than 10% more accurate than the mathematical model for 7-day-ahead predictions. In addition, to support the superiority of our depth-adaptive model, we tested the reduction in prediction accuracy by eliminating the consideration of multiple depths for the inputs and outputs of the model. Our extensive analysis of the impact of changes in water depth on temperature prediction results and our corresponding proposed prediction model provides the foundation for meeting the demand for seawater temperature prediction at the depth that is truly needed in the aquaculture.

References

[1]
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-Generation Hyperparameter Optimization Framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19). Association for Computing Machinery, New York, NY, USA, 2623–2631. https://doi.org/10.1145/3292500.3330701
[2]
Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (Oct. 2001), 5–32. https://doi.org/10.1023/A:1010933404324
[3]
National Data Buoy Center. [n. d.]. National Data Buoy Center. Retrieved Dec 19, 2022 from https://www.ndbc.noaa.gov/
[4]
Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 785–794. https://doi.org/10.1145/2939672.2939785
[5]
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).
[6]
Clara Deser, Michael A Alexander, Shang-Ping Xie, Adam S Phillips, 2010. Sea surface temperature variability: Patterns and mechanisms. Annu. Rev. Mar. Sci 2, 1 (2010), 115–143.
[7]
Anna Veronika Dorogush, Vasily Ershov, and Andrey Gulin. 2018. CatBoost: gradient boosting with categorical features support. (2018). https://doi.org/10.48550/arXiv.1810.11363
[8]
Eric Freeman, Scott D Woodruff, Steven J Worley, Sandra J Lubker, Elizabeth C Kent, William E Angel, David I Berry, Philip Brohan, Ryan Eastman, Lydia Gates, 2017. ICOADS Release 3.0: a major update to the historical marine climate record. International Journal of Climatology 37, 5 (2017), 2211–2232.
[9]
Chris Garrett. 1996. Processes in the surface mixed layer of the ocean. Dynamics of Atmospheres and Oceans 23, 1-4 (1996), 19–34.
[10]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation 9, 8 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
[11]
Leigh Howarth, Leah Lewis-McCrea, and G.K. Reid. 2021. Managing Aquaculture and Eelgrass Interactions in Nova Scotia. (03 2021).
[12]
Boyin Huang, Chunying Liu, Eric Freeman, Garrett Graham, Tom Smith, and Huai-Min Zhang. 2021. Assessment and intercomparison of NOAA daily optimum interpolation sea surface temperature (DOISST) version 2.1. Journal of Climate 34, 18 (2021), 7421–7441. https://doi.org/10.1175/JCLI-D-21-0001.1
[13]
Japan Meteorological Agency. 2018. Climate Change Monitoring Report 2017. (10 2018), 5–8. https://www.jma.go.jp/jma/en/NMHS/ccmr/ccmr2017_high.pdf
[14]
Maria Kanakidou, JH Seinfeld, SN Pandis, Ian Barnes, Franciscus Johannes Dentener, Maria Cristina Facchini, Rita Van Dingenen, Barbara Ervens, ANCJSE Nenes, CJ Nielsen, 2005. Organic aerosol and global climate modelling: a review. Atmospheric Chemistry and Physics 5, 4 (2005), 1053–1123. https://doi.org/10.5194/acp-5-1053-2005
[15]
Anssi Karvonen, Päivi Rintamäki, Jukka Jokela, and E Tellervo Valtonen. 2010. Increasing water temperature and disease risks in aquatic systems: climate change increases the risk of some, but not all, diseases. International journal for parasitology 40, 13 (2010), 1483–1488.
[16]
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 3149–3157.
[17]
Eun Jin Kim, Sok Kuh Kang, Sung-Tae Jang, Jae Hak Lee, Young Ho Kim, Hyoun-Woo Kang, Yeong Yeon Kwon, and Young Ho Seung. 2010. Satellite-derived SST validation based on in-situ data during summer in the East China Sea and western North Pacific. Ocean Science Journal 45, 3 (2010), 159–170.
[18]
Eryk Lewinson. 2022. Three Approaches to Encoding Time Information as Features for ML Models. Retrieved Dec 19, 2022 from https://developer.nvidia.com/blog/three-approaches-to-encoding-time-information-as-features-for-ml-models/
[19]
MM Locarnini, AV Mishonov, OK Baranova, TP Boyer, MM Zweng, HE Garcia, D Seidov, Kw Weathers, Cr Paver, I Smolyar, 2018. World ocean atlas 2018, volume 1: Temperature. (2018).
[20]
Seelye Martin and MiItsuhiro Kawase. 1998. The southern flux of sea ice in the Tatarskiy Strait, Japan Sea and the generation of the Liman Current. Journal of Marine Research 56, 1 (1998), 141–155.
[21]
T. Menzies and Ying Hu. 2003. Data Mining for Very Busy People. Computer 36, 11 (Nov. 2003), 22–29. https://doi.org/10.1109/MC.2003.1244531
[22]
Microsoft Dynamics 365. 2019. 2019 Manufacturing Trends Report. (2019).
[23]
Portal Site of Official Statistics of Japan website. 2021. System of Social and Demographic Statistics Statistical Observations of Prefectures 2021. Retrieved Dec 19, 2022 from https://www.e-stat.go.jp/en/stat-search/files?page=1&toukei=00200502&tstat=000001149949
[24]
Masahito Okuno and Takanobu Otsuka. 2020. How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13887–13888.
[25]
Masahito Okuno and Takanobu Otsuka. 2020. Proposal and Implementation of Multiple Term Seawater Temperature Prediction Algorithm for Marine Aquaculture. IPSJ Journal 61, 3 (2020), 687–694.
[26]
Takanobu Otsuka, Yuji Kitazawa, and Takayuki Ito. 2017. Seawater Temperature Prediction Method for Sustainable Marine Aquaculture. Preprints (2017).
[27]
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: Unbiased Boosting with Categorical Features. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 6639–6649.
[28]
Bo Qiu. 2001. Kuroshio and Oyashio currents. Ocean currents: a derivative of the encyclopedia of ocean sciences (2001), 61–72.
[29]
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1985. Learning internal representations by error propagation. Technical Report. California Univ San Diego La Jolla Inst for Cognitive Science.
[30]
Helene Volkoff and Ivar Rønnestad. 2020. Effects of temperature on feeding and digestive processes in fish. Temperature 7, 4 (2020), 307–320.
[31]
Masaaki Wada, Katsumori Hatanaka, and Mohamad Natsir. 2019. Development of Automated Sea-condition Monitoring System for Aquaculture in Indonesia. Sensors and Materials 31, 3 (2019), 773–784.
[32]
Masaaki Wada, Katsumori Hatanaka, and Masashi Toda. 2008. Developing a Water Temperature Observation Network based on a Ubiquitous Buoy System to Support Aquacultures.J. Commun. 3, 5 (2008), 2–11.
[33]
Japan Meteorological Agency website. [n. d.]. Historical Weather Data Search. Retrieved Dec 19, 2022 from https://www.data.jma.go.jp/obd/stats/etrn/index.php
[34]
Japan Meteorological Agency website. 2021. Knowledge of sea temperature and currents Surface mixed layer. Retrieved Dec 19, 2022 from https://www.data.jma.go.jp/gmd/kaiyou/data/db/kaikyo/knowledge/mixedlayer.html
[35]
Qin Zhang, Hui Wang, Junyu Dong, Guoqiang Zhong, and Xin Sun. 2017. Prediction of Sea Surface Temperature Using Long Short-Term Memory. IEEE geoscience and remote sensing letters 14, 10 (2017), 1745–1749. https://doi.org/10.1109/LGRS.2017.2733548
[36]
Zhen Zhang, Xinliang Pan, Tao Jiang, Baikai Sui, Chenxi Liu, and Weifu Sun. 2020. Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network. Journal of Marine Science and Engineering 8, 4 (2020), 249. https://doi.org/10.3390/jmse8040249

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
May 2023
270 pages
ISBN:9781450399579
DOI:10.1145/3605423
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. aquaculture
  2. moored buoys
  3. multiple depths
  4. seawater temperature prediction

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICCTA 2023

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 34
    Total Downloads
  • Downloads (Last 12 months)21
  • Downloads (Last 6 weeks)1
Reflects downloads up to 18 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media