Artificial Neural Networks (ANN) to Model Microplastic Contents in Commercial Fish Species at Jakarta Bay

Authors

  • Andriwibowo Andriwibowo Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
  • Adi Basukriadi Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
  • Erwin Nurdin Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia
  • Vita Meylani Biology Education Department, Faculty of Teaching and Education Science, Universitas Siliwangi
  • Nenti Rofiah Hasanah Biology Education Department, Faculty of Teaching and Education Science, Universitas Siliwangi
  • Zulfi Sam Shiddiq Biology Education Department, Faculty of Teaching and Education Science, Universitas Siliwangi
  • Sitiawati Mulyanah Biology Education Department, Faculty of Teaching and Education Science, Universitas Siliwangi

DOI:

https://doi.org/10.5614/3bio.2024.6.1.3

Keywords:

Anchovy, Fiber, Mackerel, Pellet, RMSE

Abstract

Jakarta Bay is known as one of the marine ecosystems that have been contaminated by microplastics. Despite massive loads of microplasticcontamination, Jakarta Bay is also habitat to potential commercial fish species, including anchovy Stolephorus commersonnii and mackerel Rastrelliger kanagurta. While information on the microplastic contents and their determining factors is still limited, the goal of this study was touse artificial neural networks (ANN) as a novel and useful tool to model the determinants of microplastic content in fish in Jakarta Bay, using fish weight and length as proxies. Inside the stomachs of S. commersonnii and R. kanagurta, the order of microplastics from the highest to thelowest was fiber > film > fragment > pellet. Based on the RMSE values of 3.199 for S. commersonnii and 2.738 for R. kanagurta, the ANNmodel of fish?s weight + length ~ pellet was found to be the best fitted model to explain the correlation of fish weight and length with microplastic content in the stomach. The results indicate that ANN is suitable for solving large, complex problems in determining and projecting microplastic contents and provides better estimates that can be used to manage R. kanagurta and S. commersonnii along with microplastic contamination threats.

References

] Irnawati, R., Surilayani, D., Susanto, A., Munandar, A., Rah- mawati, A. Potential yield and fishing season of anchovy (Sto- lephorus sp.) in Banten, Indonesia. AACL Bioflux. 2018. [cited 2023 January 29]; 11: 804-809.

] Susanto, A., Irnawati, R., Mustahal, Syabana, M.A. Fishing ef- ficiency of LED lamps for fixed lift net fisheries in Banten Bay Indonesia. Turkish Journal of Fisheries and Aquatic Sciences. 2017. [cited 2023 January 29]; 17: 283-291

] Sutono, D. and Susanto A. Anchovy (Stolephorus sp.) utiliza- tion at coastal waters of Tegal. Jurnal Perikanan dan Kelautan. 2016. [cited 2023 January 29]; 6(2): 104-115.

] Zulfahmi, I., Audila, A., Sari, A. N., Nur, F. M., Nugroho, R. A., Hasri, I. Anchovies (Stolephorus sp.) by-product material as a fish-feed ingredient of Seurukan Fish (Osteochilus vittatus): Effect on growth performance and gut morphology. Journal of Aquaculture and Fish Health. 2022. [cited 2023 January 29];11(2): 255?268. https://doi.org/10.20473/jafh.v11i2.33189.

] Ali, M., Santoso, L. and Fransisca. D. The substitution of fish meal by using anchovies head waste to increase the growth of Tilapia. Maspari Journal : Marine Science Re- search. 2015. [cited 2023 January 29]; 7(1): 63-70. https://do i.org/10.36706/maspari.v7i1.2495.

] Ali, M., Efendi, E. and Noor, N.M., Products processing of anchovies (Stolephorus sp.) and its waste potential as raw material for feed in implementing zero waste concept. Jurnal Perikanan. 2018. [cited 2023 January 29]; 8(1): 47-54. https:// doi.org/10.29303/jp.v8i1.78.

] Rachmanto, D., Djumanto. and Setyobudi, E. Reproduction of Indian Mackerel Rastreliger kanagurta (Cuvier, 1816) in Mo- rodemak Coast Demak Regency. Jurnal Perikanan Universitas Gadjah Mada. 2020. [cited 2023 January 29]; 22(2). https:// doi.org/10.22146/jfs.48440.

] da Silva, M.L., Sales, A.S., Martins, S., de Oliveira Castro, R., de Arao, F.V. The influence of the intensity of use, rainfall and location in the amount of marine debris in four beaches in Niteroi, Brazil: Sossego, Camboinhas, Charitas and Flechas Mar. Pollut. Bullet. 2016. [cited 2023 January 29]; 113: 36-39

] Swanson, R.L., Lwiza, K., Willig, K., Morris, K. Superstorm Sandy marine debris wash-ups on Long Island?What happened to them? Mar. Pollut. Bull. 2016. [cited 2023 January 29]; 108: 215-231

] Desforges, J.W., Mora, G. and Peter, S.R. Ingestion of micro- plastics by zooplankton in the Northeast Pacific Ocean Arch. Environ. Contam. Toxicol. 2015. [cited 2023 January 29]; 69: 320-330.

] Jambeck, J.R., Geyer, R., Wilcox, C., Siegler, T.R., Perryman, M., Andrady, A., Law, K.L. Plastic waste inputs from land into the ocean. Science. 2015. [cited 2023 January 29]; 347: 768-771.

] Suryanarayana, I., Braibanti, A., Sambasiva Rao, R., Veluri, A., Sudarsan, D., Rao, G. Neural networks in fisheries research. Fisheries Research. 2008. [cited 2023 January 29]; 92: 115-139. https://doi.org/10.1016/j.fishres.2008.01.012.

] Kang, H., Jeon, D.J., Kim, S., Jung, K. Estimation of fish as- sessment index based on ensemble artificial neural network for aquatic ecosystem in South Korea. Ecological Indicators. 2022. [cited 2023 January 29]; 136. 108708. https://doi.org/10.1016/j. ecolind.2022.108708.

] Cordova, M.R., and Nurhati, I.S. Major sources and monthly variations in the release of land-derived marine debris from the Greater Jakarta area, Indonesia. Sci Rep, 2019. [cited 2023 January 29]; 9, 18730.

] Dwiyitno, A.F., Anissah, U., Indra Januar, H., Wibowo, S. Con- centration and characteristic of floating plastic debris in Jakarta Bay: a Preliminary Study. Squalen Bulletin of Marine and Fish- eries Postharvest and Biotechnology. 2020. [cited 2023 January 29];

] Manalu, A.A., Hariyadi, S. and Wardiatno. Y. Microplastics abundance in coastal sediments of Jakarta Bay, Indonesia. AACL Bioflux. 2017. [cited 2023 January 29];10: 1164-1173.

] Susanti, N., Mardiastuti, A. and Hariyadi, S. Microplastics in fishes as seabird preys in Jakarta Bay Area. IOP Conference Series: Earth and Environmental Science. 2022. [cited 2023 January 29]; 967. 012033. https://doi.org/10.1088/1755- 1315/967/1/012033.

] Efadeswarni, Andriantoro, Azizah, N., Saragih, G. Microplas- tics in digestive tracts of fishes from Jakarta Bay. IOP Con- ference Series: Earth and Environmental Science. 2019. [cited 2023 January 29]; 407. 012008. https://doi.org/10.1088/1755- 1315/407/1/012008.

] Kottelat, M., Whitten, A. J., Kartikasari, S.N., Wirjoatmojo, S. 1. Freshwater of Western Indonesia and Sulawesi. London: Periplus Edition. 1993. [cited 2022 December 15].

] Kementerian Kelautan dan Perikanan. Buku Saku Pengolah Data Jenis Ikan. 2017. [cited 2022 December 15].

] Mi, , Bordos, G., Gergely, S., Bi, M., Hahn, J., Palotai, Z., Beseny?, G., Szab , Salg A., Kriszt, B., Szoboszlay, S. Validation of microplastic sample preparation method for fresh- water samples. Water Research. 2021. [cited 2023 January 29]; 202. 117409. https://doi.org/10.1016/j.watres.2021.117409.

] Thiele, C., Hudson, M., Russell, A., Saluveer, M., Sidaoui-Haddad, G. Microplastics in fish and fishmeal: an emerg- ing environmental challenge?. Scientific Reports. 2021. [cited 2023 January 29]; 11. 2045. https://doi.org/10.1038/s41598-021-81499-8.

] Ssmann, J., Krause, T., Martin, D., Walz, E., Greiner, R., Rohn, S., Fischer, E., Fritsche, J.. Evaluation and optimisation of sample preparation protocols suitable for the analysis of plastic particles present in seafood. Food Control. 2021. 125. [cited 2023 January 29]; 107969. https://doi.org/10.1016/j. foodcont.2021.107969.

] Acoustic classification of freshwater fish species using artificial neural network: evaluation of the model performance. Ind.Fish. Res.J. 2013. [cited 2023 January 29]; 19 (1): 19-24.

] Mohamed ZE. Using the artificial neural networks for pre- diction and validating solar radiation. Journal of the Egyptian Mathematical Society. 2019. [cited 2023 January 29]; 27.

] Kunzmann, A., Arifin, Z., and Baum, G. Pollution of coastal areas of Jakarta Bay: water quality and biological responses. Marine Research in Indonesia. 2018. [cited 2023 January 29]; 43(1): 37?51. https://doi.org/10.14203/mri.v43i1.299

] Irianto, H., Hartati, S. and Sadiyah, L. Fisheries and environ- mental impacts in the great Jakarta Bay ecosystem. Indonesian Fisheries Research Journal. 2018. [cited 2023 January 29]; 23. 69. https://doi.org/10.15578/ifrj.23.2.2017.69-78.

] Bardey, D. Overfishing: pressure on our oceans. Research in Agriculture Livestock and Fisheries. 2020. [cited 2023 January 29]; 6: 397-404. ttps://doi.org/10.3329/ralf.v6i3.44805.

] Altieri, A.H., Bertness, M.D., Coverdale, T.C., Herrmann, N.C. Angelini, C. A trophic cascade triggers collapse of a salt- marsh ecosystem with intensive recreational fishing. Ecology. 2012. [cited 2023 January 29]; 93(6): 1402?1410. https://doi. org/10.1890/11-1314.1

] Du, Y., Sun, J., and Zhang, G. The impact of overfishing on environmental resources and the evaluation of current policies and future guideline. Proceedings of the 2021 International Conference on Public Relations and Social Sciences (ICPRSS 2021). 2021. [cited 2023 January 29]; https://doi.org/10.2991/assehr.k.211020.316

] Zakaria, H., Amin, S.M.N., Arshad, A., Rahman, Md., Al Barwani, S. Size frequency and length-weight relationship of spined anchovy, Stolephorus tri from the coastal waters of Be- sut, Terengganu, Malaysia. Journal of Fisheries and Aquatic Science. 2011. [cited 2023 January 29]; 6 (7): 857-861. https:// doi.org/10.3923/jfas.2011.857.861.

] Pebruwanti, N. and Fitrani, I. Size distribution of Anchovy caught by ?purse seine waring? in Semarang and Demak wa- ters - Central Java. The 3rd International Conference on Fisher- ies and Marine Sciences. 2021. [cited 2023 January 29]; 718012095. https://doi.org/10.1088/1755-1315/718/1/012095

] Claessens, M., De Meester, S., Van Landuyt, L., De Clerck, K., Janssen, C.R. Occurrence and distribution of microplastics in marine sediments along the Belgian coast, Mar.Pollut. Bull. 2011. [cited 2023 January 29]; 62: 2199?2204, https://doi. org/10.1016/j.marpolbul.2011.06.030.

] Wu, J., Lai, M., Zhang, Y., Li, J., Zhou, H. Jiang, R., Zhang, C.. Microplastics in the digestive tracts of commercial fish from the marine ranching in east China sea, China. Case Stud- ies in Chemical and Environmental Engineering. 2020. [cited 2023 January 29]; 2. 100066. https://doi.org/10.1016/j.cs- cee.2020.100066.

] Khoshnevisan, B., Rafiee, S. and Omid, M. Prediction of envi- ronmental indices of Iran wheat production using artificial neu- ral networks. Int. J. Energy Environ. 2013. [cited 2023 January 29]; 42: 339?348.

] Azadbakht, M., Torshizi, M.V., Noshad, F., Rokhbin, A. Ap- plication of artificial neural network method for prediction of osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices. Energy 2018. [cited 2023 January 29]; 165: 836?845

] Tatar Turan, F., Cengiz, A. and Kahyaoglu, T. Effect of hemicel- lulose as a coating material on water sorption thermodynamics of the microencapsulated fish oil and artificial neural network (ANN) modeling of isotherms. Food and Bioprocess Technolo- gy. 2014. [cited 2023 January 29]; 7. https://doi.org/10.1007/ s11947-014-1291-0.

] Benzer, S. and Benzer, R. Artificial neural networks model bio- metric features of marine fish sand smelt. Pakistan Journal of Marine Sciences. 2019. [cited 2023 January 29]; 28(2): 115-126.

] Cevher, E.Y. and Y?ld?r?m, D. Using Artificial Neural Network Application in Modeling the Mechanical Properties of Loading Position and Storage Duration of Pear Fruit. Processes 2022. [cited 2023 January 29]; 10, 2245. https://doi.org/10.3390/ pr10112245.

] Sang, L., Gey, O.?.. alp P., Ba?usta, N. Estimation of body weight of Sparus aurata with artificial neural network (MLP) and M5P (nonlinear regression)?LR algorithms. Iranian Journal of Fisheries Sciences. 2020. [cited 2023 January 29]; 19(2): 541-550.

] Lee, K., Chung, N. and Hwang, S. Application of an Artificial Neural Network (ANN) model for predicting mosquito abundances in urban areas. Ecological Informatics. 2015. [cited 2023 January 29]; 36. https://doi.org/10.1016/j.ecoinf.2015.08.011.

] Dempsey, D.P., Pepin, P., Koen-Alonso, M.K., Gentleman, W.C. Application of neural networks to model changes in fish community biomass in relation to pressure indicators and com- parison with a linear approach. Canadian Journal of Fisheries and Aquatic Sciences.2020. [cited 2023 January 29]; https:// doi.org/10.1139/cjfas-2018-0411

] George, J.C., Antony, S., Ninan, G.K., Kumar, A., Ravishankar, C.N. Artificial neural network models for predicting and opti- mizing the effect of air-frying time and temperature on physi- cal, textural, sensory, and nutritional quality parameters of fish ball/ Journal of Aquatic Food Product Technology. 2022. [cited 2023 January 29]; 31(1): 35-46. https://doi.org/10.1080/10498 850.2021.2008079

] Amin, A., Sabrah, M., El-Ganainy, A., El, A. 2015. Population structure of Indian mackerel, Rastrelliger kanagurta (Cuvier, 1816), from the Suez Bay, Gulf of Suez, Egypt International Journal of Fisheries and Aquatic Studies. 2015. [cited 2023 January 29]; 3(1): 68-74.

3Bio Journal Vol 6, No.1, 2024

Downloads

Published

2024-07-17

Issue

Section

Articles