臺灣的水產養殖業正進入轉型期。近30年來，雖然臺灣的高科技企業數量大幅增加，但水產養殖相關的公司的發展卻面臨著巨大的挑戰。然而，在這樣的環境下，卻也存在著更多的機會與發展空間。傳統的水產養殖農民面臨著水質監測以及如何快速有效地提高水質的問題。智慧魚塭則從事優化魚塭的關鍵參數------溶氧量（Dissolved Oxygen or DO）的精確工作。此外，現今的水產養殖魚塭普遍存在溶氧量不足和潛在有毒含氮廢物積聚的問題。而這些問題最終導致了養殖漁業的盈利空間受限。
在本研究中，我們旨在觀察哪些水質參數對 DO 水平有顯著影響，利用魚塭數據建立 DO 的最佳化預測模型，並預測 8 個水質參數（溫度、鹽度、pH值、Oxidation Reduction Potential or ORP）、氨、亞硝酸鹽、葉綠素-a 和濁度，通過統計分析（包括 Pearson 相關分析、多元線性回歸 (Multiple Linear Regression or MLR)、二次多項式和交互作用以及廣義縮減梯度 (Generalized Reduced Gradient or GRG) 非線性求解器）以計算出 最佳化的DO 水平數值。
最後，我們發現了顯著影響室內魚塭中溶氧量的水質參數。 通過這項研究，我們提供了DO水平的預測模型和每個重要參數的建議值，以便我們可以幫助水產養殖業維持池塘中的DO水平。

The aquaculture industry in Taiwan is entering a phase where transformation is required. Over the past 30 years, Taiwan has seen a large increase in the number of high-tech businesses. However, aquaculture companies have experienced limited development. Aquaculture is facing huge challenges, but there is also a bigger opportunity. Traditional farmers face problems with monitoring water quality and the way to increase the quality of the water quickly and efficiently. The intelligent fish farm tries to deal with the precise work of optimizing critical parameters on the water pond, which is dissolved oxygen (DO). Besides, problems with insufficient DO and the accumulation of potentially toxic nitrogenous waste products are universally encountered in aquaculture ponds. They ultimately limit the production that can be profitably achieved.
In this research, we aim to observe which water parameter significantly affects DO levels, build a prediction model of the estimated optimum value of DO by using the environmental pond data, and predict the value of critical parameters to achieve the optimum value of DO levels through statistical analysis including Pearson Correlation Analysis, Multiple Linear Regression (MLR), Quadratic Polynomials and Interaction, and Generalized Reduced Gradient (GRG) Nonlinear Solver.
Finally, we found water parameters that significantly affects DO levels in the indoor fish pond. Through this study, we also provide a prediction model of DO levels and a suggestion value of each significant parameters to maintain the DO levels in the pond, so that we could help the aquaculture industry.

Aino, K., Hirota, K., Okamoto, T., Tu, Z., Matsuyama, H., Yumoto, I. (2018). Microbial Communities Associated With Indigo Fermentation That Thrive in Anaerobic Alkaline Environments. Frontiers in Microbiology, 9(), 2196–. doi:10.3389/fmicb.2018.02196
Barati, R. (2013). Application of excel solver for parameter estimation of the nonlinear Muskingum models. , 17(5), 1139–1148. doi:10.1007/s12205-013-0037-2
Berlian, M. H, Sahputra, T. E. R., Ardi, B. J. W., Dzatmika, L. W., Besari, A., Sudibyo, R. W., Sukaridhoto, S. (2016). IEEE 2016 International Electronics Symposium (IES) - Denpasar, Indonesia (2016.9.29-2016.9.30) 2016 International Electronics Symposium (IES) - Design and implementation of smart environment monitoring and analytics in real-time system framework based on internet of underwater things and big data. , (), 403–408. doi:10.1109/ELECSYM.2016.7861040
Bond, D. R. (2002). Electrode-Reducing Microorganisms That Harvest Energy from Marine Sediments. Science, 295(5554), 483–485. doi:10.1126/science.1066771
Bowmer, K. H. and Muirhead, W. A. (1987). Inhibition of algal photosynthesis to control pH and reduce ammonia volatilization from rice floodwater. Fertilizer Research, 13(2), 13–29.
Boyd, C.E. and Pillai, V.K. (1982). Water Quality Management in Aquaculture, (CMFRI Special Publication, India, 1985), Vol 22.
Boyd, C.E. and Tucker, C.S. (1998) Pond aquaculture water quality management. Kluwer Academic Publishers, Boston.
Bryan, S. (2015). Water Quality Concerns for Ponds. PennState University Extension. Retrieved from extension.psu.edu/water-quality-concerns-for-ponds on June 10th, 2022.
Buck, D.H., 1956. Effects of turbidity on fish and fishing. Trans. North Amer. Wildlife Conf., 21:249-261.
Caspers, H. (1981). J. S. Alabaster and R. Lloyd: Water Quality Criteria for Freshwater Fish. – 297 pp. London-Boston: Butterworth 1980. ISBN 0 408 10673 5. £ 18.00. , 66(3), 443–443. doi:10.1002/iroh.19810660329
Chen, J., Sung, W., and Lin, G. (2015). Automated Monitoring System for the Fish Farm Aquaculture Environment. 2015 IEEE International Conference on Systems, Man, and Cybernetics, 1161-1166.
Chen, Y.Y., Cheng, Y.J., Yang, L., Li, Y.Q., Li, D.L. (2019). Prediction model of ammonia-nitrogen in pond aquaculture water based on improved multi-variable deep belief network. Trans Chin Social Agriculture Engineering 35:195–202. https://doi.org/10.11975/j.issn.1002-6819.2019.07.024
Cleophas, T.J., Zwinderman, A.H. (2018). Bayesian Pearson Correlation Analysis. In: Modern Bayesian Statistics in Clinical Research. Springer, Cham. https://doi.org/10.1007/978-3-319-92747-3_11
Dai, T. G. (2009). Causes and treatment measures of exceedings of pH value in Yu Dong Reservoir. Water Resources Research, 30(3), 37–38.
Davis, J. C. (1975). Minimal Dissolved Oxygen Requirements of Aquatic Life with Emphasis on Canadian Species: a Review. Journal of the Fisheries Research Board of Canada, 32(12), 2295–2332. doi:10.1139/f75-268
Deacutis C.F. (2016). Dissolved Oxygen. In: Kennish M.J. (eds) Encyclopedia of Estuaries. Encyclopedia of Earth Sciences Series. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8801-4_72
DeCoster, J. (2004). Data Analysis in SPSS. Retrieved from http://www.stat-help.com/notes.html on June 17th, 2022.
Dzulqornain, M.I., Harun Al Rasyid, M. Udin; Sukaridhoto, Sritrusta; Setyobudi, R.H.; Alasaarela, E.; Pasila, F.; Chan, G.; Lee, S.-G. (2018). Design and Development of Smart Aquaculture System Based on IFTTT Model and Cloud Integration. MATEC Web of Conferences, 164(), 01030–. doi:10.1051/matecconf/201816401030
El-Otify, A, M. (2015). Evaluation of the physicochemical and chlorophyll-a conditions of a subtropical aquaculture in Lake Nasser area, Egypt. Beni-Suef University Journal of Basic and Applied Sciences, 4(4), 327–337. doi:10.1016/j.bjbas.2015.11.009
Elias, E., Sarah, H., Tahmina, A. (2021). Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm Water. doi:10.3390/w13131782
Frank, F., and Smith, M. D. (2018). Induced innovation in fisheries and aquaculture. Food Policy, 76, 1–7.
Fukushima, T., Matsushige, K., Takamura, N., and Fukushima, M. (2004). Metabolic quotient measured by free-water method in six enclosures with different silver carp densities. Hydrobiogia, 511(3), 201–213.
Gao, D., Liu, M., Hou, L., Derrick, Y.F. L., Wang, W., Li, X., Zeng, A., Zheng, Y., Han, P., Yang, Y., Yin, G. (2019). Effects of shrimp-aquaculture reclamation on sediment nitrate dissimilatory reduction processes in a coastal wetland of southeastern China. Environmental Pollution, 255(), 113219–. doi:10.1016/j.envpol.2019.113219
Haq, K. P. R. A., and Harigovindan, V. P. (2022). Water Quality Prediction for Smart Aquaculture Using Hybrid Deep Learning Models. IEEE Access, vol. 10, pp. 60078-60098, 2022, doi: 10.1109/ACCESS.2022.3180482.
He, W., and Xu, L. (2014). Integration of distributed enterprise applications: a survey. IEEE Transactions on Industrial Informatics, 10(1), 35–42.
Howland, R. J. M., Tappin, A. D., Uncles, R. J., Plummer, D.H., and Bloomer, N. J. (2000). Distributions and seasonal variability of pH and alkalinity in the Tweed Estuary, UK. The Science of the Total Environment, 251/252(1), 125–138.
Chen Houng-Yung; Tsai Jia-Chang. (1994). Optimal dietary protein level for the growth of juvenile grouper, Epinephelus malabaricus, fed semipurified diets. , 119(2-3), 0–271. doi:10.1016/0044-8486(94)90181-3
Conrad, R. (2002). ORP Meters Applied to Fish Ponds. Retrieved from www.koivillage.com on June 2nd, 2022. http://users.stat.umn.edu/~helwig/notes/polyint-Notes.pdf on June 12th, 2022.
Dai, T. G. (2009). Causes and treatment measures of exceedings of pH value in Yu Dong Reservoir. Water Resources Research, 30(3), 37–38.
Helwig, E. N. (2017). Regression with Polynomials and Interaction. Psychology and Statistics University of Minnesota (Twin Cities). Retrieved from
Hsu, W. C., Chao, P. Y., Wang, C. S., Hsieh, J.C., Huang, W. (2020). Application of Regression Analysis to Achieve a Smart Monitoring System for Aquaculture. Information, 11(8), 387–. doi:10.3390/info11080387
Hu ZH, Zhang YR, Zhao YC, Xie MS, Zhong JZ, Tu ZG, Liu JT. (2019). A water quality prediction method based on the deep LSTM network considering correlation in smart mariculture. Sensors 19:1420. https://doi.org/10.3390/s19061420
Huan J, Li H, Wu F, Cao WJ (2020) Design of water quality monitoring system for aquaculture ponds based on NB-IoT. Aquacult Eng 90:102088–102097. https://doi.org/10.1016/j.aquaeng.2020. 102088
Igwegbe, C. A., Onokwuli O. D., Onyechi, P. C., 2019, Optimal Route for Turbidity removal from Aquaculture Wastewater by Electrocoagulation-flocculation process. Journal of Engineering and Applied Sciences 15(1):99-108
Jim, F. (2022). Linear Regression, Retrieved from https://statisticsbyjim.com/regression/linear-regression/#more-1420 on June 20th, 2022.
Joshi, G. P., and Kim, S. W. (2013). Survey, nomenclature and comparison of reader anti-collision protocols in RFID, IETE Technical Review, Retrieved from from http://tr.ietejournals.org/text. asp?2008/25/5/285/44659on June 22nd, 2022
Kramp, T., van Kranenburg, R., Lange, S. (2013). Introduction to the Internet of Things. In: , et al. Enabling Things to Talk. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40403-0_1.
Kultz, D. (2015). Physiological mechanisms used by fish to cope with salinity stress. Journal of Experimental Biology, 218(12), 1907–1914. doi:10.1242/jeb.118695
Lasdon L. and Smith, S. (1992). Solving Large Sparse Nonlinear Programs Using GRG. ORSA Journal on Computing, 4(1), 2–15. doi:10.1287/ijoc.4.1.2
Liu SY, Tai HJ, Ding QS, Li DL, Xu LQ, Wei YG. (2013). A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model 58:458–465. https://doi.org/10.1016/j.mcm.2011.11.021
Mahalik, N. G. P. C. and Kim, K. (2014) Retrofitting High-Tech Systems in Land-based Aquaculture to Improve Production Efficiency: An Automated Expert System Architecture, IETE Technical Review, 31:2, 153-161, DOI: 10.1080/02564602.2014.892752
Martin S Tango; Graham A Gagnon (2003). Impact of ozonation on water quality in marine recirculation systems. , 29(3-4), 0–137. doi:10.1016/s0144-8609(03)00061-x
Porrello, S., Lenzi, M., Ferrari, G., and Persia, E. (2005). Loading of nutrient from a land-based fish farm (Orbetello, Italy) at different time. Aquaculture International, 13(2), 97–108.
Prabha, R.; Saranish, R.S.; Sowndharya, S.; Santhosh, A.C.; Varsha, R.; Sumathi, K. Prabha, R; Saranish, R Sri; Sowndharya, S; Santhosh, AC; Varsha, R; Sumathi, K (2020). [IEEE 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) - Coimbatore, India (2020.3.6-2020.3.7)] 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) - IoT Controlled Aquaponic System. , (), 376–379. doi:10.1109/ICACCS48705.2020.9074401 Conference on Advanced Computing and Communication Systems (ICACCS), Erode, India, 6–7 March 2020; pp. 376–379.
Pretz, K. (2013). The Next Evolution of the Internet. Retrieved from http://theinstitute.ieee.org/technology-focus/ technology-topic/the-next-evolution-of-the-internet on May 28th, 2022.
Purwandoko, ; Seminar, ; Sutrisno, ; Sugiyanta, (2019). Development of a Smart Traceability System for the Rice Agroindustry Supply Chain in Indonesia. Information, 10(10), 288–. doi:10.3390/info10100288
Rajalashmi, K.; Yugathian, N.; Monisha, S.; Jeevitha, N. (2020). IoT based water quality management system. Materials Today: Proceedings, (), S2214785320308567–. doi:10.1016/j.matpr.2020.02.110
Rose, K., Eldridge, S.D., and Chapin, L. (2015). The Internet of Things (IoT): An Overview. Understanding the Issues and Challenges of a More Connected World.
Saha, S., Hasan R., Kabir, S., (2018). [IEEE 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET) - Chittagong, Bangladesh (2018.10.27-2018.10.28)] 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET) - IoT Based Automated Fish Farm Aquaculture Monitoring System. , (), 201–206. doi:10.1109/ICISET.2018.8745543
Suslow, T. V. (2004). Oxidation-Reduction Potential (ORP) for Water Disinfection Monitoring, Control, and Documentation. doi:10.3733/ucanr.8149
Ta, X. and Wei, Y. (2018). Research on a dissolved oxygen prediction method for recirculating aquaculture systems based on a convolution neural network. Computers and Electronics in Agriculture, 145(), 302–310. doi:10.1016/j.compag.2017.12.037
Towers, L. (2015). Water quality: a priority for successful aquaculture. University of St. Andrews. Retrieved from https://thefishsite.com/articles/water-quality-a-priority-for-successful-aquaculture on June 23rd, 2022.
Wang, Z., Van L., Mark C.M., Saikaly, P. E. (2017). Gradual adaptation to salt and dissolved oxygen: Strategies to minimize adverse effect of salinity on aerobic granular sludge. Water Research, (), S0043135417306887–. doi:10.1016/j.watres.2017.08.026
Wu, Z., He, H., Cai, Y., Zhang, L., Chen, Y. (2014). Spatial distribution of chlorophyllaand its relationship with the environment during summer in Lake Poyang: a Yangtze-connected lake. Hydrobiologia, 732(1), 61–70. doi:10.1007/s10750-014-1844-2
Yang, X., Ramezani, R. U., Ingrid B., Mosleh, A., Lader, PÃ¥l F. (2020). Operational limits for aquaculture operations from a risk and safety perspective. Reliability Engineering and System Safety, 204(), 107208–. doi:10.1016/j.ress.2020.107208
Yuan, X., Kong, Q., Li, Q., Li, L., Daoliang. (2015). Evaluation method for application of Internet of Things for aquaculture. Transactions of the Chinese Society of Agricultural Engineering, Volume 31, Number 4.
Yu, H.H., Yang, L., Li, D.L., Chen, Y.Y. (2020) A hybrid intelligent soft computing method for ammonia nitrogen prediction in aquaculture. Information Processing in Agriculture 8:64–74. https://doi.org/10.1016/j. inpa.2020.04.002
Zang, C., Huang, S., Wu, M. (2011). Comparison of Relationships Between pH, Dissolved Oxygen and Chlorophyll-a a for Aquaculture and Non-aquaculture Waters. Water Air Soil Pollut 219, 157–174 (2011). https://doi.org/10.1007/s11270-010-0695-3
Zhang M., Yu Y., Yang Z., Shi X.L., Kong F.X. (2012). The distribution of phytoplankton along trophic gradients and its mediation by available light in the pelagic zone of large eutrophic lakes. Can J Fish Aquat Sci 2012;69:1935–46.
Zhang, W. T. (2009). Analysis on limiting factors of eutrophication in Dashahe Reservoir. Guang Dong Water Resources and Hydropower, 9, 26–28.