Trophic state assessment using hybrid classification tree-artificial neural network

(1) * Ronnie Sabino Concepcion II Mail (De La Salle University, Philippines)
(2) Pocholo James Mission Loresco Mail (De La Salle University, Philippines)
(3) Rhen Anjerome Rañola Bedruz Mail (De La Salle University, Philippines)
(4) Elmer Pamisa Dadios Mail (De La Salle University, Philippines)
(5) Sandy Cruz Lauguico Mail (De La Salle University, Philippines)
(6) Edwin Sybingco Mail (De La Salle University, Philippines)
*corresponding author


The trophic state is one of the significant environmental impacts that must be monitored and controlled in any aquatic environment. This phenomenon due to nutrient imbalance in water strengthened with global warming, inhibits the natural system to progress. With eutrophication, the mass of algae in the water surface increases and results to lower dissolved oxygen in the water that is essential for fishes. Numerous limnological and physical features affect the trophic state and thus require extensive analysis to asses it. This paper proposed a model of hybrid classification tree-artificial neural network (CT-ANN) to assess the trophic state based on the selected significant features. The classification tree was used as a multidimensional reduction technique for feature selection, which eliminates eight original features. The remaining predictors having high impacts are chlorophyll-a, phosphorus and Secchi depth. The two-layer ANN with 20 artificial neurons was constructed to assess the trophic state of input features. The neural network was modeled based on the key parameters of learning time, cross-entropy, and regression coefficient. The ANN model used to assess trophic state based on 11 predictors resulted in 81.3% accuracy. The modeled hybrid classification tree-ANN based on 3 predictors resulted to 88.8% accuracy with a cross-entropy performance of 0.096495. Based on the obtained result, the modeled hybrid classification tree-ANN provides higher accuracy in assessing the trophic state of the aquaponic system.


Aquaponics; Assessment; Artificial neural network; Modelling tree; Trophic state



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[1] Andrea Sulis, P. Buscarinu, and G. M. Sechi, “Using reservoir trophic-state indexes in optimisation modelling of water-resource systems,” Environ. Model. Softw., vol. 26, no. 6, pp. 731–738, 2011, doi: 10.1016/j.envsoft.2011.01.001.

[2] B. Vinçon-Leite and C. Casenave, “Modelling eutrophication in lake ecosystems: A review,” Sci. Total Environ., vol. 651, pp. 2985–3001, Feb. 2019, doi: 10.1016/j.scitotenv.2018.09.320.

[3] J. C. Puno, E. Sybingco, E. Dadios, I. Valenzuela, and J. Cuello, “Determination of soil nutrients and pH level using image processing and artificial neural network,” in 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2017, pp. 1–6, doi: 10.1109/HNICEM.2017.8269472.

[4] J. Wang, Z. Fu, H. Qiao, and F. Liu, “Assessment of eutrophication and water quality in the estuarine area of Lake Wuli, Lake Taihu, China,” Sci. Total Environ., vol. 650, pp. 1392–1402, Feb. 2019, doi: 10.1016/j.scitotenv.2018.09.137.

[5] X. Peng, S. Kang, L. Zhang, Y. Zhang, D. Hu, and W. Yao, “The Assessment of the Eutrophication Status and Analysis on the Main Controlling Factor of Erlongshan Reservoir,” in 2009 International Conference on Environmental Science and Information Application Technology, 2009, pp. 120–123, doi: 10.1109/ESIAT.2009.351.

[6] Q. Liu, H. Pei, W. Hu, and J. Xie, “Assessment of Trophic Status for Nansi Lake Using Trophic State Index and Phytoplankton Community,” in 2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010, pp. 1–4, doi: 10.1109/ICBBE.2010.5514833.

[7] B. Wang and Q. Qi, “Modeling the lake eutrophication stochastic ecosystem and the research of its stability,” Math. Biosci., 2018, doi: 10.1016/j.mbs.2018.03.019.

[8] Y. Tao, J. Yu, G. Lei, B. Xue, F. Zhang, and S. Yao, “Indirect influence of eutrophication on air – water exchange fluxes, sinking fluxes, and occurrence of polycyclic aromatic hydrocarbons,” Water Res., vol. 122, pp. 512–525, Oct. 2017, doi: 10.1016/j.watres.2017.06.026.

[9] G. M. Wilkinson, “Eutrophication of Freshwater and Coastal Ecosystems,” 2017, pp. 145–152, doi: 10.1016/B978-0-12-409548-9.10160-5.

[10] K. Katsuki, K. Seto, A. Tsujimoto, H. Takata, and T. Sonoda, “Relationship between regional climate change and primary ecosystem characteristics in a lagoon undergoing anthropogenic eutrophication, Lake Mokoto, Japan,” Estuar. Coast. Shelf Sci., vol. 222, pp. 205–213, Jun. 2019, doi: 10.1016/j.ecss.2019.04.016.

[11] P. M. Glibert, “Eutrophication, harmful algae and biodiversity — Challenging paradigms in a world of complex nutrient changes,” Mar. Pollut. Bull., 2017, doi: 10.1016/j.marpolbul.2017.04.027.

[12] S. Payen and S. F. Ledgard, “Aquatic eutrophication indicators in LCA: Methodological challenges illustrated using a case study in New Zealand,” J. Clean. Prod., vol. 168, pp. 1463–1472, Dec. 2017, doi: 10.1016/j.jclepro.2017.09.064.

[13] T.-K. Liu, P. Chen, and H.-Y. Chen, “Comprehensive assessment of coastal eutrophication in Taiwan and its implications for management strategy,” Mar. Pollut. Bull., vol. 97, no. 1–2, pp. 440–450, Aug. 2015, doi: 10.1016/j.marpolbul.2015.05.055.

[14] R. H. Bosma et al., “The financial feasibility of producing fish and vegetables through aquaponics,” Aquac. Eng., vol. 78, pp. 146–154, Aug. 2017, doi: 10.1016/j.aquaeng.2017.07.002.

[15] C. Jaeger, P. Foucard, A. Tocqueville, S. Nahon, and J. Aubin, “Mass balanced based LCA of a common carp-lettuce aquaponics system,” Aquac. Eng., vol. 84, pp. 29–41, Feb. 2019, doi: 10.1016/j.aquaeng.2018.11.003.

[16] R. de C. de S. Schneider, M. de Moura Lima, M. Hoeltz, F. de Farias Neves, D. K. John, and A. de Azevedo, “Life cycle assessment of microalgae production in a raceway pond with alternative culture media,” Algal Res., 2018, doi: 10.1016/j.algal.2018.04.012.

[17] M. El Wali, S. R. Golroudbary, and A. Kraslawski, “Impact of recycling improvement on the life cycle of phosphorus,” Chinese J. Chem. Eng., vol. 27, no. 5, pp. 1219–1229, May 2019, doi: 10.1016/j.cjche.2018.09.004.

[18] B. Samuel-Fitwi, S. Wuertz, J. P. Schroeder, and C. Schulz, “Sustainability assessment tools to support aquaculture development,” J. Clean. Prod., vol. 32, pp. 183–192, Sep. 2012, doi: 10.1016/j.jclepro.2012.03.037.

[19] D. Sanjuan-Delmás et al., “Environmental assessment of an integrated rooftop greenhouse for food production in cities,” J. Clean. Prod., vol. 177, pp. 326–337, Mar. 2018, doi: 10.1016/j.jclepro.2017.12.147.

[20] D. Romeo, E. B. Vea, and M. Thomsen, “Environmental Impacts of Urban Hydroponics in Europe: A Case Study in Lyon,” Procedia CIRP, vol. 69, pp. 540–545, 2018, doi: 10.1016/j.procir.2017.11.048.

[21] H. Wu, L. Gao, Z. Yuan, and S. Wang, “Life cycle assessment of phosphorus use efficiency in crop production system of three crops in Chaohu Watershed, China,” J. Clean. Prod., vol. 139, pp. 1298–1307, Dec. 2016, doi: 10.1016/j.jclepro.2016.08.137.

[22] A. Cohen, S. Malone, Z. Morris, M. Weissburg, and B. Bras, “Combined Fish and Lettuce Cultivation: An Aquaponics Life Cycle Assessment,” Procedia CIRP, vol. 69, pp. 551–556, 2018, doi: 10.1016/j.procir.2017.11.029.

[23] K. Abdou, J. Aubin, M. S. Romdhane, F. Le Loc’h, and F. B. R. Lasram, “Environmental assessment of seabass (Dicentrarchus labrax) and seabream (Sparus aurata) farming from a life cycle perspective: A case study of a Tunisian aquaculture farm,” Aquaculture, vol. 471, pp. 204–212, Mar. 2017, doi: 10.1016/j.aquaculture.2017.01.019.

[24] A. A. Forchino, H. Lourguioui, D. Brigolin, and R. Pastres, “Aquaponics and sustainability: The comparison of two different aquaponic techniques using the Life Cycle Assessment (LCA),” Aquac. Eng., vol. 77, pp. 80–88, May 2017, doi: 10.1016/j.aquaeng.2017.03.002.

[25] C. Maucieri et al., “Life cycle assessment of a micro aquaponic system for educational purposes built using recovered material,” J. Clean. Prod., vol. 172, pp. 3119–3127, Jan. 2018, doi: 10.1016/j.jclepro.2017.11.097.

[26] A. A. Forchino, V. Gennotte, S. Maiolo, D. Brigolin, C. Mélard, and R. Pastres, “Eco-designing Aquaponics: A Case Study of an Experimental Production System in Belgium,” Procedia CIRP, vol. 69, pp. 546–550, 2018, doi: 10.1016/j.procir.2017.11.064.

[27] B. König, J. Janker, T. Reinhardt, M. Villarroel, and R. Junge, “Analysis of aquaponics as an emerging technological innovation system,” J. Clean. Prod., vol. 180, pp. 232–243, Apr. 2018, doi: 10.1016/j.jclepro.2018.01.037.

[28] A. Asciuto, E. Schimmenti, C. Cottone, and V. Borsellino, “A financial feasibility study of an aquaponic system in a Mediterranean urban context,” Urban For. Urban Green., vol. 38, pp. 397–402, Feb. 2019, doi: 10.1016/j.ufug.2019.02.001.

[29] R. S. Concepcion and L. C. Ilagan, “Application of Hybrid Soft Computing for Classification of Reinforced Concrete Bridge Structural Health Based on Thermal-Vibration Intelligent System Parameters,” in 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), 2019, pp. 207–212, doi: 10.1109/CSPA.2019.8696007.

[30] A. Testa, M. Cinque, A. Coronato, G. De Pietro, and J. C. Augusto, “Heuristic strategies for assessing wireless sensor network resiliency: an event-based formal approach,” J. Heuristics, vol. 21, no. 2, pp. 145–175, Apr. 2015, doi: 10.1007/s10732-014-9258-x.

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