Document Type: Research Note


1 Department of Mechatronics Engineering, School of Electrical Engineering and Technology, Federal University of Technology Minna Nigeria

2 Department of Mechatronics Engineering, Federal University of Technology, Minna, Nigeria

3 Department of Electrical Engineering, Federal University of Technology, Minna

4 Department of Water Resources, Aquaculture, and fisheries Technology, Federal University of Technology, Minna

5 Department of Water Resources, Aquaculture and Fisheries Technology, Federal University of Technology, Minna


Water Quality plays an important role in attaining a sustainable aquaculture system, its cumulative effect can make or mar the entire system. The amount of dissolved oxygen (DO) alongside other parameters such as temperature, pH, alkalinity and conductivity are often used to estimate the water quality index (WQI) in aquaculture. There exist different approaches for the estimation of the quality index of the water in the aquatic environment. One of such approaches is the use of the Artificial Neural Network (ANN), however, its efficacy lies in the ability to select and use optimal parameters for the network. In this work, different WQI estimation models have been developed using the ANN. These models have been developed by varying the activation function in the hidden layer of the ANN. The performance of the ANN-based estimation models was compared with that of the multilinear regression (MLR) based model. The performance comparison depicts the ANN model case 3 with a tangent activation function as the most accurate and optimal model as compared with MLR model and other ANN models based on the mean square error (MSE), root mean square error (RMSE) and regression (R) metrics. The optimal model has a goodness of fit of 0.998, thereby outweighing other developed models in its capability to estimate the WQI in the aquaculture system


Main Subjects

[1] M. Garcia, S. Sendra, G. Lloret, and J. Lloret "Monitoring and Control Sensor System for Fish Feeding in Marine Fish Farms," Special Issue on Distributed Intelligence and Data Fusion for Sensor Systems, IET Communications vol. 5, no. 12, pp. 1682-1690, 2011.
[2] O. Oyakhilomen and R. G. Zibah, "Fishery Production and Economic Growth in Nigerian Pathway for Sustainable Economic Development," Journal of Sustainable Development in Africa, vol. 15, no. 2, 2013.
[3] T. A. Folorunso , A. M. Aibinu, J. G. Kolo, S. O. E. Sadiku, and A. m. Orire, "Iterative Parameter Selection Based Artificial Neural Network for Water Quality Prediction in Tank Cultured Aquaculture System," in International Engineering Conference 2017, Minna Nigeria, 2017, vol. Vol 2, pp. 148-154.
[4] Y. J. Mallya, "The effects of dissolved oxygen on fish growth in aquaculture," Final project at the Fisheries training programmer, The United Nations University, 2007.
[5] D. Antanasijevic, V. Pocajt, A. Peric-Grujic, and M. Ristic, "Modelling of Dissolved Oxygen in the Danube River using Artificial Neural Network and Monte Carlo Simulation Uncertainty Analysis," Journal of Hydrology, vol. 519, pp. 1895-1907, 2014.
[6] Wang, C. Deng, and X. Li, "Soft sensing of dissolved oxygen in fishpond via extreme learning machine," in Intelligent Control and Automation (WCICA), 2014 11th World Congress on, 2014, pp. 3393-3395: IEEE.
[7] E. Olyaie, H. Z. Abyaneh, and A. D. Mehr, "A comparative analys among computational intellegence techniques for dissolved oxygen prediction in Delaware River," Geoscience Frontiers, pp. 1-11, 2016.
[8] C. Deng, X. Wei, and L. Guo, "Application of Neural Network Based PSO Algorithm in Prediction Model for Dissolved Oxygen in FishPond," in 6th World Congress on Intelligent Control and Automation, Dalian Chain 2006.
[9] T. A. Folorunso , A. M. Aibinu, J. G. Kolo, S. O. E. Sadiku, and A. M. Orire, "Effects of Data Normalization on Water Quality Model in a Recirculatory Aquaculture system using Artificial Neural Network," i-manager's Journal on Pattern Recognition, vol. 5, no. 3, pp. 21-28, 2018.
[10] X. Miao, C. Deng, X. Li, Y. Gao, and D. He, "A Hybrid Neural Network and Genetic Algorithm Model for Predicting Dissolved Oxygen in an Aquaculture Pond," in 2010 International Conference on Web Information Systems and Mining (WISM), 2010, vol. 1, pp. 415-419: IEEE.
[11] L. Ghosh and G. Tiwari, "Computer modeling of dissolved oxygen performance in greenhouse fishpond: an experimental validation," international journal of agricultural research, vol. 3, no. 2, pp. 83-97, 2008.
[12] W. Wang, D. Changhui, and L. Xiangjun, "Soft Sensing of Dissolved Oxygen in FishPond Via Extreme Learning Machine," in 11th World Congress on Intelligent Control and Automation, Shenyang China, 2014, pp. 3393-3395.
[13] H. Xuemei, H. Yingzhan, and Y. Xingzhi, "The soft measure model of dissolved oxygen based on RBF network in ponds," in Fourth International Conference on Information and Computing (ICIC), 2011 2011, pp. 38-41: IEEE.
[14] R. C. Gustilo and E. Dadios, "Optimal control of prawn aquaculture water quality index using artificial neural networks," in 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), , 2011, pp. 266-271: IEEE.
[15] L. Nan, F. Zetian, W. Ruimei, and Z. Xiaoshuan, "Developing a Web-based Early Warning System for Fish Disease based on Water Quality Management," in 2006 1ST IEEE Conference on Industrial Electronics and Applications, , 2006, pp. 1-6: IEEE.
[16] F. Schtz, V. d. Lima, E. Eyng, and A. A. Bresolin, "Simulation of the concentration of dissolved oxygen in river waters using artificial neural Networks," in 11th International Conference on Natural Computation (ICNC), 2015, pp. 1252-1257.
[17] X. Xu, N. Hu, and B. Liu, "Water Quality Prediction of Changjiang of Jingdezhen through Particle Swarm Optimization Algorithm," in Management and Service Science (MASS), 2011 International Conference on, 2011, pp. 1-4.
[18] B. Chang and Z. Xinrong, "Aquaculture Monitoring System Based on Fuzzy-PID Algorithm and Intelligent Sensor Networks," in Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), 2013, pp. 385-388: IEEE.
[19] S. N. Hidayah, N. Tahir, M. Rusop, and M. S. B. S. Rizam, "Development of Fuzzy Fish Pond Water Quality Model," in 2011 IEEE Colloqium on Humanities, Science and Engineering Research (CHUSER 2011), Penang Malaysia, 2011, pp. 556-561.
[20] Wang, D. Chen, and Z. Fu, "AWQEE-DSS: A decision support system for aquaculture water quality evaluation and early-warning," in 2006 International Conference on Computational Intelligence and Security, , 2006, vol. 2, pp. 959-962: IEEE.
[21] W. T. Fu, J. F. Qiao, G. T. Han, and X. Meng, "Dissolved oxygen control system based on the TS fuzzy neural network," in 2015 International Joint Conference on Neural Networks (IJCNN), 2015, pp. 1-7: IEEE.
[22] A. H. Sweidan, N. El-Bendary, A. E. Hassanien, and O. M. H. A. El-karim, "Water Quality Classification Approach based on Bio-inspired Gray Wolf Optimization," in 7th IEEE International Conference of Soft Computing and Pattern Recognition, 2013.
[23] S. Malek, A. Salleh, and s. S. Ahmed, "A comparison Between Neural Network Based and Fuzzy Logic Models for Chlorophylll-a Estimation," in Second International Conference on Computer Engineering and Applications, 2010, vol. 217, pp. 340-343.
[24] H. Luo, D. Liu, and Y. Huang, "Artificial Neural Network Modelling of Algal Bloom in Xiangxi Bay of Three Gorges Reservior," in International Conference of Intelligent Control and Information Processing, Dalian China, 2010, pp. 645-647.
[25] B. H. Schmid and J. Koskiaho, "Artificial Neural Network Modelin of Dissolved Oxygen in a Wetland Pond: The Case of Hovi,Finland," Journal of Hydrologic Engineering vol. 11, no. 2, pp. 188-192, 2006.
[26] M. H. Al Shamisi, A. H. Assi, and H. A. Hejase, "Using MATLAB to develop artificial neural network models for predicting global solar radiation in Al Ain City–UAE," in Engineering education and research using MATLAB: IntechOpen, 2011.