Determination of fish condition factor using artificial neural networks and machine learning algorithms

Tamer AKKAN, Cengiz MUTLU, Hakan IŞIK, Okan YAZICIOĞLU, Ramazan YAZICI, Mahmut YILMAZ, Nazmi POLAT

Abstract


Determination of the condition factor in fish is an indispensable element in protecting fish health and improving the status of the population. In this study, the condition factor (CF) of fish was predicted using three input parameters including length, weight and sex. In this paper, the results obtained with six machine learning algorithms; Support vector machine (SVM), Neural Network/Multilayer Perceptron (MLP), Ensemble Learning, Gaussian Process Regression (GSR), Decision Tree and Linear Regression were compared with a multilayer perceptron artificial neural network (MLP-ANN), which is one of the statistical tools to predict the condition factor value obtained in this paper. As a result of the benchmarking, the Levenberg-Marquardt learning algorithm with 3-9-1 architecture neurons was found to be the best network for the hidden layer. The output of this model was the most effective for condition factor modeling with R2 values (R2= training (1), testing (0.99), validation (1) and overall (0.99)). This value is indicative of the high quality of this model compared to other existing models. Up to now, multilayer perceptron artificial neural network (MLP-ANN) has achieved significant success in predicting the condition factor.


Keywords


MLP-YSA; boy- ağırlık-cinsiyet ilişkileri; makine öğrenimi; tahmin modeli; kondisyon faktörü

Full Text:

PDF

References


Bervoets, L., & Blust, R. (2003). Metal concentrations in water, sediment and gudgeon (Gobio gobio) from a pollution gradient: relationship with fish condition factor. Environmental pollution, 126(1), 9-19.

Brey, T. (2012). A multi‐parameter artificial neural network model to estimate macrobenthic invertebrate productivity and production. Limnology and Oceanography: Methods, 10(8), 581-589.

Brosse, S., Lek, S., & Townsend, C. R. (2001). Abundance, diversity, and structure of freshwater invertebrates and fish communities: an artificial neural network approach. New Zealand Journal of Marine and Freshwater Research, 35(1), 135-145.

Chauhan, S. S., & Trivedi, M. K. (2023). Artificial neural network-based assessment of water quality index (WQI) of surface water in Gwalior-Chambal region. International Journal of Energy and Environmental Engineering, 14(1), 47-61.

Çetinkaya, A., & Baykan, Ö. K. (2020). Prediction of middle school students' programming talent using artificial neural networks. Engineering Science and Technology, an International Journal, 23(6), 1301-1307.

Dan-Kishiya, A. S. (2013). Length-weight relationship and condition factor of five fish species from a tropical water supply reservoir in Abuja, Nigeria. American Journal of Research Communication, 1(9), 175-187.

Dinh, Q. M., Nguyen, T. H. D., Nguyen, T. T. K., Van Tran, G., & Truong, N. T. (2022). Spatiotemporal variations in length-weight relationship, growth pattern and condition factor of Periophthalmus variabilis Eggert, 1935 in Vietnamese Mekong Delta. PeerJ, 10, e12798.

Do, A. N. T., & Tran, H. D. (2023). Potential application of artificial neural networks for analyzing the occurrences of fish larvae and juveniles in an estuary in northern Vietnam. Aquatic Ecology, 57(4), 813-831.

El-Mir, A., El-Zahab, S., Sbartaï, Z. M., Homsi, F., Saliba, J., & El-Hassan, H. (2022). Machine learning prediction of concrete compressive strength using rebound hammer test. Journal of Building Engineering, 105538.

Froese, R. (2006). Cube law, condition factor and weight–length relationships: history, meta‐analysis and recommendations. Journal of applied ichthyology, 22(4), 241-253.

Ghritlahre, H. K., & Prasad, R. K. (2018). Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. Journal of environmental management, 223, 566-575.

Goethals, P. L., Dedecker, A. P., Gabriels, W., Lek, S., & De Pauw, N. (2007). Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquatic Ecology, 41, 491-508.

Granata, F., & Di Nunno, F. (2023). Neuroforecasting of daily streamflows in the UK for short-and medium-term horizons: A novel insight. Journal of Hydrology, 624, 129888.

Hema, M., Toghraie, D., & Amoozad, F. (2023). Prediction of viscosity of MWCNT-Al2O3 (20: 80)/SAE40 nano-lubricant using multi-layer artificial neural network (MLP-ANN) modeling. Engineering Applications of Artificial Intelligence, 121, 105948.

Imran, M., Dai, H. L., Zaidi, F. S., Hu, X., Tran, K. P., & Sun, J. (2024). Analyzing out-of-control signals of T2 control chart for compositional data using artificial neural networks. Expert Systems with Applications, 238, 122165.

Jones, R. E., Petrell, R. J., & Pauly, D. (1999). Using modified length–weight relationships to assess the condition of fish. Aquacultural engineering, 20(4), 261-276.

Juntunen, T., Vanhatalo, J., Peltonen, H., & Mäntyniemi, S. (2012). Bayesian spatial multispecies modelling to assess pelagic fish stocks from acoustic-and trawl-survey data. ICES Journal of Marine Science, 69(1), 95-104.

Kalaivanan, K., & Vellingiri, J. (2022). Survival Study on Different Water Quality Prediction Methods Using Machine Learning. Nature Environment & Pollution Technology, 21(3).

Katipoğlu, O. M. (2023). Evaluation of the performance of data-driven approaches for filling monthly precipitation gaps in a semi-arid climate conditions. Acta Geophysica, 71(5), 2265-2285.

Kelleher, J.D. (2019). Deep Learning. MIT Press, Cambridge, MA

Kida, M., Pochwat, K., & Ziembowicz, S. (2024). Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation. Journal of Hazardous Materials, 461, 132565.

Kumolu-Johnson, C. A., & Ndimele, P. E. (2010). Length-weight relationships and condition factors of twenty-one fish species in Ologe Lagoon, Lagos, Nigeria. Asian Journal of Agricultural Sciences, 2(4), 174-179.

Lalabadi, H. M., Sadeghi, M., & Mireei, S. A. (2020). Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines. Aquacultural Engineering, 90, 102076.

Latifoğlu, L. (2022). The performance analysis of robust local mean mode decomposition method for forecasting of hydrological time series. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(4), 3453-3472.

Li, L., Wang, P., Chao, K. H., Zhou, Y., & Xie, Y. (2016). Remaining useful life prediction for lithium-ion batteries based on Gaussian processes mixture. PloS one, 11(9), e0163004.

Liu, K., Hu, X., Wei, Z., Li, Y., & Jiang, Y. (2019). Modified Gaussian process regression models for cyclic capacity prediction of lithium-ion batteries. IEEE Transactions on Transportation Electrification, 5(4), 1225-1236.

Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), 101-124.

Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., ... & Reed, P. M. (2014). Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling & Software, 62, 271-299.

Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11(2), 431-441.

McKenna Jr, J. E. (2005). Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin. Transactions of the American Fisheries Society, 134(1), 28-43.

Najjar, Y.M., Basheer, I.A., Naouss, W.A. (1996). On the identification of compaction characteristics by neuronets. Computers and Geotechnics 18, 167–187.

Oni, S.K., J.Y. Olayemi & J.D. Adegboye, (1983). Comparative physiology of three ecologically distinct fresh water fishes, Alestes nurse (Ruppell), Synodontis schall (Bloch), S. schneider and Tilapia zilli (Gervais). J. Fish Biol., 22: 105-109

areek, C. M., Tewari, V. K., Machavaram, R., & Nare, B. (2021). Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ANN-PSO approach. Artificial Intelligence in Agriculture, 5, 1-12.

Park, Y. S., Grenouillet, G., Esperance, B., & Lek, S. (2006). Stream fish assemblages and basin land cover in a river network. Science of the Total Environment, 365(1-3), 140-153.

Pauly, D. (1980). A selection of simple methods fort he assesment of tropical fish stocks. FAO Fish. Circ.No:729, Rome

Ragheb, E. (2023). Length-weight relationship and well-being factors of 33 fish species caught by gillnets from the Egyptian Mediterranean waters off Alexandria. The Egyptian Journal of Aquatic Research.

Ranganathan, A., Yang, M. H., & Ho, J. (2010). Online sparse Gaussian process regression and its applications. IEEE Transactions on Image Processing, 20(2), 391-404.

Rocha, J. C., Peres, C. K., Buzzo, J. L. L., de Souza, V., Krause, E. A., Bispo, P. C., ... & Branco, C. C. (2017). Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs. Journal of Applied Phycology, 29, 2145-2153.

Saberi, M., Paighambari, S. Y., Darvishi, M., & Farkhondeh Shilsar, G. (2017). Length–weight relationships of six fish species from the Coastal Waters of Jask, Iran. Journal of Applied Ichthyology, 33(6), 1226-1228.

Sinshaw, T. A., Surbeck, C. Q., Yasarer, H., & Najjar, Y. (2019). Artificial neural network for prediction of total nitrogen and phosphorus in US lakes. Journal of Environmental Engineering, 145(6), 04019032.

Sparre, P., & Venema, S.C. (1998). Introduction to tropical fish stock assessment. FAO Fisheries Technical Paper, 306/1, Rev.2, Rome

Tao, H., Hameed, M. M., Marhoon, H. A., Zounemat-Kermani, M., Heddam, S., Kim, S., ... & Yaseen, Z. M. (2022). Groundwater level prediction using machine learning models: A comprehensive review. Neurocomputing, 489, 271-308.

Tarawneh, B., (2013). Pipe pile setup: Database and prediction model using artificial neural network. Soils and Foundations 53, 607–615.

Thessen, A. E. (2016). Adoption of machine learning techniques in ecology and earth science (No. e1720v1). PeerJ PrePrints.

Wang, J., Deng, Z., Yu, T., Yoshida, A., Xu, L., Guan, G., & Abudula, A. (2022). State of health estimation based on modified Gaussian process regression for lithium-ion batteries. Journal of Energy Storage, 51, 104512.

Wang, M., Fu, X., Zhang, D., Lou, S., Li, J., Chen, F., ... & Tan, S. K. (2023). Urban agglomeration waterlogging hazard exposure assessment based on an integrated Naive Bayes classifier and complex network analysis. Natural Hazards, 118(3), 2173-2197.

Weatherly,A.H., & Gill,H.S. (1987). The biology of fish growth, London, academic Press. 433-443.

Yasin, E. T., Ozkan, I. A., & Koklu, M. (2023). Detection of fish freshness using artificial intelligence methods. European Food Research and Technology, 1-12.

Yassir, A., Andaloussi, S. J., Ouchetto, O., Mamza, K., & Serghini, M. (2023). Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review. Fisheries Research, 266, 106790.

Zhao, S., Zhang, S., Liu, J., Wang, H., Zhu, J., Li, D., & Zhao, R. (2021). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture, 540, 736724.


Refbacks

  • There are currently no refbacks.