American Journal of Modeling and Optimization
ISSN (Print): 2333-1143 ISSN (Online): 2333-1267 Website: http://www.sciepub.com/journal/ajmo Editor-in-chief: Dr Anil Kumar Gupta
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American Journal of Modeling and Optimization. 2019, 7(2), 35-41
DOI: 10.12691/ajmo-7-2-1
Open AccessArticle

An Intelligent Model Using Relationship in Weather Conditions to Predict Livestock-Fish Farming Yield and Production in Nigeria

R.E. Yoro1, and A.A. Ojugo2

1Department of Computer Science, Delta State Polytechnic Ogwashi-Uku, Delta State, Nigeria

2Department of Mathematics/Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria

Pub. Date: November 18, 2019

Cite this paper:
R.E. Yoro and A.A. Ojugo. An Intelligent Model Using Relationship in Weather Conditions to Predict Livestock-Fish Farming Yield and Production in Nigeria. American Journal of Modeling and Optimization. 2019; 7(2):35-41. doi: 10.12691/ajmo-7-2-1

Abstract

Soft-computing is an inexact computing art and science that seeks to analyze input so as to yield a predicted, optimal and complete-feasible solution to a complex task for which conventional methods yield a corresponding non-cost effective solution. It achieves tractability, robustness and low-cost to a solution with a tolerance of ambiguity, uncertainty, partial truth, noise, imprecision and approximation as applied to its input. Our study adopts profile hidden markov model to predict fish production yield in Nigeria. In comparison to other models, PHMM misclassification rate of 18.7% and improvement rate of 78.78%; while FGANN (fuzzy genetic algorithm trained neural net) has a misclassification rate of 19.3%, and had improvement rate of 69.30% respectively. Conversely, LDA and K-Nearest neighbourhood had a misclassification error rate of 36.6% and 43.4%; with an improvement rate of 45.83% and 41.79% respectively. We compared the supervised versus unsupervised model and results shows that unsupervised PHMM can more accurately predict future yields in fish production as it forecasts outliers in weather conditions such as rain, sunshine, humidity, and temperature amongst others – as it can greatly improve fish yield in the years ahead.

Keywords:
intelligent models stochastic validation predictions

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References:

[1]  Saccheri, I, and Hanski, I. (2006). Natural selection and population dynamics, Trends Ecol. Evol. 21: 341-347.
 
[2]  International Council for the Exploration of Sea (ICES) (2013) Report of the Arctic Fisheries Working Group (ICES, Copenhagen).
 
[3]  Taylor, B.W., Flecker, A.S., and Hall, R.O., (2006). Loss of a harvested fish species disrupts carbon flow in a diverse tropical river, Science, 313: pp833-836.
 
[4]  Verdiell, N.C., (2011). Protecting the larger fish: an ecological, economical and evolutionary analysis using a demographic model, Published Doctoral Thesis submitted to Technical University of Denmark, National Institute of Aquatic Resources, Section of Population Ecology and Genetics.
 
[5]  Hutchings, J.A, & Fraser, D.J. 2008. The nature of fisheries and farming-induced evolution. Molecular Ecology, 14, 294-313.
 
[6]  Iles, T.D., and Sinclair, M., (1982). Atlantic herring: stock discreteness and abundance, Science, 215: pp627-633.
 
[7]  Hutchings, J.A., and Reynolds, J.D., (2004). Marine fish population collapses: consequences for recovery and extinction risk, BioScience, 54: 297-309.
 
[8]  Heppner, H and Grenander, U., (1990). Stochastic non-linear model for coordinated bird flocks, In Krasner, S (Ed.), The ubiquity of chaos (pp.233–238). Washington: AAAS.
 
[9]  Branke, J., (2001b). Reducing the sampling variance when searching for robust solution, Proceedings of Man and Cybernetics, 14(6), pg 234-242, Springler-Verlag.
 
[10]  Ojugo, A.A., (2012). Artificial neural networks gravitational search algorithm for rainfall runoff modeling in hydrology, unpublished PhD Thesis, Computer Science Department: Ebonyi State University Abakiliki, Nigeria.
 
[11]  Ojugo, A., Eboka, A., Okonta, E., Yoro, R and Aghware, F., (2012). GA rule-based intrusion detection system, Journal of Computing and Information Systems, 3(8), pp 1182 - 1194.
 
[12]  Conover, D.O, & Munch, S.B. (2002). Sustaining Fisheries Yields Over Evolutionary Time Scales. Science, 297, 94–96.
 
[13]  Ojugo, A.A., and Yoro, R., (2013a). Computational intelligence in stochastic solution for Toroidal Queen, Progress in Intelligence Computing and Applications, 2(1), pp 46-56.
 
[14]  Little, D.C and Edwards, P., (2003). Integrated livestock-fish farming systems, A Publication of the Inland Water Resources and Aquaculture Service (Animal Production Service): Food and Agriculture Organization of the United Nations, Rome 2003.
 
[15]  International Council for the Exploration of Sea (ICES) (2009). Report of the Arctic Fisheries Working Group (ICES, Copenhagen).
 
[16]  Swain, D.P., Sinclair, A.F and Hanson, J.M. (2007). Evolutionary response to size-selective mortality in an exploited fish population. In Proc. Research. Soc. B: Biol. Sci. 274: 1015-1022.
 
[17]  Fraser, D.J., (2013). The emerging synthesis of evolution with ecology in fisheries science, Canadian J. of Fisheries and Aquatic Science, NRC Press, Vol 70, pp 1417-1428.
 
[18]  Ojugo, A.A., Eboka, A.O., Yoro, R.E., Yerokun, M.O and Efozia, F.N., (2015a). Hybrid model for early diabetes diagnosis, Mathematics and Computers in Science and Industry, 50: 176-182.
 
[19]  Ojugo, A.A., Yoro, R.E., Eboka, A.O., Yerokun, M.O., Anujeonye, C.N and Efozia, F.N.,(2015b). Predicting behavioural evolution on a grap-based model, Advances in Networks, Vol. 3, No. 2, pp 8-21.
 
[20]  Ojugo, A.A., Ben-Iwhiwhu, E., Kekeje, D.O., Yerokun, M.O and Iyawa, I.J.B., (2014). Malware propagation on time varying network, Int. J. Modern Edu. Comp. Sci., 8, pp25-33.
 
[21]  Ojugo, A.A., D. Allenotor., D.A. Oyemade., (2016). A stochastic model face detection, Adv. in Multidisciplinary Scientific Research Journal, 2(5): pp101-114.
 
[22]  Ojugo, A.A., D.A. Oyemade., D. Allenotor., (2016). Solving for computational intelligence the timetable problem, Adv. in Multidisciplinary Scientific Research Journal, 2(5): pp67-84.
 
[23]  Ojugo, A.A and Otakore, O.D., (2018). Improving early detection of gestational diabetes via intelligent classification models: a case of the Niger Delta region of Nigeria, SciEP Journal of Computer Science & Application, 6(2): pp82-90.
 
[24]  Ojugo, A.A and Eboka, A.O., (2018). Comparative evaluation for performance adaptive model for spam phishing detection, SciEP Digital Technologies, 3(1): pp9-15.
 
[25]  Beven, K. J and Binley, N.J., (1992). Knowledge driven models and computations, Wiley and Sons, Chichester, ISBN: 0-465-08245-3, pp 234 -289.
 
[26]  Branke, J., (2001a). Evolutionary Optimization in Dynamic Environments, Kluwer.
 
[27]  Gaas, S.I., (1983). Decision-aiding models: validation, assessment, and related issues in policy analysis, Operations Research, 31, pp 603-631.
 
[28]  Hall, M. J., (2001). How well does your model fit data?, J. Hydroinformatics, 3(1), pp.49-55.
 
[29]  Oreskes, N., Shrader-Frechette, K. and Belitz, K., (1984). Verification, validation, and confirmation of numerical models in the earth sciences, Earth Science 263: pp 641-646.
 
[30]  Perez, M and Marwala, T., (2011). Stochastic optimization approaches for solving Sudoku, IEEE Transaction on Evolutionary Computation, pp.256-279.
 
[31]  Ursem, R., Krink, T., Jensen, M.and Michalewicz, Z., (2002). Analysis and modeling of controls in dynamic systems. IEEE Transaction on Memetic Systems and Evolutionary Computing, 6(4), pp.378-389.
 
[32]  Andersen, K.H, & Rice, J.C. 2010. Direct and indirect community effects of rebuilding plans. ICES Journal of Marine Science, 67, 1980-1988.
 
[33]  Hilborn, R, & Minte-Vera, C.V. 2008. Fisheries-induced changes in growth rates in marine fisheries: are they significant? Bulletin of Marine Science, 83, 95-105.
 
[34]  Eikeset, A.M., Richter, A., Dunlop, E.S., Dieckmann, U and Stenseth, N.C., (2012). Economic repercussions of fisheries-induced evolution, PNAS, 110(30), pp12259-12264.
 
[35]  Law, R. (2000). Fishing, selection, and phenotypic evolution. ICES Journal of Marine Science, 57, 659–668. 3, 44.