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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.
Yoro et al. (Mon,) studied this question.