Journal of Computer Science

Artificial Neural Network Based Model for Forecasting Sugar Cane Production

O. O. Obe and D. K. Shangodoyin

DOI : 10.3844/jcssp.2010.439.445

Journal of Computer Science

Volume 6, Issue 4

Pages 439-445

Abstract

Problem statement: The global need for alternative energy source has necessitated the exploration of vast organic agricultural products with a view of processing them for the production of ethanol in commercial quantity. To ascertain a sustainable production of ethanol from processed sugar cane, a predictive model based on non-linearity nature of its production is imperative. This is due to unavailability of sufficient reliable data and the wide yield fluctuation that was not well dispersed over time. Approach: This study employed heuristic technique to develop an Artificial Neural Network (ANN) model to forecast sugar cane production in Nigeria. The input data set used includes the socio-economic and agro-climatic factors affecting sugar cane production while the output is the actual sugar cane output for the period covering 1920-2005. Various hidden layers and processing elements were tested giving rise to different Artificial Neural Network (ANN) models. The performance of the ANN models were measured using the Mean Squared Error (MSE), Normalized Mean Squared Error (NMSE), correlation coefficient (r), Akaike's Information Criterion (AIC) and Minimum Description Length (MDL). The contributions of the inputs to the outputs were determined to know how variation of the input variables affected the output. Results: The 85.70% accuracy result of the best ANN model of 2-hidden layer network of 4 Processing Elements (PEs) indicated the efficacy of Artificial Neural Network in accurate prediction. Conclusion: The developed ANN based model fits well real data and can be used for predicting purpose with a high accuracy.

Copyright

© 2010 O. O. Obe and D. K. Shangodoyin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.