Probability-based Non-Linear Model: Approach to Population Forecasts for Proper Development Planning in Nigeria
Keywords:
Nigeria, Multi-layer feed-forward, Back-propagation, Time Series, PopulationAbstract
Despite the series of population census that has been carried out over the years, the question of how to forecast relatively accurate population for the subsequent years for proper planning in Nigeria still remains under investigation. Non-linear model (Neural Network; NN) has been developed and predictions are carried out on past population data. The results were compared with conventional method (Time series, TS) and it is found that the NN model performs better than the Time series model.
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References
BackPropagation Neural Network model; http://ieee.uow.edu.au/~daniel/software/libneural/BPN_tutorial/BPN_English/BPN_ English/node8.html, 10/4/2012, 7:43PM.
Baker, N. M. and Tahir, I.M. (2009), Applying Multiple Linear Regression and Neural Network to Predict Bank Performance. International Business Research, Vol.2, No.4, pp. 176-183.
Bandyopadhyay G. and Chattopadhyay S. (2008). An Artificial Neural Net approach to forecast the population of India.
Chaturvedi, D. K., M. Mohan, R. K. Singh and P. K. Kalra, (2003). Improved generalized neuron model for short-term load forecasting, Soft Computing 8: 10-18.
Keilman, N., Pham, D. and Hetland, A. (2002). Why population forecasts should be probabilistic - illustrated by the case of Norway. Demographic Research 6, Article 15.
Lee, R. (1993). Modelling and forecasting the time series of US fertility: Age patterns, range and ultimate level.International Journal of Forecasting 9, 187- 202
Lee, R. and Miller, T. (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality, "in: Demography 38 (4), 537-549
Neural Network Model; http://leenissen.dk/fann/report/node4.html, 9/25/2012, 11:18AM.
Noori, R., Khakpour, A., Omidvar, B. and Farokhnia, A. (2010), Comparison of ANN and principle component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistics. Expert Systems with Applications, 37, 5856-5862.
Pulido-Calvo, I., Montessinos, P., Roldan, J. and Ruiz-Navarro, F. (2007), Linear regression and neural approaches to water demand forecasting in irrigation districts with telemetry systems. Biosystems Engineering, 97, 283-293.
Ruggles, S. (1992). Migration, marriage and mortality: correcting sources of bias in English family reconstitutions. Population Studies, 46: 507-522.
Smith, B. A., Ronald W. M., and Gerrit H. (2005). An Enhanced Artificial Neural Network for Air Temperature Prediction, Transactions on Engineering Computing and Technology 7(August): 7-12.
Thompson, P.A., Bell, W.R., Long, J.F. and Miller, R.B. (1989). Multivariate Time Series Projections of Parameterized Age-Specific Fertility Rates. Journal of the American Statistical Association, 84 (407), 689-699
Yuan, B. J. and J. Hsu, (2006). Application of population structure change on industry structure forecast. IAMOT 2006: 1-15.
Zhang, G. P. (2001). An Investigation of neural networks for linear time-series forecasting. Computer & Operation Research 28, pp. 1183-1202.
