Predicting the contribution of mining sector to the gross of domestic product index (GDP) utilizing heuristic approaches

In this paper heuristic approaches were adopted to predict the contribution of Mining Sector to the growth of Gross Domestic Product index (GDPMS). For this purpose, the effect of three parameters namely value added of GDP, the value of industrial output per capita and per capita value added on GDPMS has been investigated. For this purpose, the data of countries which are active in mining industry was applied to a hybrid intelligent techniques and an effective model was proposed. The results of study revealed that a combination of neuro-fuzzy inference system and genetic algorithm has relatively the best performance to predict GDPMS. Furthermore, multiple parametric sensitivity analysis was conducted on the output of the model and the outcomes showed that GDPMS is highly sensitive to the all three input parameters, also per capita value added and value added of GDP have respectively the highest and the least effect on GDPMS

Keywords:

GDP , Mining Sector , Heuristic Approach , Multiple Parametric Sensitivity Analysis