Abstract
ABSTRACT GDP is a measure of the size of the economy and how an economy is performing. The mining industry has become a focal point in the total economic picture of many countries; however, the factors affecting the contribution of the mining sector to the growth of GDP (GDPMS) have not been investigated in depth yet. In this paper, heuristic approaches were adopted to predict the GDPMS. Therefore, the effect of three parameters, namely, value added of GDP, the value of industrial output per capita and per capita value added on GDP MS, has been investigated. For this purpose, the data of countries that are actively participating in the mining industry was applied to a hybrid intelligent technique and an effective model was proposed. The results showed that a combination of a neuro-fuzzy inference system and a 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 all three input parameters; also, per capita value added and value added of GDP have the highest and the least effect on GDPMS, respectively.
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