Risk probability predictions for coal enterprise infrastructure projects in countries along the Belt and Road Initiative

Abstract The Belt and Road Initiative has significantly promoted the construction and development of coal enterprises along the Belt and Road through the building of a new platform that encourages global economic cooperation. This study examines problems caused by the uncertainty of risk occurrence during the process of coal enterprise construction as part of the Belt and Road Initiative. Consequently, the study identifies and classifies various risk factors. In total, 11 types of risks and 39 risk factors involved in the construction process are summarized. These include natural, cultural, religious, marketing, and outsourcing-related risks. A prediction indicator system was also established to apply to the risk occurrence probability related to coal mine construction along the Belt and Road. In this regard, the study embedded an improved artificial fish swarm algorithm (IAFSA) into a cerebellar model articulation controller (CMAC) neural network model and collected risk probability details for the construction of the Barapukuria Coal Mine in Bangladesh from 2013 to 2018 as sample data. By researching the model and sample, this study obtained various risk occurrence probability intervals. Moreover, diversified risk probabilities were verified and predicted. Finally, this study empirically proves that an IAFSA–CMAC parallel coupling algorithm is able to achieve precise predictions about risks. This finding has great significance for risk management and control in the coal enterprises of countries along the Belt and Road.

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