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Modeling and forecasting of principal minerals production
Arabian Journal of Geosciences ( IF 1.827 ) Pub Date : 2021-04-30 , DOI: 10.1007/s12517-021-07135-x
Sunila Saadat , Ijaz Hussain , Muhammad Faisal

Although coal reserves are abundant in Pakistan, still share of gas and oil is about 65% in the energy mix. Pakistan, despite being a mineral-enriched country, is facing an alarming situation as its power generation is based on foreign exchange. The mineral sector of Pakistan is dominated by four principal minerals which are gas, oil, gypsum, and coal, while gypsum being a source of reclamation and poverty reduction. There is a strong need to analyze and forecast the production of these four principal minerals to cope up with emerging challenges. Data contains 114 observations from the period of July 2005 to December 2014, measured in terms of metric tonnes (MT). In parametric models, Box-Jenkins (B-J) methodology, a regression model with auto-regressive errors (ARAR), and Holt-Winter (HW) method are used to model. In nonparametric models, univariate singular spectrum analysis (SSA) and multivariate SSA (MSSA) modeling approach are applied. Data is split into train and test data in order to specify a suitable model for forecasting. Root Mean Square Error (RMSE), Mean Absolute Percentage Error, and Theil’s U statistic are utilized as the measure of accuracy. For gas and coal, HW model is a suitable model to forecast. For gypsum and oil, Auto-regressive Integrated Moving Average (Box Jenkins ARIMA) and MSSA provide more accurate predictions, respectively. The forecasts for gas and gypsum as compared to 2014 are expected to be approximately 11 % and 45 %, respectively, more in 2020. In 2020, the forecasts of oil are expected to be eight times more than in 2014. The production of coal in 2020 is expected to decrease 12 % than in 2014. There is a strong need to optimize the production of coal by providing incentives for exploration and mining. The stakeholders should make serious efforts to bring the production of coal at an optimum level such as by providing modern equipment and high incentives to promote coal mining and exploration.



中文翻译:

主要矿产生产的建模和预测

尽管巴基斯坦的煤炭储量丰富,但在能源结构中,天然气和石油的份额仍然约为65%。巴基斯坦尽管是一个矿藏丰富的国家,但其发电是基于外汇,因此面临着令人震惊的情况。巴基斯坦的矿产部门主要有四种主要矿物质,分别是天然气,石油,石膏和煤炭,而石膏是填海和减贫的来源。迫切需要分析和预测这四种主要矿物的产量,以应对新出现的挑战。数据包含2005年7月至2014年12月期间的114观测值,以公吨(MT)表示。在参数模型中,使用Box-Jenkins(BJ)方法,具有自回归误差(ARAR)的回归模型和Holt-Winter(HW)方法进行建模。在非参数模型中,应用了单变量奇异谱分析(SSA)和多变量SSA(MSSA)建模方法。数据分为训练数据和测试数据,以便为预测指定合适的模型。均方根误差(RMSE),平均绝对百分比误差和Theil的U统计量用作准确性的度量。对于天然气和煤炭,HW模型是一种合适的预测模型。对于石膏和石油,自回归综合移动平均线(Box Jenkins ARIMA)和MSSA分别提供更准确的预测。与2014年相比,天然气和石膏的预测分别约为2020年的11%和45%。到2020年,石油的预测将是2014年的八倍。预计2020年将比2014年减少12%。强烈需要通过激励勘探和开采来优化煤炭生产。利益相关者应认真努力使煤炭产量达到最佳水平,例如提供现代化的设备和大力鼓励煤炭开采和勘探的激励措施。

更新日期:2021-04-30
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