|Appears in Collections:||Management, Work and Organisation Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
Basiri, Mohammad Houssain
|Title:||Forecasting the Number of Fatal Injuries in Underground Coal Mines|
|Citation:||Oraee K, Yazdani-Chamzini A & Basiri MH (2011) Forecasting the Number of Fatal Injuries in Underground Coal Mines. 2011 SME Annual Meeting & Exhibit and CMA 113th National Western Mining Conference "Shaping a Strong Future Through Mining", 27.2.2011 - 2.3.2011, Denver, Colorado, USA, Colorado, USA: Society for Mining, Metallurgy & Exploration.|
|Conference Name:||2011 SME Annual Meeting & Exhibit and CMA 113th National Western Mining Conference "Shaping a Strong Future Through Mining"|
|Conference Location:||Denver, Colorado, USA|
|Abstract:||Most management decisions at all levels of the organization are as directly or indirectly depends on the circumstance of future. With regard to predict the future events in the process of decision-making plays a main role, therefore, forecasting is very important for every organizations and institutions. There is a variety of methods to predict time series. In general, these techniques can be divided as following: statistical, artificial intelligence and analytical techniques. Two of the most common methods for time series prediction is autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) methods, these methods are the subset of statistical and artificial intelligence techniques respectively. In this paper, a hybrid model of ARIMA and ANN models are employed to predict the number of fatal injuries in the USA underground coal mines. This research showed the result of hybrid model is better than split model.|
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