|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 In: SME Annual Meeting & Exhibit and CMA 113th National Western Mining Conference 2011, Colorado, USA: Society for Mining, Metallurgy & Exploration. 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, pp. 297-301.|
|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.|
|Rights:||The publisher has not yet responded to our queries therefore this work cannot be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.|
|11-049.pdf||1.81 MB||Adobe PDF||Under Permanent Embargo Request a copy|
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.