Time series analysis of coal mine accident experience

Bhattacherjee, Ashis; Ramani, R.V.; Natarajan, Rajesh

doi:10.1016/0022-4375(94)90047-7

Cited by 10 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time series analysis is a statistical technique used either to predict unusual trends of the process or for the purpose of process forecasting. The AutoRegressive Moving Average (ARMA) model, developed by Box and Jenkins (1976), has gained enormous popularity in many areas and research practice for the prediction and forecasting of many phenomena (Seppala et al, 2003;Bhattacherjee et al, 1994;Freivalds and Johnson, 1990). ARIMA's prediction power and flexibility was indicated by many studies (Gutiérrez-Estrada et al, 2004;Zhang, G.P., Qi, 2005).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Influent-Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques

Al‐Asheh

Mjalli

Alfadala

2007

Chemical Product and Process Modeling

View full text Add to dashboard Cite

We consider the problem of predicting the future behavior of wastewater treatment plant quality indicators by creating prediction models using historical plant data. One of the main aims of this work is to be able to predict plant operational situations in advance so that corrective actions can be taken in time. Sets of historical plant data, such as BOD, COD and TSS were collected for a local wastewater treatment plant in Doha, the capital of the State of Qatar. These variables characterize the performance of any wastewater treatment plant and can be considered as quality indicators of the plant performance. Data were collected over a period of 4 years for the influent and effluent streams of the station. The plant influent and effluent predictions were performed using different techniques. These include time-series analysis, where the ARIMA (Autoregressive Integrated Moving Average) model was implemented in this case, and two Artificial Neural Networks (ANN) algorithms, namely Adaptive Linear Neuron networks (ADALINE) and Multi-layer Feedforward (ML-FF) neural networks. The predictions from the three techniques were presented and compared. The ML-FF model predictions proved to be more reliable than that of the equivalent ARIMA predictions followed by the ADALINE predictions, particularly for the finial effluent stream variables.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting Influent-Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques

Al‐Asheh

Mjalli

Alfadala

2007

Chemical Product and Process Modeling

View full text Add to dashboard Cite

show abstract

“…The high toll in terms of human suffering that is exacted by mine accidents is reflected in numerous studies of mine safety and of strategies for reducing accident rates. [1][2][3][4][5][6][7][8] These studies have encompassed a wide variety of topics, such as accident causation, accident prevention, training, the costs of accidents, safety management and loss control. The literature on accident causation focuses on classification tables [9][10][11][12] and the factors that contribute to accident and injury occurrences have been categorized.…”

mentioning

confidence: 99%