Alcohol Consumption and Fatal Injuries in Australia Before and After Major Traffic Safety Initiatives: A Time Series Analysis

Jiang, Heng; Livingston, Michael; Room, Robin

doi:10.1111/acer.12609

Cited by 17 publications

(14 citation statements)

References 30 publications

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“…The expert statisticians Box and Jenkins [25], in (1970) establish a practical approach to create the ARIMA model, which given the best match to time series. This approach has an essential significance in the domain of time series analysis and forecast [26].…”

Section: Q) X(p D Q)mentioning

confidence: 99%

Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model

Awad¹,

Zaid-Alkelani²

2019

IJISA

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The prediction of future water demand will help water distribution companies and government to plan the distribution process of water, which impacts on sustainable development planning. In this paper, we use a linear and nonlinear models to predict water demand, for this purpose, we will use different types of Artificial Neural Networks (ANNs) with different learning approaches to predict the water demand, compared with a known type of statistical methods. The dataset depends on sets of collected data (extracted from municipalities databases) during a specific period of time and hence we proposing a nonlinear model for predicting the monthly water demand and finally provide the more accurate prediction model compared with other linear and nonlinear methods. The applied models capable of making an accurate prediction for water demand in the future for the Jenin city at the north of Palestine. This prediction is made with a time horizon month, depending on the extracted data, this data will be used to feed the neural network model to implement mechanisms and system that can be employed to predicts a short-term for water demands. Two applied models of artificial neural networks are used; Multilayer Perceptron NNs (MLPNNs) and Radial Basis Function NNs (RBFNNs) with different learning and optimization algorithms Levenberg Marquardt (LM) and Genetic Algorithms (GAs), and one type of linear statistical method called Autoregressive integrated moving average ARIMA are applied to the water demand data collected from Jenin city to predict the water demand in the future. The execution results appear that the MLPNNs-LM type is outperformed the RBFNN-GAs and ARIMA models in the prediction the water demand values.

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Section: Q) X(p D Q)mentioning

confidence: 99%

Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model

Awad¹,

Zaid-Alkelani²

2019

IJISA

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“…Child injury deaths associated with adult drinking ought to occur soon after alcohol is consumed, so lagged effects were not considered. Adapting similar methods used in a previous study (Jiang et al, 2015), an ARIMA model applying the Box-Jenkins approach (Box and Jenkins, 1970) was…”

Section: Arima Model With Intervention Dummiesmentioning

confidence: 99%

“…Although previous analyses have shown significant associations between PCC and adult external-cause injuries and deaths (Jiang et al, 2015;Norström, 2011;Ramstedt, 2011), no such analysis has been done among children aged 0-14. We hypothesise that populationlevel child external-cause injuries, such as traffic injuries, accidental falls, drownings, poisonings, and homicides, will demonstrate similar kinds of associations over time with PCC as have been measured in the adult population.…”

Section: Introductionmentioning

confidence: 98%

Child injury deaths linked with adult alcohol consumption: A time series analysis

Laslett

Jiang

Chikritzhs

2018

Drug and Alcohol Dependence

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“…These papers continue to be used widely in policy debates and liquor licensing hearings throughout Australia. CAPR staff have also examined the impact of historical policy changes, such as the effects of introduction of random breath‐testing and lowering the minimum drinking age on traffic fatalities , providing evidence that the introduction of random breath‐testing reduced traffic fatalities by 20% among people aged 17–39 years from 1970 to 2010 in four states, while lowering the drinking age led to a statistically significant increase in traffic fatalities in two states. CAPR researchers have also modelled the effect of changes in the pricing of alcohol across different population subgroups .…”

Section: Main Lines Of Workmentioning

confidence: 99%

Addiction research centres and the nurturing of creativity

et al. 2017

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Established in 2006, the Centre for Alcohol Policy Research (CAPR) is Australia's only research centre with a primary focus on alcohol policy. CAPR has four main areas of research: alcohol policy impacts; alcohol policy formation and regulatory processes involved in implementing alcohol policies; patterns and trends in drinking and alcohol problems in the population; and the influence of drinking norms, cultural practices and social contexts, particularly in interaction with alcohol policies. In this paper, we give examples of key publications in each area. During the past decade, the number of staff employed at CAPR has increased steadily and now hovers at approximately 10. CAPR has supported the development of independent researchers who collaborate on a number of international projects, such as the Alcohol's Harm to Others study which is now replicated in approximately 30 countries. CAPR receives core funding from the Foundation for Alcohol Research and Education, and staff have been highly successful in securing additional competitive research funding. In 2016, CAPR moved to a new institutional setting at La Trobe University and celebrated 10 years of operation.

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Alcohol Consumption and Fatal Injuries in Australia Before and After Major Traffic Safety Initiatives: A Time Series Analysis

Cited by 17 publications

References 30 publications

Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model

Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model

Child injury deaths linked with adult alcohol consumption: A time series analysis

Addiction research centres and the nurturing of creativity

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