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To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is one of the leading causes of cancer-related death in women. The early detection of breast cancer with screening mammograms plays a pivotal role in reducing mortality rates. Although population-based double-reading screening mammograms have reduced mortality by over 31% in women with breast cancer in Europe, continuing this program is difficult due to the shortage of radiologists. Artificial intelligence (AI) is an emerging technology that has provided promising results in medical imaging for disease detection. This study investigates the performance of AI models on an Australian mammographic database, demonstrating how transfer learning from a USA mammographic database to an Australian one, contrast enhancement on mammographic images and the quality of training data according to radiologists' concordance can improve breast cancer diagnosis. Our proposed methodology offers a more efficacious approach for AI to contribute to radiologists' decision making when interpreting mammography images. This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models. [ABSTRACT FROM AUTHOR]

This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia. [ABSTRACT FROM AUTHOR]

Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model's forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model's accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike's information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires. [ABSTRACT FROM AUTHOR]

Beef production drives a significant proportion of Australia's agricultural economy, representing AUD11 billion in agricultural production value annually. The profitability of cattle farms across seasons varies greatly and is particularly influenced by weather and associated uncertainty through pasture growth. Furthermore, producers must be careful in managing stocking rates to avoid overgrazing which has a significant environmental and economic impact. Predictive modelling of pasture and cattle liveweight can help producers to better manage this difficult balancing act. In this paper, we integrate a model for pasture growth and cattle weight gain to formulate predictions of liveweight production volume and pasture biomass. Monte‐Carlo simulation was used to understand the impact of uncertainty in model inputs relating to weather. Through such an approach, a distribution of possible pasture outcomes and beef production is formed which can be inspected to determine, for example expected production volume and risk of overgrazing. The methodology is demonstrated and validated through application to case study data sourced from Australian beef producers. Results demonstrate that it is possible to obtain reasonable predictions of liveweight gain and highlight the importance of considering the associated uncertainty. The accuracy of the proposed models has been examined by calculating statistical parameters MSE, MAE and R2. This approach can provide insight into the distribution of possible production outcomes given the long‐term rainfall outlook. The use of such predictive models and simulations by producers will lead to better‐informed planning of stocking rates, with benefits to productivity and sustainable utilisation of pasture. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess whether existing pavement temperature prediction algorithms can be used to reliably determine Performance Grade (PG) design temperatures for Australian asphalt pavements. The results show good agreement between internationally and locally developed pavement temperature algorithms for the prediction of high pavement design temperature. There is more variability between models in the prediction of low pavement design temperature. The paper also provides a set of PG grading results for a range of Australian asphalt binders. The findings indicate that for a given design situation harder bitumen, ormore highly modified binder would be specified in Australia than in jurisdictions using the PG system. [ABSTRACT FROM AUTHOR]

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision. [ABSTRACT FROM AUTHOR]

Forecasting solar radiation for a given region is an emerging field of study. It will help to identify the places for installing large-scale photovoltaic-systems, designing energy-efficient buildings and energy estimation. The different machine learning kernel-based approaches for prediction problems uses either a local or global kernel. These models can provide either strong training capability or good generalization performance. In this paper, a new hybrid kernel is proposed using the combination of a local and global kernel. A novel algorithm Hybrid Kernel-based Extreme Learning Machine is proposed for predictive modelling of Incident Solar Radiation (ISR) time series using new hybrid kernel. The proposed algorithm uses the surface, atmospheric, cloud properties obtained from the MODIS instrument and observed ISR at time t – 1 to predict ISR at time t. This study is conducted for 41 diverse sites of Australia for the period of 2012–2015. Further, the proposed model is experimented with other time series datasets to prove its efficacy. It is shown that the proposed methodology outperforms five other benchmarking methods in terms of MAE and Willmott's Index (WI). Therefore, the suggested approach can be used for modelling solar energy at a national scale using remotely-sensed satellite footprints. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence has the potential to innovate current practices used to detect the imminent risk of suicide and to address shortcomings in traditional assessment methods. Objective: In this paper, we sought to automatically classify short segments (40 milliseconds) of speech according to low versus imminent risk of suicide in a large number (n=281) of telephone calls made to 2 telehealth counselling services in Australia. Methods: A total of 281 help line telephone call recordings sourced from On The Line, Australia (n=266, 94.7%) and 000 Emergency services, Canberra (n=15, 5.3%) were included in this study. Imminent risk of suicide was coded for when callers affirmed intent, plan, and the availability of means; level of risk was assessed by the responding counsellor and reassessed by a team of clinical researchers using the Columbia Suicide Severity Rating Scale (=5/6). Low risk of suicide was coded for in an absence of intent, plan, and means and via Columbia suicide Severity Scale Ratings (=1/2). Preprocessing involved normalization and pre-emphasis of voice signals, while voice biometrics were extracted using the statistical language r. Candidate predictors were identified using Lasso regression. Each voice biomarker was assessed as a predictor of suicide risk using a generalized additive mixed effects model with splines to account for nonlinearity. Finally, a component-wise gradient boosting model was used to classify each call recording based on precoded suicide risk ratings. Results: A total of 77 imminent-risk calls were compared with 204 low-risk calls. Moreover, 36 voice biomarkers were extracted from each speech frame. Caller sex was a significant moderating factor (β=-.84, 95% CI -0.85, -0.84; t=6.59, P<.001). Candidate biomarkers were reduced to 11 primary markers, with distinct models developed for men and women. Using leave-one-out cross-validation, ensuring that the speech frames of no single caller featured in both training and test data sets simultaneously, an area under the precision or recall curve of 0.985 was achieved (95% CI 0.97, 1.0). The gamboost classification model correctly classified 469,332/470,032 (99.85%) speech frames. Conclusions: This study demonstrates an objective, efficient, and economical assessment of imminent suicide risk in an ecologically valid setting with potential applications to real-time assessment and response. [ABSTRACT FROM AUTHOR]

Background: Previous research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05–I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data. Methods: A comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000– 2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations. Results: The proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available. Conclusion: This paper is the first to show that not only misclassification of non-rheumatic VHD but also of ARF as RHD yields substantial FPRs. Both sources of bias can be successfully addressed with the proposed model which provides an effective solution for reliable RHD case ascertainment from hospital data for epidemiological disease monitoring and policy evaluation. [ABSTRACT FROM AUTHOR]

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks. [ABSTRACT FROM AUTHOR]

The emergence of many strains of the coronavirus, including the latest omicron strain, which is spreading at a very high speed, is leading to the World Health Organization's (WHO) concern about the creation of this new mutation. Therefore, there is a strong motivation for modeling and predicting COVID-19 to control the number of cases of the disease. The proposed system for predicting the number of cases of COVID-19 can help governments take precautions to prevent the spread of the disease. In this paper, a statistical logistic growth model was employed to predict the spread of COVID-19 in Australia and Brazil. The datasets were collected from the surveillance systems in Australia and Brazil from March 13, 2020, to December 12, 2021, for 641 days. This proposed method used a tested logistic growth model for the complex spread of COVID-19 and forecasted future values within a time interval of six days. The results of the predicted, cumulative, confirmed cases indicate the robustness and effectiveness of the proposed system, which was categorized by time-dependent dynamics. The coefficient of determination (R) metric was used to evaluate the model to predict COVID-19, and the proposed system scored the highest correlation ( R 2 = 99 %). The proposed system has the potential to contribute to public health by making decisions about how to prevent the spread of COVID-19. [ABSTRACT FROM AUTHOR]

This paper sets up and estimates a structural model of Australia as a small open economy using Bayesian techniques. Unlike other recent studies, the paper shows that a small micro-founded model can capture the open economy dimensions quite well. Specifically, the model attributes a substantial fraction of the volatility of domestic output and inflation to foreign disturbances, close to what is suggested by unrestricted VAR studies. The paper also investigates the effects of various exogenous shocks on the Australian economy. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models. [ABSTRACT FROM AUTHOR]

Understanding and projecting drought, especially in the face of climate change, is crucial for assessing its impending risks. However, the causes of drought are multifaceted. As the environmental research paradigm pivots towards machine learning (ML) for predictions, our investigation contrasted multiple ML techniques to simulate the Standardized Precipitation Evapotranspiration Index (SPEI) from 2009 to 2022, utilizing various potential evapotranspiration (PET) methods. Our primary focus was Australia, the world's driest inhabited continent. Given the challenges with ML model interpretation, SHAP (SHapley Additive exPlanations) values were employed to decipher SPEI variations and to gauge the relative importance of precipitation (Prec) and PET in six key Australian cities. Our findings revealed that while different PET methods resulted in distinct mean values, their trends remained consistent. Post the Millennium Drought, Australia experienced several drought events. SPEI discrepancies based on PET methods were minimal in humid regions like Brisbane and Darwin. However, for arid cities, the Priestley–Taylor equation-driven SPEI differed notably from other methods. Ridge regression was the most adept at mirroring SPEI changes among the assessed ML models. Furthermore, the SHAP explainer discerned that PET-related climate variables had a greater impact on SPEI in drier cities, whereas in humid cities, Prec was more influential. Notably, the research emphasised CO2′s role in influencing drought dynamics in humid cities. These insights are invaluable for enhancing drought mitigation strategies and refining predictive models. Such revelations are crucial for stakeholders aiming to improve drought prediction and management, especially in drought-prone regions like Australia. [ABSTRACT FROM AUTHOR]

Background: Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon's estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement. Methods: We used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon's estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration. Results: The results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods. Conclusion: The results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach. [ABSTRACT FROM AUTHOR]

• Mutual information is effective to analyze dependence of foF2 on R 12 and F10.7 12. • Dependence of foF2 on R 12 and F10.7 12 differ in different solar activity years. • Mutual information provides an effective selection of inputs for foF2 prediction. This paper analyzes the dependence of the ionospheric F2 layer critical frequency, f oF2, on two widely used solar activity indices (12-month running mean of the sunspot number (R 12) and the solar radio flux of 10.7 cm wavelength (F10.7 12)) by using mutual information method at the ionospheric station of Darwin (geographic 12.4°S, 131.5°E) in Australia. The results show that the f oF2 have different responses to R 12 and F10.7 12 in different solar activity years. In the high and low solar activity years, the f oF2 is more dependent on R 12 rather than F10.7 12. In the moderate solar activity years, the f oF2 is more dependent on F10.7 12 rather than R 12. We further verify the effectiveness of the mutual information on the selection of the solar activity index by developing the prediction models of the f oF2. Additionally, the results are compared against the International Reference Ionosphere (IRI) model. The data of training period used in the model is from 1998 to 2015, except 2000, 2009 and 2013. Data for 2000, 2009 and 2013 are used to validate the prediction accuracy, for high, low and moderate solar activity years, respectively. The results demonstrate that the favorable prediction results can be obtained when the solar activity index with large mutual information is used as the input variable of the fo F2 prediction model. The proposed method not only can quantitatively analyze the nonlinear relationship between the fo F2 and solar activity indices but also provides an effective way of selecting the solar activity indices for the fo F2 prediction. [ABSTRACT FROM AUTHOR]

Frailty is a biological syndrome that is associated with increased risks of morbidity and mortality. To assess the value of interventions to prevent or manage frailty, all important impacts on costs and outcomes should be estimated. The aim of this study is to describe the development and validation of an individual-based state transition model that predicts the incidence and progression of frailty and frailty-related events over the remaining lifetime of older Australians. An individual-based state transition simulation model comprising integrated sub models that represent the occurrence of seven events (mortality, hip fracture, falls, admission to hospital, delirium, physical disability, and transitioning to residential care) was developed. The initial parameterisation used data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The model was then calibrated for an Australian population using data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The simulation model established internal validity with respect to predicting outcomes at 24 months for the SHARE population. Calibration was required to predict longer terms outcomes at 48 months in the SHARE and HILDA data. Using probabilistic calibration methods, over 1,000 sampled sets of input parameter met the convergence criteria across six external calibration targets. The developed model provides a tool for predicting frailty and frailty-related events in a representative community dwelling Australian population aged over 65 years and provides the basis for economic evaluation of frailty-focussed interventions. Calibration to outcomes observed over an extended time horizon would improve model validity. [ABSTRACT FROM AUTHOR]

Bayesian networks (BNs) are increasingly being used to model complex coastal processes due to their ability to integrate non-linear systems, their transparent probabilistic framework, and low computational cost. A BN may be suited to descriptive or predictive application. Descriptive BNs are highly calibrated models that are useful for better understanding the physics and causal relationships driving a system. Predictive BNs are generalisations of a system that have skill at predicting outside of the training domain. The predictive and descriptive usefulness of a BN depends on its complexity and the amount of data available to train it, but there is often a trade-off; higher descriptive skill comes at the cost of reduced predictive skill. To demonstrate the differences between predictive and descriptive BNs in a coastal engineering context, a BN to predict shoreline recession caused by coastal storm events is developed and tested using an extensive 10-year dataset incorporating 137 individual storms events monitored at Narrabeen-Collaroy Beach, Australia. A parsimonious approach to BN development is used to separately determine the optimum predictive and descriptive BNs for this dataset. Results show that for this dataset two quite different BNs can be developed: one that is optimized to achieve the highest predictive skill, and a second network that is optimized to maximize descriptive skill. The optimum predictive BN is found to comprise 3 nodes (variables) and can predict the shoreline recession caused by unseen storm events with a skill of 65%. The optimum descriptive BN is composed of 5 nodes and can reproduce 88% of the training dataset, but with more limited predictive capabilities. The uses and limitations of these two different approaches to BN formulation are illustrated with example applications to coastal process modelling. It is anticipated that the insights provided in this paper will help to clarify the further development of Bayesian Networks applied to coastal modelling. [ABSTRACT FROM AUTHOR]

Background: The purpose of the study was to explore, analyse, and describe the patterns of public dental service utilisation among the refugee populations in Victoria, Australia, and determine their predictors at the individual and contextual levels. Methods: Data on the refugees who attended Victorian public dental services between July 2016 to June 2020 was gathered from the Dental Health Program dataset. Latent profile analysis was used to identify discrete groups among the refugee clientele with similar mean utilisation patterns across six indicator variables describing the attributes of dental services received and the site of care provision, over the study period. Multilevel multinomial logistic regression analysis was performed to examine the individual and contextual level correlates of the identified utilisation patterns. Results: Six distinct profiles of public dental service utilisation were identified among the study population (n = 25,542). The largest group comprised refugees predominantly using restorative services under general course of care (38.10%), followed by extraction services under emergency course of care (23.50%). Only a small proportion were estimated as having a higher mean utilisation of preventive services under general course of care (9.10%). Multilevel analysis revealed that the following variables had a significant association with refugee utilisation pattern: at the individual-level – demographic and ethnic attributes including age, gender, region of birth, preferred language for communication, use of language interpreter services, and type of eligibility card; at the contextual-level – characteristics of refugees' neighbourhood of residence including urbanicity, socioeconomic disadvantage, delivery of Refugee Health Program at the community health centres, and spatial accessibility to public dental services via driving and public transit modes of travel. Conclusions: The study represents a significant step towards the development of an evidence-based knowledge around public dental service utilisation among Victorian refugees. Overall, the study findings reiterate the critical need for targeted strategies to promote the importance of routine dental visits, oral disease prevention, and timely intervention among refugee groups. [ABSTRACT FROM AUTHOR]

Fire behaviour and spread predictions guides suppression strategies and public warnings during wildfires. The scientific understanding of fire behaviour forms the core of these predictions, but is incomplete, and expert judgement and experience are required to augment the evidence based knowledge. Amicus is a new decision support system that implements contemporary, published and operationalised bushfire behaviour models (e.g. rate of spread, flame height, fireline intensity, spotting distance) in the Australian bushfire context. It enables the inclusion of expert judgement and local knowledge, allows users to analyse temporal trends and uncertainty in inputs, and facilitates reliable and practical predictions. This paper provides a comprehensive overview of Amicus, including its operation and functionality, identifies the boundaries of the current understanding of fire science, discusses the major limitations in existing knowledge, and provides a framework for allowing deterministic and anecdotal/local knowledge to be incorporated into formal fire behaviour predictions. [ABSTRACT FROM AUTHOR]

Older people prefer to remain living in their own home for as long as possible; however, many require support to do so through health and other care services provided in the home. This study aimed to explore the trends in usage of a home-based care service by older people in metropolitan Melbourne and factors associated with unplanned hospitalisations. This longitudinal study analysed episodes of home-based care for people aged ≥65 years between 2006 and 2015. An episode of care was defined as the period of time during which the home care services were provided to the client. Care episodes culminated in a planned discharge from the service or an unplanned hospitalisation. Descriptive statistics and multivariable logistic regression were utilised to investigate the characteristics associated with unplanned hospitalisations. Utilisation of home-based care services over the 10-year period showed an increasing rate of use by people aged ≥85 years and a reduced usage rate by females aged 70–84 years and males 75–79 years old. Of 170,001 episodes of care, 43,608 (25.7%) resulted in an unplanned hospitalisation. Home-based care delivered to people aged ≥85 years showed an increasing rate of episodes ending in an unplanned transfer to the hospital. Between 2006 and 2015, individuals aged 85–89 years displayed a rate increase of 18.7% in episodes ending in an unplanned hospitalisation; for those aged ≥90 years, the rate rise was 43.6%. Factors associated with an unplanned hospitalisation included advancing age, male gender, living alone, cognitive dysfunction, and the complexity of medical issues. Health policy has focussed on providing services to enable older people to remain in their own home. The increasing rate of unplanned hospitalisations for community-dwellers aged ≥85 years suggests more support is required to enable ageing in place. [ABSTRACT FROM AUTHOR]

Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating their data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near-real-time horizontal line-of-sight winds across Australia during October 2020–March 2021 by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s -1 and correlation coefficients larger than or equal to 0.9. Moreover, assuming the radar measurements as the reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 4.14 and 5.81 m s -1 , respectively. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 and 3.50 m s -1 for Rayleigh-clear winds and Mie-cloudy winds. In addition, the Mie channel is shown to be more capable of capturing the wind in the planetary boundary layer (< 1500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction. [ABSTRACT FROM AUTHOR]

It is critically meaningful to accurately predict the ionospheric F2 layer critical frequency (foF2), which greatly limits the efficiency of communications, radar, and navigation systems. This paper introduced the entropy weight method to develop the combination prediction model (CPM) for long-term foF2 at Darwin (12.4° S, 131.5° E) in Australia. The weight coefficient of each individual model in the CPM is determined by using the entropy weight method after completing the simulation of the individual model in the calibration period. We analyzed two sets of data to validate the method used in this study: One set is from 2000 and 2009, which are included in the calibration period (1998–2016), and the other set is outside the calibration cycle (from 1997 and 2017). To examine the performance, the root mean square error (RMSE) of the observed monthly median foF2 value, the proposed CPM, the Union Radio Scientifique Internationale (URSI), and the International Radio Consultative Committee (CCIR) are compared. The yearly RMSE average values calculated from CPM were less than those calculated from URSI and CCIR in 1997, 2000, 2009, and 2017. In 2000 and 2009, the average percentage improvement between CPM and URSI is 9.01%, and the average percentage improvement between CPM and CCIR is 13.04%. Beyond the calibration period, the average percentage improvement between CPM and URSI is 13.2%, and the average percentage improvement between CPM and CCIR is 12.6%. The prediction results demonstrated that the proposed CPM has higher precision of prediction and stability than that of the URSI and CCIR, both within the calibration period and outside the calibration period. [ABSTRACT FROM AUTHOR]

In low-to-moderate seismicity (intraplate) regions where locally recorded strong motion data are too scare for conventional regression analysis, stochastic simulations based on seismological modelling have often been used to predict ground motions of future earthquakes. This modelling methodology has been practised in Central and Eastern North America (CENA) for decades. It is cautioned that ground motion prediction equations (GMPE) that have been developed for use in CENA might not always be suited for use in another intraplate region because of differences in the crustal structure. This paper introduces a regionally adjustable GMPE, known as the component attenuation model (CAM), by which a diversity of crustal conditions can be covered in one model. Input parameters into CAM have been configured in the same manner as a seismological model, as both types of models are based on decoupling the spectral properties of earthquake ground motions into a generic source factor and a regionally specific path factor (including anelastic and geometric attenuation factors) along with a crustal factor. Unlike seismological modelling, CAM is essentially a GMPE that can be adapted readily for use in different regions (or different areas within a region) without the need of undertaking any stochastic simulations, providing that parameters characterising the crustal structure have been identified. In addressing the challenge of validating a GMPE for use in an area where instrumental data are scarce, modified Mercalli intensity (MMI) data inferred from peak ground velocity values predicted by CAM are compared with records of MMI of past earthquake events, as reported in historical archives. South-Eastern Australia (SEA) and South-Eastern China (SEC) are the two study regions used in this article for demonstrating the viability of CAM as a ground motion prediction tool in an intraplate environment. [ABSTRACT FROM AUTHOR]

• The LSTM method is proposed to construct the LUBE approach-based prediction interval for wind power forecast. • An innovative fuzzy approach is suggested to adjust the prediction interval coverage probability. • CDOA is applied to achieve the most optimal prediction intervals. This paper proposes a novel deep learning method based on lower-upper-bound-estimation (LUBE) and long short-term memory (LSTM) models to capture the uncertainty effects in the power generation of wind turbines. The LUBE model deploys the prediction interval concept as well as the LSTM approach to make a robust and resilient model. The LSTM approach is applied to construct the optimal prediction intervals with an appropriate upper bound and lower bound with a certain confidence level. In addition, the fuzzy set theory is proposed in the model to let adjust LSTM parameters based on the decision maker's ideas. This approach empowers operators to satisfy both coverage probability and width indicators of prediction intervals to achieve the optimal solution. The collective decision optimization algorithm is introduced to provide more flexibility in tuning the LSTM parameters. The efficiency and quality of the proposed scheme are studied using some datasets gathered from the Australia wind farms. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The idea of ground-based evapotranspiration (ET) is of the most interesting for land–atmosphere interactions, such as water-saving irrigation, the performance of irrigation systems, crop water deficit, drought mitigation strategies and accurate initialization of climate prediction models especially in arid and semiarid catchments where water shortage is a critical problem. The recent year''s drought in Australia and concerns about climate change has prominent the need to manage water resources more sustainably especially in the Murrumbidgee catchment which utilizes bulk water for food security and production. This paper discusses the application of a Surface Energy Balance System (SEBS) model based on Landsat-5 TM data and field observations has been used and tested for deriving ET over Coleambally Irrigation Area (CIA), located in the southwest of NSW, Australia. 16 Landsat-5 TM scenes were selected covering the time period of 2009, 2010 and 2011 for estimating the actual ET in CIA. To do the validation the used methodology, the ground-measured ET was compared to the Landsat-5 TM retrieved actual ET results for CIA. The derived ET value over CIA is much closer to the field measurement. From the remote sensing results and observations, the root mean square error (RMSE) is 0.74 and the mean APD is 7.5%. The derived satellite remote sensing values belong to reasonable range. [Copyright &y& Elsevier]

ABSTRACT Aim Macroecological theory predicts that along direct physiological gradients there will be unimodal abundance distributions of species and consistent rates of assemblage turnover. However, the majority of marine studies that have investigated the realized distribution of species along latitudinal or temperature gradients have generally found unimodal distributions to be rare. We assess fish distributions along a temperature gradient in a stable oligotrophic seascape and suggest that unimodal distributions will be more common. Location Nearshore demersal fish habitat extending 1500 km along the coast of south-western Australia. Methods The relative abundances of demersal fish species were sampled off the coast of south-western Australia along a temperature gradient. The confounding influence of other environmental variables was tested, and the assemblage was found to be highly correlated with temperature. For the 20 most abundant species, quantile regression spline models were used to construct a model within which 95% of their abundance was expected to fall. We compared the results from this study with the proportion of unimodal species abundance distributions observed in other studies. Results Of the 20 most abundant species, 19 displayed patterns that indicated temperature was an important factor influencing their range and relative abundance; with 15 species exhibiting unimodal abundance distributions, four having ramped distribution to one end of the sampled range and one showing no consistent pattern. Main conclusions The high diversity and percentage of endemic species in terrestrial and marine habitats of south-western Australia is likely to be due to the stable geological and oceanographic history of the region. In comparison, studies of abundance distribution in other marine systems have been conducted in relatively heterogeneous and productive environments. The old, climatically buffered, oligotrophic seascape of south-western Australia has provided a simple system in which the consistent influence of physiological gradients on the abundance distribution of fish species can be observed. [ABSTRACT FROM AUTHOR]

Abstract: The significant loss of wave energy due to seabed interaction in finite depths is a known effect and bottom friction terms are used in the wave models to account for this dissipation. In this paper, a new bottom-interaction function is tested by means of the SWAN model, based on measurements at two field sites, Lake George and Lakes Entrance, both in Australia. The function accounts for dependence of the friction on the formation process of bottom ripples and on the grain size of the sediment. The overall improvement of the model prediction both for the wave height and wave period is demonstrated. [Copyright &y& Elsevier]

BACKGROUND: In Australia, smoking prevalence has declined in men since the 1950s and in women since the 1980s. Future smoking prevalence in Australia is predicted from estimates of previous and current age-specific and sex-specific cessation rates and smoking uptake in young people derived from national survey data on the prevalence of smoking between 1980 and 2007. METHODS: A dynamic forecasting model was used to estimate future smoking prevalence in the Australian population based on a continuation of these current trends in smoking uptake and cessation. RESULTS: The results suggest that Australia’s smoking prevalence will continue to fall while current rates of initiation and cessation are maintained. But a continuation of current smoking cessation and initiation patterns will see around 14% of adults still smoking in 2020. CONCLUSIONS: Smoking cessation rates will need to double for Australian smoking prevalence to reach a policy target of 10% by 2020. [ABSTRACT FROM AUTHOR]

Background: Kidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia.Methods: Data included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model.Results: Two models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration).Conclusion: This index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools. [ABSTRACT FROM AUTHOR]

Background: Serum 25 hydroxyvitamin D [25(OH)D] levels of pregnant women have been linked to various health outcomes in their offspring. Satellite-derived ultraviolet radiation (UVR) data have been used as a proxy for 25(OH)D levels, as individual-level cohort studies are time-consuming, costly and only feasible for common outcomes.Methods: Data on 25(OH)D levels from a public laboratory database were linked to data from the Western Australian Midwives' Notification System and daily erythemal UVR dose from NASA satellites. Regression analysis was used to identify the time period prior to venesection where daily UVR dose best predicted 25(OH)D levels. A predictive model was used to validate the use of daily UVR dose as a proxy for personal sun exposure during pregnancy.Results: Data from 19 173 pregnancies in women aged 18-43 years in Western Australia were included. The daily UVR dose averaged over the 90 days before venesection was the strongest UVR predictor of 25(OH)D level (a 5% increase per 1000 J m-2; equal to 3.3 nmol L-1 at the median of 66 nmol L-1). Ethnicity was the strongest predictor of 25(OH)D levels (21% lower in non-Caucasian vs Caucasian: equal to 7.2 nmol L-1 difference). Other significant predictors were gestation, age, year, parity, socio-economic status, remoteness, medical conditions and season.Conclusion: NASA-derived erythemal UVR dose in the 90 days prior to venesection is a significant predictor of 25(OH)D levels in pregnant women. Linked administrative data can be used to investigate associations between UVR during pregnancy and health outcomes in offspring. [ABSTRACT FROM AUTHOR]

We investigate a framework for improving predictions from models for spatio-temporal data. The framework is based on minimising the mean squared prediction error and can be applied to many models. We applied the framework to a model for monthly rainfall data in the Murray-Darling Basin in Australia. Across a range of prediction situations, we improved the predictive accuracy compared to predictions using only the expectation given by the model. Further, we showed that these improvements in predictive accuracy were maintained even when using a reduced subset of the data for generating predictions. [ABSTRACT FROM AUTHOR]

Ross River virus (RRV) is the most common and widespread arbovirus in Australia. Epidemiological models of RRV increase understanding of RRV transmission and help provide early warning of outbreaks to reduce incidence. However, RRV predictive models have not been systematically reviewed, analysed, and compared. The hypothesis of this systematic review was that summarising the epidemiological models applied to predict RRV disease and analysing model performance could elucidate drivers of RRV incidence and transmission patterns. We performed a systematic literature search in PubMed, EMBASE, Web of Science, Cochrane Library, and Scopus for studies of RRV using population-based data, incorporating at least one epidemiological model and analysing the association between exposures and RRV disease. Forty-three articles, all of high or medium quality, were included. Twenty-two (51.2%) used generalised linear models and 11 (25.6%) used time-series models. Climate and weather data were used in 27 (62.8%) and mosquito abundance or related data were used in 14 (32.6%) articles as model covariates. A total of 140 models were included across the articles. Rainfall (69 models, 49.3%), temperature (66, 47.1%) and tide height (45, 32.1%) were the three most commonly used exposures. Ten (23.3%) studies published data related to model performance. This review summarises current knowledge of RRV modelling and reveals a research gap in comparing predictive methods. To improve predictive accuracy, new methods for forecasting, such as non-linear mixed models and machine learning approaches, warrant investigation. Author summary: As the most common human arbovirus infection in Australia, Ross River virus exerts a significant public health and economic burden on the population. Because the virus is transmitted by mosquitoes, incidence is influenced by climate, environment, and socio-economic factors. Using epidemiological models to predict incidence or outbreaks of RRV fully utilises these data to inform decision-making. In this systematic review, we summarised models and their predictive performance, and highlighted significant exposures in order to increase understanding of transmission. [ABSTRACT FROM AUTHOR]

The differential flammability of individual plant species in landscape-scale fire behaviour is an important consideration, but one that is often overlooked. This is in part due to a relative dearth in the availability of plant flammability data. Here, we present a highly accurate predictive model of the likelihood of plant leaves entering flaming combustion as a function of leaf mass per area (LMA), leaf area (LA) and radiant heat flux using species of fire-prone dry sclerophyll forests of south-eastern Australia. We validated the performance of the model on two separate datasets, and on plant species not included in the model building process. Our model gives accurate predictions (75–84%) of leaf flaming with potential application in the next generation of fire behaviour models. Given the global wealth of species' data for LMA and LA, in stark contrast to leaf flammability data, our model has the potential to improve understanding of forest flammability in the absence of leaf flammability information. [ABSTRACT FROM AUTHOR]

Habitat modelling and predictive mapping are important tools for conservation planning, particularly for lesser known species such as many insectivorous bats. However, the scale at which modelling is undertaken can affect the predictive accuracy and restrict the use of the model at different scales. We assessed the validity of existing regional-scale habitat models at a local-scale and contrasted the habitat use of two morphologically similar species with differing conservation status (Mormopterus norfolkensis and Mormopterus species 2). We used negative binomial generalised linear models created from indices of activity and environmental variables collected from systematic acoustic surveys. We found that habitat type (based on vegetation community) best explained activity of both species, which were more active in floodplain areas, with most foraging activity recorded in the freshwater wetland habitat type. The threatened M. norfolkensis avoided urban areas, which contrasts with M. species 2 which occurred frequently in urban bushland. We found that the broad habitat types predicted from local-scale models were generally consistent with those from regional-scale models. However, threshold-dependent accuracy measures indicated a poor fit and we advise caution be applied when using the regional models at a fine scale, particularly when the consequences of false negatives or positives are severe. Additionally, our study illustrates that habitat type classifications can be important predictors and we suggest they are more practical for conservation than complex combinations of raw variables, as they are easily communicated to land managers. [ABSTRACT FROM AUTHOR]

Average depth of snow in the mountains of southeastern Australia is decreasing at a rate of 0.48 cm a-1, while the duration of the snowpack has been shortened by 18.5 days since 1954 (--3 days per decade). The major factors responsible for these declines are an increasing temperature trend of 0.36 °C per decade, and a reduction in winter precipitation at the rate of 10.1 mm a-1. While the depth of the snowpack is dependent upon precipitation trends and minimum temperatures (multiple r² = 0.43), the shortening in the length of the snow period is best predicted by increasing temperatures and reduced humidity. The major forcing of the warming trend involves greenhouse gasses, in particular atmospheric carbon dioxide and water vapor. However, the decline in winter precipitation seems to be unrelated to the forcing of greenhouse gasses, and is instead statistically associated with the Southern Oscillation Index (r = 0.38). Inverse correlations were found between depth of snow and solar irradiance, which in turn is inversely correlated with the number of sunspots per cycle. The latter findings suggest that the declining precipitation and snow trends could additionally be associated with a reduction in solar activity during the past five decades. [ABSTRACT FROM AUTHOR]

Seasonal rainfall predictions for Australia from the Predictive Ocean Atmosphere Model for Australia (POAMA), version P15b, coupled model seasonal forecast system, which has been run operationally at the Australian Bureau of Meteorology since 2002, are overconfident (too low spread) and only moderately reliable even when forecast accuracy is highest in the austral spring season. The lack of reliability is a major impediment to operational uptake of the coupled model forecasts. Considerable progress has been made to reduce reliability errors with the new version of POAMA2, which makes use of a larger ensemble from three different versions of the model. Although POAMA2 can be considered to be multimodel, its individual models and forecasts are similar as a result of using the same perturbed initial conditions and the same model lineage. Reliability of the POAMA2 forecasts, although improved, remains relatively low. Hence, the authors explore the additional benefit that can be attained using more independent models available in the European Union Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project. Although forecast skill and reliability of seasonal predictions of Australian rainfall are similar for POAMA2 and the ENSEMBLES models, forming a multimodel ensemble using POAMA2 and the ENSEMBLES models is shown to markedly improve reliability of Australian seasonal rainfall forecasts. The benefit of including POAMA2 into this multimodel ensemble is due to the additional information and skill of the independent model, and not just due to an increase in the number of ensemble members. The increased reliability, as well as improved accuracy, of regional rainfall forecasts from this multimodel ensemble system suggests it could be a useful operational prediction system. [ABSTRACT FROM AUTHOR]

Aim Many Australian Acacia species have been widely planted around the world. Some taxa are among the most aggressive of invasive alien plants and cause severe ecosystem degradation. We aimed to predict invasiveness of taxa in a large set of Australian Acacia species on the basis of easy-to-assess predictors. Location Global. Methods We considered three groups of predictors: (1) climatic affinities of species in their native ranges; (2) life history traits; and (3) human usage factors. Logistic multiple regressions were applied to construct predictive models for 85 Australian acacias (species in Acacia subgenus Phyllodineae) that are known to have been transported outside of their native range (17 known to be invasive and 68 non-invasive). The best model was then applied to predict the probability of an additional 34 Acacia species with unknown invasive status. Results Water availability in the native range and human uses were significant predictors of invasiveness in all models. Life history index (proportional to plant height, leaf area and seed mass) and climatic amplitude were also positive predictors of invasiveness when human use was not considered. The best model, based on human uses and water availability, correctly classified 92% of the species. Results suggest that Acacia species that evolved under low climatic stress have a greater chance of becoming invasive. Main conclusions Species that are useful to humans are more likely to be disseminated to and within new regions, thus increasing the risk of invasion. Combining ecological, evolutionary and human-use criteria is useful for quantifying the risk of Australian acacias becoming invasive. Acacia species can attain invasive status by virtue of intrinsic traits and/or through increased use by humans. Therefore, we predict that the invasion risk of species coming from native areas with high water availability will rise sharply if the interest in exploiting these species increases. [ABSTRACT FROM AUTHOR]

The Joint Australian Tsunami Warning Centre uses the T2 tsunami scenario database to provide forecast guidance for potential tsunami threats to the coastlines of mainland Australia and its external territories. This study describes a method for generating coastal tsunami warnings from model data obtained from the T2 scenario database. Consideration of observed coastal impacts for nine past events leads to retrospective or "ideal" warning schemes being designed. The 95th percentile values of maximum amplitude within designated coastal zones are examined and thresholds that produce the best match for the ideal schemes are selected. This empirical method is impact-based and allows the T2 scenarios to be used as a proxy for potential impacts on the coast in order to generate warnings for the Australian region. [ABSTRACT FROM AUTHOR]

1. Predicting which species are more likely to invade a region presents significant difficulties to researchers and government agencies. Methods for estimating the risk of establishment are often qualitative and rely on consultation with experts and stakeholders. The inherent subjectivity of this process can lead to ambiguities in any estimate of a species’ risk of establishment. 2. Using global presence/absence data of insect crop pests employed a self-organizing map (SOM) to categorize regions based on similarities in species assemblages. This technique enabled them to generate a list of species and rank them based on an index of the risk of establishment. However, the sensitivity of this risk list to errors in the presence/absence data has never been tested. 3. We evaluated the sensitivity of the SOM method by altering the original presence/absence data by increasing amounts and compared estimates of risk with those generated by a national coordinating body (Plant Health Australia) utilizing expert stakeholder opinion. 4. The risk list was unaffected by alterations of up to 20% of data over all regions. The error rate we detected in the data was within these limits. 5. Comparison with the expert stakeholder methodology revealed significant differences in the estimates of establishment risk. Further analysis of the Australian data revealed that a number of regions with strong trade links to Australia supported species assemblages similar to those in Australia, suggesting they are possible sources of pest species with high probability of establishment. 6. Synthesis and applications. This analysis confirms that the SOM methodology is a robust tool in the quantification of risk of establishment. In addition, SOMs can deliver a level of objectivity, which can complement current consultative processes employed by many biosecurity agencies around the world, providing a better overall assessment of invasion risk. This assessment can inform research and development funding decisions and incursion management plans for both government and host industries. While SOMs are utilized in this work for the prioritization of pest insects they can potentially be applied to any taxa (pest or native) or at any scale in which the data are available. [ABSTRACT FROM AUTHOR]

We review the efficacy of three approaches to forecasting elections: econometric models that project outcomes on the basis of the state of the economy; public opinion polls; and election betting (prediction markets). We assess the efficacy of each in light of the 2004 Australian election. This election is particularly interesting both because of innovations in each forecasting technology, and also because the increased majority achieved by the Coalition surprised most pundits. While the evidence for economic voting has historically been weak for Australia, the 2004 election suggests an increasingly important role for these models. The performance of polls was quite uneven, and predictions both across pollsters, and through time, vary too much to be particularly useful. Betting markets provide an interesting contrast, and a slew of data from various betting agencies suggest a more reasonable degree of volatility, and useful forecasting performance both throughout the election cycle and across individual electorates. [ABSTRACT FROM AUTHOR]

While significant studies have been conducted in Intermittently Closed and Open Lakes and Lagoons (ICOLLs), very few have employed Lagrangian drifters. With recent attention on the use of GPS-tracked Lagrangian drifters to study the hydrodynamics of estuaries, there is a need to assess the potential for calibrating models using Lagrangian drifter data. Here, we calibrated and validated a hydrodynamic model in Currimundi Lake, Australia using both Eulerian and Lagrangian velocity field measurements in an open entrance condition. The results showed that there was a higher level of correlation (R2 = 0.94) between model output and observed velocity data for the Eulerian calibration compared to that of Lagrangian calibration (R2 = 0.56). This lack of correlation between model and Lagrangian data is a result of apparent difficulties in the use of Lagrangian data in Eulerian (fixed-mesh) hydrodynamic models. Furthermore, Eulerian and Lagrangian devices systematically observe different spatio-temporal scales in the flow with larger variability in the Lagrangian data. Despite these, the results show that Lagrangian calibration resulted in optimum Manning coefficients (n = 0.023) equivalent to those observed through Eulerian calibration. Therefore, Lagrangian data has the potential to be used in hydrodynamic model calibration in such aquatic systems. [ABSTRACT FROM AUTHOR]

Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling (GBM), to seabed sand content point data and acoustic multibeam data and their derived variables, to develop an accurate model to predict seabed sand content at a local scale. We also addressed relevant issues with variable selection. It was found that: (1) backscatter-related variables are more important than bathymetry-related variables for sand predictive modelling; (2) the inclusion of highly correlated predictors can improve predictive accuracy; (3) the rank orders of averaged variable importance (AVI) and accuracy contribution change with input predictors for RF and are not necessarily matched; (4) a knowledge-informed AVI method (KIAVI2) is recommended for RF; (5) the hybrid methods and their averaging can significantly improve predictive accuracy and are recommended; (6) relationships between sand and predictors are non-linear; and (7) variable selection methods for GBM need further study. Accuracy-improved predictions of sand content are generated at high resolution, which provide important baseline information for environmental management and conservation. [ABSTRACT FROM AUTHOR]

Pilkey, O.H. and Cooper, J.A.G., 2007. Lifting up the flap or why quantitative beach behaviour predictive modelling can't work. Journal of Coastal Research, SI 50 (Proceedings of the 9th International Coastal Symposium), 585 – 587. Gold Coast, Australia, ISSN 0749.0208 Quantitative mathematical modelling of beach behaviour with sufficient accuracy for engineering purposes is not possible. Success in "prediction" by hindcasting does not indicate a model works. Probabilistic modelling of beach processes is not possible either because accurate determinations of error bars is impossible. One measure of the problem with quantitative modelling is the nature of model simplifications. In qualitative models where the how, why and what if answers sought are directional, or order of magnitude, simplifications are often quite useful and valid. But a quantitative model can have no simplifications of important processes. All important processes along with their interactions and feedbacks must be represented in the models. Even if all parameters in the models are perfectly understood, one never knows their intensity, frequency, direction or duration. Some widely used models such as the Bruun Rule use virtually none of the critical processes in the model (except sea level rise) that cause shoreline retreat. The CERC equation and its many variants uses only a few of what are likely to be the important parameters effecting sand transport. [ABSTRACT FROM AUTHOR]