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This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

Accurate land valuation is necessary for tax purposes, land resources allocation, real estate management and urban development and planning. Since various factors from different domains affect land prices through non-linear relationships, automating the land valuation process on a large scale is a complex task. Advanced technologies in big data analysis and artificial intelligence have demonstrated superior capabilities in knowledge extraction in such cases. Accordingly, this paper develops and compares the performance of four Automated Valuation Models (AVMs) based on machine learning and deep learning techniques utilizing physical, geographical, socio-economic, environmental, legal and planning factors in Melbourne Metropolitan, Australia. According to the results, the eXtreme Gradient Boosting (XGBoost) method outperforms other algorithms of Support Vector Regression (SVR), random forest and Deep Neural Network (DNN). This method has achieved the coefficient of determination (R2) of 0.862, Mean Absolute Percentage Error (MAPE) of 0.139, and normalized Root Mean Square Error (nRMSE) of 0.281. The achieved high accuracy is due to incorporating a wide range of driving factors and applying innovative feature selection and hyperparameter tuning procedures evaluating various possible feature sets and hyperparameters. Accordingly, this paper can contribute to research, governmental and industry-based activities in terms of developing AVMs for mass land valuation. • Automated land valuation models using machine learning and deep learning • Comprehensive feature set and hybrid feature selection process for robust model performance • Spatial mapping of land values in large urban areas • Contribution to fair housing policies and urban development by accurate land valuation • Practical insights for housing affordability strategies in terms of land management [ABSTRACT FROM AUTHOR]

This paper examines the relationship between locus of control (LOC) and the demand for supplementary health insurance (SUPP). Drawing on longitudinal data from Germany, we document robust evidence that individuals internal LOC increases the take up of supplementary private health insurance (SUPP). We find that the effect of one standard deviation increase in the measure of internal LOC on the probability of SUPP purchase is equivalent to a 14 percent increase in household income. Second, we find that the positive association between self-reported health and SUPP becomes small and insignificant when we control for LOC. These results suggest that LOC might be an unobserved individual trait that can partly explain previously documented evidence of advantageous selection into SUPP. Third, we find comparable results using data from Australia, which enhances the external validity of our results. [ABSTRACT FROM AUTHOR]

• Develops a deep Bayesian survival model that integrates deep neural network and Bayesian inference into the survival analysis for rail useful lifetime modelling. • Demonstrates survival analysis is an effective way to handle censored data problems in rail useful lifetime modelling. • Conducts extensive evaluation to verify the effectiveness of this model, using Concordance index, root mean square error, and a proposed CW-index. • The estimated lifetimes obtained by this model are the closest to real lifetimes compared to other commonly used models. • This model can provide the confidence interval of the estimated lifetime, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. [ABSTRACT FROM AUTHOR]

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized bistatic radar cross section DDM of the land surface. This method was tested on both simulated DDMs and CYGNSS DDMs over the Soil Moisture Active Passive (SMAP) Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDMs were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The vegetation water content (VWC) was 0.19   kg m − 2 and 4.89   kg m − 2 for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than 0.03   m 3 m − 3 while it was approximately 0.06   m 3 m − 3 and 0.09   m 3 m − 3 for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an an ubRMSE of less than 0.12   m 3 m − 3 . [ABSTRACT FROM AUTHOR]

Mungbean, a grain legume with high nutritional value, is grown widely throughout Asia and increasingly in Australia. Despite growing interest amongst farmers, mungbean remains an inconsistent and thus risky crop to plant in Australia. Cropping system models like the Agricultural Production Systems sIMulator (APSIM) are valuable tools for helping farmers to examine options for improving crop management and assess production risks across potential growing regions for mungbean. This paper outlines the simulation capacity of a new mungbean crop model parameterized using the Plant Modelling Framework in APSIM Next Generation, the newest version of the APSIM framework. The aim of the paper is to document the parameterization and validation processes of the model. The new mungbean model was built using data from 28 field experiments to simulate measured phenology, canopy development, biomass accumulation/partitioning, stress responses, N fixation, root growth, and yield across a wide range of environments. The root mean squared error (RMSE) in predictions for grain weight and aboveground weight were 25.4 g m−2 and 91.4 g m−2, respectively. The model successfully captured the dynamics of crop response to sowing dates, water/irrigation regimes, and climate. The new mungbean model is a robust and accurate tool for use in Australia and tropical/sub-tropical Asia. Researchers can use the new mungbean model to determine best management practices such as the optimal time to sow mungbeans in different environments. The output from model simulations can help farmers assess risks associated with sowing at different times and soil water conditions specific to their region. Such risk analysis can improve farmer decision-making confidence in mungbean, increasing its potential production for Australia. Overall, the new APSIM mungbean model can be used effectively to identify and close the mungbean yield gap, mitigate risk of crop failure, and increase profits for mungbean farmers in Australia and tropical/sub-tropical Asia; it has the capacity to assist with increasing mungbean production globally under changing climate conditions. • The APSIM Next Generation mungbean model was built using data from 28 diverse field experiments. • Detailed phenology, biomass and N partitioning, and canopy data was used to parameterize parameters. • The model captures mungbean's dynamic response to water stress and targeted irrigation. • It is a robust, data-driven crop model that performs well in Australia and tropical/subtropical Asia. [ABSTRACT FROM AUTHOR]

Accurately predicting river flows over daily timescales is considered as an important task for sustainable management of freshwater ecosystems, agricultural applications, and water resources management. In this research paper, artificial intelligence (AI) techniques, namely the cascade correlation neural networks (CCNN) and the random forest (RF) models, were employed in daily river stage and river flow prediction for two river systems (i.e., Dulhunty River and Herbert River) in Australia. To develop the CCNN and RF models, a significant 3-day antecedent river stage and river flow time series were used. 80% of the whole data were used for model training and the remaining 20% for model testing. A total of ten different model structures with different input combinations were used to evaluate the optimal model in the training phase, and the results were analyzed using statistical metrics including the root mean square error (RMSE), Nash–Sutcliffe coefficient (NS), Willmott's index of agreement (WI), and Legate and McCabe's index (ELM) in the testing phase. The inter-comparison of CCNN and RF models for both river systems showed that the CCNN model was able to generate a more accurate prediction of the river stage and river flow compared to the RF model. Due to hydro-geographic differences leading to a different underlying historical data characteristics, the optimal CCNN's performance for the Dulhunty River was found to be most accurate, in terms of ELM = 0.779, WI = 0.964, and ENS = 0.862 versus 0.775, 0.968, and 0.885 for the Herbert River. Following the performance accuracies, the authors ascertained that the CCNN model can be taken as a preferred data intelligent tool for river stage and river flow prediction. [ABSTRACT FROM AUTHOR]

• The ELM model is applied to predict the ionospheric propagation factor M(3000)F2. • The ELM achieves slightly higher accuracy at a faster training speed than BPNN. • Better average accuracy of ELM over the IRI in high and low solar activity years. This paper presents an novel extreme learning machine (ELM)-based prediction model for the ionospheric propagation factor M(3000)F2 at Darwin station (12.4°S, 131.5°E; −44.5°dip) in Australia. The proposed ELM model is trained with hourly daily values of M(3000)F2 from the period 1998–2014 except 2001 and 2009. The hourly daily values of 2001 (high solar activity) and 2009 (low solar activity) are used for validating the prediction accuracy. The proposed ELM for modeling M(3000)F2 can achieve faster training process and similar testing accuracy compared with backward propagation neural network (BPNN). In addition, the performance of the ELM is verified by comparing the predicted values of M(3000)F2 with observed values and the international reference ionosphere (IRI −2016) model predicted values. Based on the error differences (the root mean square error (RMSE) and the M(3000)F2 percentage improvement values M(3000)F2 IMP (%)), the result demonstrates the effectiveness of the ELM model compared with the IRI-2016 model at hourly, daily, monthly, and yearly in high (2001) and low (2009) solar activity years. The ELM also shows good agreement with observations compared with the IRI during disturbed magnetic activity. [ABSTRACT FROM AUTHOR]

The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale (<2 km) models. [ABSTRACT FROM AUTHOR]

This paper focuses on predicting Australia's outbound international airline passenger demand using an artificial neural network (ANN) modelling method. The modelling in the study was based on annual data for the period 1993 to 2016. The model was developed using the input parameters of world GDP, world population growth, world jet fuel prices, world air fares (proxy for air travel cost), Australia's tourism attractiveness, outbound flights, Australia's unemployment levels, the Australian and United States foreign exchange rate and three dummy variables (Sydney Olympics, 9/11 and the 2006 Commonwealth Games). The artificial neural network (ANN) used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 8 inputs, 1 neurons in the hidden layers and 1 neuron in the output layer. The data was randomly divided into three data sets; training, testing and model validation. The best-fit model was selected according to four goodness-of-fit measures: mean absolute error (MAE), mean square error (MSE), root mean square errors (RMSE), AND mean absolute percentage errors (MAPE). The highest R-value obtained from the ANN model is 0.99733, demonstrating that the ANN provided a high predictive capability. [ABSTRACT FROM AUTHOR]

Objectives: Despite possessing considerable relevance for planning and delivery of effective rehabilitation interventions, systematic evaluation of cognitive function is often ignored in clinical practice. This paper describes a successful method for measuring cognitive function and the nature of cognitive deficits (CD) in people with schizophrenia admitted to psychiatric rehabilitation services.Methods: Data on the cognitive functioning of consecutive patients with schizophrenia / schizoaffective disorder admitted during a 5-year period to a public in-patient rehabilitation facility was collated retrospectively and analysed. The Brief Assessment of Cognition in Schizophrenia (BACS) was used to evaluate cognitive function.Results: It was possible to administer the BACS to 122 of 135 consecutive admissions. The mean composite score on the BACS was 1.8 standard deviations below the norm, and 43% had moderate or severe CD. The BACS sub-tests of list learning and symbol coding revealed more severe deficits.Conclusions: The study indicates that evaluation of cognitive function using brief instruments is feasible in psychiatric rehabilitation settings. Global and domain-specific CD were prevalent among people with schizophrenia. In view of the strong association of cognitive functioning with community functioning and rehabilitation outcomes, further studies exploring the feasibility and utility of routinely evaluating cognitive function are warranted. [ABSTRACT FROM AUTHOR]

Estimation of major flood flows is needed in the design and operation of large water infrastructure. This paper presents a simple probabilistic model (PM) that can be used to derive "easy to apply" prediction equations for estimation of major flood flows. The proposed method assumes that the maximum observed flood data over a large number of sites in a region can be pooled together by accounting for the across-site variations in the mean and standard deviation values. The method is developed and tested in this paper using data from 227 catchments across Victoria and NSW. The application to ungauged catchments involves the development of prediction equations using generalised least squares regression for the mean and coefficient of variation of the annual maximum flood series as a function of catchment characteristics. An independent test shows that the PM provides quite reasonable design flood estimation in the study area for average recurrence intervals in the range of 20 to 200 years, with median relative error values (compared to at-site flood frequency estimates) in the range of 10% to 35%. The method is under further development, eg. consideration of the inter-station correlation in pooling the standardised data from the nearby stations and comparison of results with the rainfall-based methods. The method has the potential to derive regional flood prediction equations for major floods by pooling the flood data from all the Australian states. [ABSTRACT FROM AUTHOR]

In this paper, the application of a hybrid coastal morphodynamic model to forecast inter-annual beach change is discussed through the prediction of beach change in a high energy sandy beach over a period of 5 years. The modelling approach combines a 'reduced-physics' formulation with a data-driven approach through an inverse technique to form the hybrid coastal morphodynamic model. The beach considered for the demonstration of the model is the Narrabeen Beach, which is a dynamic sand beach located in New South Wales, Australia. Despite its simplicity, we find that the model is able to capture beach change at Narrabeen Beach at inter-annual timescales with root mean square error between measured and computed beach profiles less than 0.4 m on average. Even though the model is used to forecast inter-annual beach change in this study, its ability to predict beach change is not limited to that timescale but depends on the frequency of historic beach profile measurements available to determine key unknown parameters of the model. Also, the length of profile forecasts largely depends on the length of available historic measurements where longer data sets allow longer predictions within a range of beach behaviour contained in the observations. The ability of the model to reliably forecast coastal change at inter-annual and potentially at other timescales, and its high efficiency make it possible to be used in providing multiple simulations required for probabilistic coastal change forecasts which will be very useful for coastal management purposes. [ABSTRACT FROM AUTHOR]

• An innovative framework to derive intensity-frequency-duration curves from satellite data for remote areas. • A variant of the Bartlett-Lewis model calibrated with sub-daily satellite precipitation data to disaggregate daily gauge data. • This new framework was tested for a wide range of climates in Australia. • IFD curves can be derived with reasonable accuracy for data scarce regions. Intensity-frequency-duration (IFD) curves (also known as intensity-duration-frequency (IDF) curves in some regions) are widely used to take into consideration the probability of occurrence of extreme precipitation for the design of water related infrastructure. Derivation of IFD curves is based on statistical analysis of observed extreme precipitation events and thus the reliability of the curves mostly depends on the record length of observations. At sub-hourly timescales however, ground-based precipitation observations are often absent or very limited in terms of spatial coverage and record length, particularly in remote areas around the world. Given the availability of satellite-derived precipitation products (SPPs) with finer spatiotemporal resolution and near-global coverage, there is great potential to use these products in the planning and design of infrastructure. This paper proposes a novel framework, SIFD (Satellite-derived IFD curves) to combine long-term daily gauge data and sub-hourly SPP to estimate IFD curves for time intervals from 0.5 to 24 h. A recent variant of the Random Parameter Bartlett-Lewis Rectangular Pulse model (RPBLRPX) was used to disaggregate daily gauge data to derive IFD curves. In the absence of sub-daily rain gauge data, the statistics required for the RPBLRPX model were obtained using the Integrated Multi-satellitE Retrievals for GPM (IMERG; 30-min, 0.1° resolution) product derived from the Global Precipitation Measurement (GPM) mission. Performance of the SIFD framework was tested against gauge data from seven Australian sites across a wide range of climatologies. The disaggregation scheme coupled with the RPBLRPX model was capable of reproducing the wet-period mean intensity, wet-period standard deviation, and wet-period fraction with reasonable accuracy across the sub-daily timescales. The coefficient of determination ( r 2) values were 0.39 (mean intensity), 0.38 (standard deviation), and 0.87 (wet-period fraction) at 0.5 h timescale. Furthermore, despite some differences in methodology and record length, the IFD estimates computed using the SIFD framework were fairly close to those of the Bureau of Meteorology (BoM). The skill metrics for Sydney were the best with a bias ratio (BR) of 1.01, root mean square error (RMSE) of 13% of the mean of the BoM estimates, and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.97. The worst case was found at Hobart (BR = 1.43, RMSE = 66%, and NSE = 0.26). Overall, the results indicate that the proposed SIFD framework could provide a viable alternative to estimate unbiased IFD curves at the sub-hourly timescale in the absence of adequate sub-hourly ground-based observations. [ABSTRACT FROM AUTHOR]

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to ≈ 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR , including the Willmott's, Nash-Sutcliffe's coefficient and Legates & McCabe's Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications. [Display omitted] • This paper presents a hybrid CSVR algorithm for global solar radiation prediction. • The proposed approach combines Convolutional Neural Network with a Support Vector Machine. • An Atom Search-based feature selection is applied to improve the performance of the system. • Experiments are carried out in data at six solar energy farms in Queensland, Australia. • The results show the efficacy of deep learning CSVR for solar energy prediction problems. [ABSTRACT FROM AUTHOR]

Medium resolution satellite-derived fractional cover estimates of bare soil (f BS), photosynthetic vegetation (f PV), and non-photosynthetic vegetation (f NPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of f PV and f NPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall 'pulses' and subsequent transition to non-photosynthetic dormancy or senescence; (b) multiple time scales of antecedent climatic influence on vegetation cover; (c) the susceptibility of dunes to wind-blown sand drift during periods of low cover; and (d) the implications of image resolution choice when ground cover is heterogeneous. A spectral unmixing model for Australia's arid zone (termed 'AZN') was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of f PV and f NPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data. The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (f BS), 6.5% (f PV) and 15.8% (f NPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as f PV , and 3–9 months later as a peak in f NPV. Components of f PV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of f NPV , if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover <14%) and partial erodibility (cover <35%) were more common at the driest sites but did not universally match aridity levels, due to fires and differing vegetation. The targeting of dune crest regions highlighted their enhanced susceptibility to sand drift (in most cases), and, given their occurrence on relatively narrow ridges (~30 m), the importance of estimating cover at Landsat resolutions or better (e.g. Sentinel-2). • Arid fractional vegetation cover was unmixed from Landsat and Sentinel-2 data. • Pulse-reserve behaviour was detected as coupled peaks in green then dry vegetation. • Decomposed time series revealed multiple time scales of rainfall response. • 30 m resolution or less is required to detect wind erosion risk on vegetated dunes. [ABSTRACT FROM AUTHOR]

Locata Corporation's positioning technology 'Locata' is a terrestrial-based RF-ranging technology that provides high accuracy position solutions using a network (LocataNet) of time synchronised pseudolite-like transceivers (LocataLites). This technology provides centimetre level accurate position solutions for static positioning using carrier phase measurement data which provides an advantage over other technology for monitoring structural movement in many applications. The Locata network can be deployed in such a configuration around a structure that optimal network geometry could be ensured despite site constraints. This paper describes the first deformation trial using Locata technology carried out at Tumut Pond Dam, in the state of New South Wales, Australia. This trial was run for 22 hours and yielded millimetre-level precision for horizontal positions, and centimetre-level vertical precision for all observed epochs. The accuracy of the coordinate solutions was at the few millimetre level with standard deviations of 2.5 mm, 2.1 mm and 18 mm for the east, north and height components respectively. The position solutions were generated by an iterative least squares estimation process of Locata carrier phase observations per epoch. Then mean of sixty seconds coordinate solutions was used as input for a Kalman filter. Standard deformation detection methods were applied. No significant displacement was observed during this trial. [ABSTRACT FROM AUTHOR]

In this paper, the validity of Recommendation ITU-R P.1546 in a short-range terrestrial environment is analyzed. Its three versions (P.1546, P.1546-1, and P.1546-2) are compared against simple models and evaluated using measurement results that were obtained by utilizing the pilot signal of a commercial code-division multiple-access mobile telephone network. Measurement results show that P.1546-2, on average, underestimates the field strength by more than 10 dB for typical Australian rural areas. However, it improves the error standard deviation compared to previous versions. The causes of these effects and the suggestions for further development of the Recommendation are discussed and evaluated. [ABSTRACT FROM AUTHOR]

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models. [ABSTRACT FROM AUTHOR]

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora wind farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of 5-min intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory, GRU, autoregressive integrated moving average and support vector machine, which are tuned to optimise outcome. To further evaluate the efficacy of the proposed model, another data set taken from the Capital wind farm in Australia is used. It is observed that the hybrid deep learning model exhibits superior performance in both the data sets over other forecasting models to improve the accuracy of wind power forecasting, numerically for the Bodangora wind farm, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error. [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]

Density log data is one of the key physical attributes used for reservoir characterization by quantifying and qualifying lithological attributes in a wellbore. The density log is most often acquired by geophysical wireline logging techniques after drilling. However, wireline logging can be difficult to execute in highly deviated wells and alternative data acquisition such as logging-while-drilling (LWD) can be very costly. This paper describes the process of using instantaneous drilling attributes together with a machine learning algorithm, extreme gradient boosting (XGBoost), to generate a pseudo density log. The mean absolute error (MAE) and root mean square error (RMSE) are used as the evaluation metrics. Case studies are performed using data from six coalbed methane (or coal seam gas) wells in the Surat Basin, Australia. The inputs include drilling data [weight on bit (WOB), rotations per minute (RPM), torque, true vertical depth (TVD) and rate of penetration (ROP)] and LWD data (i.e., natural gamma ray and hole diameter). The MAE of pseudo density log for most wells is between 0.08 and 0.11 g/cc (except Well 4 is 0.16 g/cc), which results in an average error rate less than 5%. It is found that TVD, gamma ray, ROP, and hole diameter are four most important features. Further experiment shows that the model with these four important features has almost the same performance as the model with all features. The proposed machine learning methodology can assist petroleum engineers and geologists in reservoir characterization by generating pseudo density logs from ongoing LWD and drilling data in real time. It can potentially mitigate the need to run wireline logging tools after drilling or costly LWD techniques whilst drilling. • Pseudo density logs of six coal seam gas wells in Surat Basin, Australia are generated using a machine learning method (XGBoost). • Error rate of generated pseudo density logs for most wells is less than 5%. • True vertical depth (TVD), gamma ray, rate of penetration (ROP), and hole diameter are four important parameters. [ABSTRACT FROM AUTHOR]

• Deep learning hybrid model is proposed for solar radiation prediction. • Convolutional network is used to extract features for solar radiation. • Long Short-Term Memory is used for prediction of solar radiation. • Deep learning hybrid model outperforms all comparative models. • The model can be adopted as a decision-support tool in solar energy simulation. This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Network for pattern recognition with the Long Short-Term Memory Network for half-hourly global solar radiation (GSR) forecasting. The Convolution network is applied to robustly extract data input features from predictive variables (i.e., statistically significant antecedent inputs) while Long Short-Term Memory absorbs them for prediction. Half-hourly GSR for Alice Springs (Australia: 01 January 2006 to 31 August 2018) are extracted with stationarity checks applied via unit-root and mutual information test to capture antecedent GSR values required to forecast future GSR. The proposed hybrid model is benchmarked with standalone models as well as other Deep Learning, Single Hidden Layer and Tree based models. The results show that the benchmarked models are not able to generate satisfactory GSR predictions and the proposed hybrid model outperforms all other counterparts. The hybrid model registers superior results with over 70% of predictive errors lying below ±10 Wm−2 and outperforms the benchmark model for 1-Day half-hourly GSR prediction with low Relative Root Mean Square Error (≈1.515%), Mean Absolute Percentage Error (≈4.672%) and Absolute Percentage Bias (≈1.233%). This study ascertains that a proposed hybrid model based on a convolution network framework can accurately predict GSR and enable energy availability to be regularly monitored over multi-step horizons when coupled with a low latency Long Short-Term Memory network. Furthermore, it also concludes that the proposed model can have practical implications in forecasting GSR , capitalizing its versatility as a stratagem in monitoring solar powered systems by integrating freely available solar radiation into a real power grid system. [ABSTRACT FROM AUTHOR]