https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34898 Wed 07 Feb 2024 16:53:23 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42417 Tue 23 Aug 2022 08:55:24 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39499 2 emissions: urbanisation, R&D, population size, GDP, and energy use. The study used quarterly data throughout 1971Q1–2014Q4 to develop, calibrate, and validate the models. Each model was developed using 140 observations and validated on 36 observations. In tuning each ML model for comparative purposes, 10-fold with cross-validation approach was used in selecting the optimal hyperparameters and their associated arguments. The results indicate that the random forest (RF) model attained the highest coefficient of determination (R²) of 99.88%, followed by the k-nearest neighbour (KNN) (99.87%), extreme gradient boosting (XGBoost) (99.77%), decision tree (DT) (99.63%), adaptive boosting (AdaBoost) (99.56%), and the support vector regression (SVR) model (97.67%). Overall, the RF algorithm is the best performing ML algorithm in accurately predicting building-related CO₂ emissions, whereas the best algorithm in terms of time efficiency is the DT algorithm. The KNN model is highly recommended when practitioners want to have accurate predictions in a timely manner. RF, KNN, and DT models could be added to the toolkits of environmental policymakers to provide high-quality forecasts and patterns of building-related CO₂ emissions in an accurate and real-time manner.]]> Tue 09 Aug 2022 14:38:12 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39790 d) ranged from 5 to 229 mL/g (median: 28 mL/g), with 63% of the Fijian soils and 35% of the Australian soils showing K_d values that exceeded the observed median Kd. Multiple linear regression showed that TOC, amorphous aluminum and iron oxides contents, anion exchange capacity, pH, and silt content, jointly explained about 53% of the variance in PFOS K_d in soils. Variable charge soils with net positive surface charges, and moderate to elevated TOC content, generally displayed enhanced PFOS sorption than in temperate or tropical soils with TOC as the only sorbent phase, especially at acidic pH ranges. For the first time, two artificial neural networks were developed to predict the measured PFOS K_d (R² = 0.80) in the soils. Overall, both TOC and surface charge characteristics of soils are important for describing PFOS sorption.]]> Thu 23 Jun 2022 14:06:17 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41010 Thu 21 Jul 2022 11:54:27 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49727 Mon 29 May 2023 16:23:44 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53038 Mon 13 Nov 2023 09:02:15 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41605 Mon 08 Aug 2022 10:03:24 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45816 Mon 07 Nov 2022 12:04:03 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46092 Fri 11 Nov 2022 09:45:06 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38948 Fri 11 Mar 2022 12:45:18 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:49179 Fri 05 May 2023 15:44:03 AEST ]]>