https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:54889 Wed 20 Mar 2024 13:20:10 AEDT ]]> Uncertainties of landslide susceptibility prediction: Influences of different study area scales and mapping unit scales https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:55904 Wed 03 Jul 2024 16:08:27 AEST ]]> A Random Forest-Based Multi-Index Classification (RaFMIC) Approach to Mapping Three-Decadal Inundation Dynamics in Dryland Wetlands Using Google Earth Engine https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:50427 Tue 25 Jul 2023 18:47:10 AEST ]]> Effects of different landslide boundaries and their spatial shapes on the uncertainty of landslide susceptibility prediction https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:48954 Tue 18 Apr 2023 14:24:59 AEST ]]> A Spatial Data-Driven Approach for Mineral Prospectivity Mapping https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:53041 Tue 14 Nov 2023 11:50:37 AEDT ]]> Predicting building-related carbon emissions: a test of machine learning models https://nova.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 ]]> Uncertainties analysis of collapse susceptibility prediction based on remote sensing and GIS: Influences of different data-based models and connections between collapses and environmental factors https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:41207 Thu 28 Jul 2022 12:01:25 AEST ]]> Characterising the seasonal nature of meteorological drought onset and termination across Australia https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:44707 Thu 20 Oct 2022 12:46:27 AEDT ]]> Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: a comparison of conventional and machine-learning methods https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:24849 0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.]]> Sat 24 Mar 2018 07:11:24 AEDT ]]> Development of an advanced machine learning model to predict the pH of groundwater in permeable reactive barriers (PRBs) located in acidic terrain https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:50772 Sat 05 Aug 2023 09:37:39 AEST ]]> Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43604 Mon 26 Sep 2022 15:33:49 AEST ]]> Developing a two-decadal time-record of rice field maps using Landsat-derived multi-index image collections with a random forest classifier: A Google Earth Engine based approach https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:55691 Mon 17 Jun 2024 10:25:35 AEST ]]> Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35128 Fri 21 Jun 2019 12:52:57 AEST ]]> CBR of stabilized and reinforced residual soils using experimental, numerical, and machine-learning approaches https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:53209 Fri 17 Nov 2023 11:40:57 AEDT ]]> Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:51746 Fri 15 Sep 2023 18:22:55 AEST ]]> Uncertainties of Landslide Susceptibility Prediction Modeling: Influence of Different Selection Methods of “Non-landslide Samples” https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:54702 Fri 08 Mar 2024 12:07:11 AEDT ]]> Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:32135 Fri 04 May 2018 15:36:57 AEST ]]>