https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:44633 Wed 28 Feb 2024 15:20:44 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:24949 Wed 11 Apr 2018 12:20:14 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34898 Wed 07 Feb 2024 16:53:23 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:54529 Tue 27 Feb 2024 19:44:40 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:47677 2. This process is considered to be one of the largest global carbon fluxes and is affected by different physicochemical and biological properties of soil, land use, vegetation types and climate patterns. Soil respiration recently received much attention, and it could be measured in two states basal respiration (BR) and substrate induced respiration (SIR) which together gives a good representation of the general soil microbial activity. The aim of this study was to estimate the BR and SIR of 150 data points obtained from soil samples collected from the surface to 20 cm of depth under different land use categories using the Artificial Neural Network (ANN) and Linear Regression Methodology (LRM). This study is bringing data from an arid area, and there is little information on this issue. Soil samples were chosen from three provinces of Iran, with humid subtropical and semi-arid climate patterns. In each soil sample a variety of characteristics were measured: soil texture, pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), organic carbon (OC), OC fractionation data e.g. light fraction OC (LOC), heavy fraction OC (HOC), cold water extractable OC (COC) and warm water extractable OC (WOC), population of fungi, bacteria, actinomycete, BR and SIR. Our goal was to use the most efficient ANN-model to predict soil respiration with simple soil data and annual precipitation (AP) and mean annual temperature (MAT) and compare it with LRM. Our results indicated that for an ANN model containing all the measured soil parameters (14 variables), the R² and RMSE values for BR prediction were 0.64 and 0.05 while these statistical indicators for SIR obtained 0.58 and 0.15, respectively; whereas the addition of AP and MAT data to this model (16 variables) caused a decrease in statistical indicators. When the R² and RMSE values of the BR-ANN and SIR-ANN predicted using an ANN model with only 7 variables (including OC, pH, EC, CCE and soil texture) they were estimated to be 0.66, 0.043 and 0.52, 0.16, respectively. Overall, LRM in comparison to ANN had a lower R^2M. Therefore, the results show that ANN modeling is a reliable method for predicting soil respiration, even when based on easy to measure data. Our results revealed that highest and lowest BR and SIR were recorded in rice paddy soils and saline lands, respectively. In total, soil respiration (BR: 0.09 vs 0.06 and SIR: 0.46 vs 0.32 mg CO₂ g^-1 day^-1) was higher in agricultural land compared to natural covered land.]]> Tue 24 Jan 2023 16:15:52 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:32559 Tue 19 Jun 2018 11:56:22 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45917 Tue 08 Nov 2022 09:32:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:48431 Thu 16 Mar 2023 14:17:53 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:35130 98%) compared with supervised ANNs. AlexNet demonstrated the highest accuracy (99.55%) with negligible false classifications. The results indicate that sEMG quality evaluation can be automated via an ANN without compromising human-like classification accuracy. This classifier will be publicly available and will be a valuable tool for researchers and clinicians using electromyography.]]> Thu 02 Apr 2020 15:47:43 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42730 Thu 01 Sep 2022 13:32:25 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:19746 Sat 24 Mar 2018 07:53:41 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45518 μSM) have shown promising results over arid and semi-arid landscapes. However, the linearity of these algorithms is affected by factors such as vegetation, soil texture and meteorology in a complex manner. This study tested a (i) Regression Tree (RT), an Artificial Neural Network (ANN), and a Gaussian Process Regression (GPR) model based on the soil thermal inertia theory over a semi-arid agricultural landscape in Australia, given the ability of machine learning algorithms to capture complex, non-linear relationships between predictors and responses. Downscaled soil moisture from the RT, ANN and GPR models showed root mean square errors (RMSEs) of 0.03, 0.09 and 0.07 cm³/cm³ compared to airborne retrievals and unbiased RMSEs (ubRMSEs) of 0.07, 0.08 and 0.05 cm³/cm³ compared to in-situ observations, respectively. The study showed encouraging results to integrate machine learning techniques in estimating near-surface soil moisture at a high spatial resolution.]]> Mon 31 Oct 2022 14:02:59 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:43797 Fri 30 Sep 2022 13:54:20 AEST ]]>