https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 Short-term River Streamflow Modeling Using Ensemble-based Additive Learner Approach https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39600 Wed 28 Feb 2024 14:58:30 AEDT ]]> Experimental Analysis of Incipient Motion for Uniform and Graded Sediments https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39662 Wed 27 Jul 2022 15:31:22 AEST ]]> Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38911 Wed 02 Mar 2022 14:33:32 AEDT ]]> Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46670 3/h were selected and randomly divided into two groups. In all, 238 wells (70%) were used for training the models and 101 wells (30%) were used for testing and validating the models. Fifteen conditioning factors were selected as input parameters for the modeling. The accuracy of the groundwater potential maps for the study area was determined using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation of error (SD), as well as the area under the receiver operating characteristic (ROC) curve (AUC). Overall, the results demonstrated that ANFIS-GA had the highest prediction capability (AUC = 0.915) for groundwater potential mapping followed by ANFIS-BBO (0.903), entropy (0.862), FR (0.86), ANFIS-SA (0.83), ANFIS (0.82) and EBF (0.80). According to the entropy model, land-use, soil order and rainfall factors had the highest impact on groundwater potential in the study area. The results of this research show that the ANFIS models combined with meta-heuristic optimization algorithms can be a useful decision-making tool for assessment and management of groundwater resources.]]> Tue 29 Nov 2022 08:50:38 AEDT ]]> New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39454 SWARA-BBO, GALDITSI-BBO, GALDITSWARA-DE and GALDITSI-DE models, respectively. The results from the GALDITSI-DE model outperformed all other models at improving the accuracy of the vulnerability assessment. Moreover, the statistical-metaheuristic method yielded more accurate results than SWARA-metaheuristic hybrid models. The vulnerability map of the studied region indicates that the northwestern and western areas are very highly vulnerable. According to GALDITSI-DE model, 42%, 17%, 18% and 22% of the aquifer areas respectively have a low, medium, high and very high vulnerability to seawater intrusion. The research findings could be applied by regional authorities to manage and protect groundwater resources.]]> Tue 09 Aug 2022 14:28:09 AEST ]]> A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:50385 Mon 24 Jul 2023 13:23:30 AEST ]]> Model identification and accuracy for estimation of suspended sediment load https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53269 Mon 20 Nov 2023 12:56:41 AEDT ]]>