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Correct inflow prediction is a critical non-engineering measure for ensuring flood control and increasing water supply efficiency. In addition, accurate inflow prediction can offer reservoir planning and management guidance since inflow is the major input into reservoirs. This study aims at generalizing a machine learning model for forecasting reservoir inflow. Daily, weekly, and monthly inflow and rainfall time-series data have been collected as two hydrological parameters to forecast reservoir inflow using a machine learning method, namely, support vector regression (SVR). Four different SVR kernels have been applied in this study. The kernels are radial basis function (RBF), linear, normalized polynomial, and sigmoid. Two scenarios for input selection have been implemented. Dokan dam in Kurdistan region of Iraq and Warragamba Dam in Australia were selected as the case studies for this research. For the purpose of generalization, the proposed models have been applied to two countries with a different climate condition. The findings showed that daily timescale outperformed weekly and monthly, while RBF outperformed the other SVR kernels with root-mean-square error (RMSE) = 145.7 and coefficient of determination (R2) = 0.85 for forecasting daily inflow at Dokan dam. However, RBF kernel could not perform well for forecasting daily inflow in Warragamba dam. The results showed that the proposed machine learning model performed well at Kurdistan region of Iraq only, while the result for Australia was not accurate. Therefore, the proposed models could not be generalized. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) simulation is of great importance for various procedures of the hydrological cycle, irrigation, agronomic management, and planning and management of water resources. Agricultural water management and irrigation scheduling require accurate estimation of crop water requirements. The reference evapotranspiration (ETo), which is used to calculate the evapotranspiration of each crop, is estimated first. The purpose of this study was to develop a new coupled model for estimating daily ETo. In this research, differing artificial intelligence (AI) approaches including support vector regression (SVR), algorithm of innovative gunner (AIG), and hybrid algorithm of innovative gunner-support vector regression (AIG-SVR) models were used in two different climatic conditions to estimate reference evapotranspiration, one station in the dry region (Marree Aero) and one station in the humid region (St Helen Aerodrome) in Australia. The results of the AIG-SVR model were compared with those of the conventional support vector regression (SVR) model using several performance evaluation methods comprising the statistical criteria including correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe coefficient (NS), and RMSE-observations standard deviation ratio (RSR)). Evaluation results showed that the developed coupled models yielded better results than the classic SVR, with the AIG-SVR outperforming the SVR. The results showed that ETo values predicted by all AIG-SVR models agreed well with the corresponding observed values, with R, RMSE (mm day−1), NS, and RSR = 0.945, 1.124, 0.894, 0.325 respectively in Marree Aero station and 0.951, 0.476, 0.905, and 0.307 respectively in St Helen Aerodrome station in testing data sets. In the local scenario, RMSE and RSR reduced by up to 15.805% (1.335 to 1.124) and 15.803% (0.386 to 0.325), respectively, and NSE and R increased by up to 5.176 (0.850 to 0.894) and 2.383% (0.923 to 0.945), respectively, in testing data sets at Marree Aero station whereas RMSE and RSR reduced by up to 19.594% (0.592 to 0.476) and 19.633% (0.382 to 0.307), respectively, and NSE and R increased by up to 6.096 (0.853 to 0.905) and 2.922% (0.924 to 0.951), respectively, in testing data sets at St Helen Aerodrome station. The findings reveal that the AIG-SVR model performs better than the SVR model. As a result of this study, AIG-SVR and SVR models can both provide important insights into how ETo simulating can be improved. Based on the results obtained from the hybrid model in this research. ETo in Australia is possibly considered an application system in Australia. [ABSTRACT FROM AUTHOR]

One of the most reliable and prominent renewable energy resource which addresses the global energy demand is the Solar photovoltaics (PV) systems. Usage of the PV system substantially reduces the carbon footprint of the energy generation process leading towards an environmentally friendly alternative. The substantial rise in the adaptation of the distributed energy resources likes solar PV and batteries into conventional electric power grids, has increased the complexity of the energy management problem. Many Researchers across the world are working towards developing accurate forecasting models to predict the solar PV system’s generation capacity in advance to effectively manage the supply and demand. In this research, a modified support vector regression (SVR) based solar irradiance forecasting model with a minute wise time horizon is proposed, developed, and tested using the data obtained from Bureau of Meteorology (BOM), Australia. The performance of the proposed approach is then validated using the benchmark statistical models like, the persistence algorithm, autoregression model, moving average model, a hybrid autoregressive moving average model and an autoregressive integrated moving average model. Metrics like root mean square error (RMSE), the mean absolute error (MAE) and mean bias error (MBE) are used to validate the performance of the proposed approach. Results indicated that the Modified SVR model reduces the forecasting error and the computational complexity substantially and outperforms the other conventional approaches. The increased performance of the forecasting method will assist in developing an efficient energy management system for future electricity grids. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting (STLF) is of positive significance to the effective management of power companies and the stable operation of society. In spite of many studies conducted in this field, there are few to consider the inherent disadvantages of an individual module, which results in sub-optimal forecasting accuracy. Therefore, by integrating data preprocessing module and optimization module into support vector regression (SVR) forecasting module, this paper successfully presents a novel model (AHO-Lasso-SVR). The data preprocessing module, which is comprised of feature construction and Lasso feature selection, is used to construct and select meaningful features. An adaptive hybrid optimization (AHO) algorithm is proposed by introducing two strategies on the basis of standard particle swarm optimization (PSO). The AHO algorithm inevitably increases the computational complexity of model learning, thus, this paper proposes a subsampling technology to improve the optimization efficiency of the algorithm based on the sparsity of SVR. The proposed model is used to forecast the load at 48 points in the next day. To verify the properties of the proposed model, power load data from New South Wales, Australia are adopted as a case study. The results reveal that our model positively exceeds all comparison models in terms of forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]

Context: The contribution of forest ecosystem services to human well-being varies over space following the dynamics in forest cover. Use of machine learning models is increasing in projecting forest cover changes and investigating the drivers, yet references are still lacking for selecting machine learning models for spatial projection of forest cover patterns. Objectives: We assessed the ability of nonparametric machine learning techniques to project the spatial distribution of forest cover and identify its drivers using a case study of Tasmania, Australia. Methods: We developed, evaluated, and compared the performance of four nonparametric machine learning models: support vector regression (SVR), artificial neural networks (ANN), random forest (RF), and gradient boosted regression trees (GBRT). Results: The results demonstrated that RF far outperformed the other three models in both fitting and projection accuracy, and required less computional costs. GBRT outperformed SVR and ANN in projection accuracy. However, RF exhibited serious overfitting due to the full growth of its decision trees. The influence rankings of explanatory variables on spatial patterns of forest cover were different under the four models. Land tenure type and rainfall were identified among the top four most influential variables by all four models. The ranking produced by the RF model was significantly different with topographic factors associated with land clearing and production costs (elevation and distance to timber facilities) being the two most influential variables. Conclusions: We encourage practitioners to consider nonparametric machine learning methods, especially RF, when facing problems of complex environmental data modelling. [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]

This study describes the supervised support vector regression method and cuckoo optimization algorithm (COA-SVR), a newly developed mineral potential modelling (MPM) technique, and a case study of its application to predicting gold potential in the Granites-Tanami Orogen (GTO), Australia. COA-SVR, which was borne out of a computer program coded in MATLAB®, incorporates a popular radial basis function (RBF) known as the Gaussian kernel function. The COA-SVR model was trained using a series of predictor maps previously generated and described by Roshanravan et al. (2020a), and corroborated by way of a 10-fold cross-validation. The modelling results indicate that the COA-SVR approach to MPM outperformed the previous data-driven (i.e., random forest) and continuous (i.e., fuzzy gamma, geometric average, and data-driven index overlay) models of Roshanravan et al. (2020a). As such, the newly proposed COA-SVR method presents a valid approach to MPM. To further constrain the results, the gold prospective domains identified via COA-SVR and the previous models of Roshanravan et al. (2020a) were delimited, using the student's t-value and concentration-area (C-A) fractal techniques. Following the above, indices of overall performance (O e), accuracy (A), kappa (K) and normalized density (N d) were applied to further assess the gold prospective domains as delimited by the student's t-value and C-A fractal techniques. The results are consistent with the COA-SVR and random forest models having outperformed the more traditional modelling approaches documented by Roshanravan et al. (2020a). Given the positive results obtained in this study and similarly positive results reported by others, we believe that the apparent superior nature of machine-learning algorithms strongly merits further consideration, research and more widespread application in MPM. [Display omitted] • New approach to mineral potential modelling called a COA-SVR • Approach was tested on orogenic gold systems in the GTO, Australia. • Results were compared to previously generated mineral potential models. • Results indicate that the COA-SVR method presents a valid approach to MPM. • Study highlighted there is a direct correlation between the indices of Oe , A and K. [ABSTRACT FROM AUTHOR]