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    An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland
    (Springer, 2016) Deo, Ravinesh C.; Sahin, Mehmet
    A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model was utilized to simulate the mean streamflow water level (Q(WL)) for three hydrological sites in eastern Queensland (Gowrie Creek, Albert, and Mary River). The performance of the ELM model was benchmarked with the artificial neural network (ANN) model. The ELM model was a fast computational method using single-layer feedforward neural networks and randomly determined hidden neurons that learns the historical patterns embedded in the input variables. A set of nine predictors with the month (to consider the seasonality of Q(WL)); rainfall; Southern Oscillation Index; Pacific Decadal Oscillation Index; ENSO Modoki Index; Indian Ocean Dipole Index; and Nino 3.0, Nino 3.4, and Nino 4.0 sea surface temperatures (SSTs) were utilized. A selection of variables was performed using cross correlation with Q(WL), yielding the best inputs defined by (month; P; Nino 3.0 SST; Nino 4.0 SST; Southern Oscillation Index (SOI); ENSO Modoki Index (EMI)) for Gowrie Creek, (month; P; SOI; Pacific Decadal Oscillation (PDO); Indian Ocean Dipole (IOD); EMI) for Albert River, and by (month; P; Nino 3.4 SST; Nino 4.0 SST; SOI; EMI) for Mary River site. A three-layer neuronal structure trialed with activation equations defined by sigmoid, logarithmic, tangent sigmoid, sine, hardlim, triangular, and radial basis was utilized, resulting in optimum ELM model with hard-limit function and architecture 6-106-1 (Gowrie Creek), 6-74-1 (Albert River), and 6-146-1 (Mary River). The alternative ELM and ANN models with two inputs (month and rainfall) and the ELM model with all nine inputs were also developed. The performance was evaluated using the mean absolute error (MAE), coefficient of determination (r(2)), Willmott's Index (d), peak deviation (P-dv), and Nash-Sutcliffe coefficient (E-NS). The results verified that the ELM model as more accurate than the ANN model. Inputting the best input variables improved the performance of both models where optimum ELM yielded R-2 approximate to(0.964, 0.957, and 0.997), d approximate to(0.968, 0.982, and 0.986), and MAE approximate to(0.053, 0.023, and 0.079) for Gowrie Creek, Albert River, and Mary River, respectively, and optimum ANN model yielded smaller R-2 and d and larger simulation errors. When all inputs were utilized, simulations were consistently worse with R-2 (0.732, 0.859, and 0.932 (Gowrie Creek), d (0.802, 0.876, and 0.903 (Albert River), and MAE (0.144, 0.049, and 0.222 (Mary River) although they were relatively better than using the month and rainfall as inputs. Also, with the best input combinations, the frequency of simulation errors fell in the smallest error bracket. Therefore, it can be ascertained that the ELM model offered an efficient approach for the streamflow simulation and, therefore, can be explored for its practicality in hydrological modeling.
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    An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland (vol 188, pg 90, 2016)
    (Springer, 2016) Deo, Ravinesh C.; Sahin, Mehmet
    [Abstract Not Available]
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    Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia
    (Elsevier Science Inc, 2015) Deo, Ravinesh C.; Sahin, Mehmet
    The forecasting of drought based on cumulative influence of rainfall, temperature and evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture, ecosystems, wildlife, tourism, recreation, crop health and hydrologic engineering. Predictive models of drought indices help in assessing water scarcity situations, drought identification and severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network (ANN) as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index (SPEI) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006-2012. The predictive variables were: monthly rainfall totals, mean temperature, minimum temperature, maximum temperature and evapotranspiration, which were supplemented by large-scale climate indices (Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and Indian Ocean Dipole) and the Sea Surface Temperatures (Nino 3.0, 3.4 and 4.0). A total of 30 ANN models were developed with 3-layer ANN networks. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton backpropagation algorithms were utilized to train the network, tangent and logarithmic sigmoid equations used as the activation functions and the linear, logarithmic and tangent sigmoid equations used as the output function. The best ANN architecture had 18 input neurons, 43 hidden neurons and 1 output neuron, trained using the Levenberg-Marquardt learning algorithm using tangent sigmoid equation as the activation and output functions. An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute Error ranging from 0.9945-0.9990, 0.0466-0.1117, and 0.0013-0.0130, respectively for individual stations. Also, the Willmott's Index of Agreement and the Nash-Sutcliffe Coefficient of Efficiency were between 0.932-0.959 and 0.977-0.998, respectively. When checked for the severity (S), duration (D) and peak intensity (I) of drought events determined from the simulated and observed SPEI, differences in drought parameters ranged from -1.41-0.64%, -2.17-1.92% and -3.21-1.21%, respectively. Based on performance evaluation measures, we aver that the Artificial Neural Network model is a useful data-driven tool for forecasting monthly SPEI and its drought-related properties in the region of study. (C) 2015 Elsevier B.V. All rights reserved.
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    Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia
    (Elsevier Science Inc, 2015) Deo, Ravinesh C.; Sahin, Mehmet
    The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties. (C) 2014 Elsevier B.V. All rights reserved.
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    Erratum to: Poster Abstracts, 17th Annual Meeting of the International Association of Medical Science Educators, St. Andrews, Scotland, UK, June 8–11, 2013. (Medical Science Educator, (2013), 23, S4, (668-725), 10.1007/BF03341701)
    (Springer, 2016) Deo, Ravinesh C.; Şahin, Mehmet
    Abstract 218—Team-Based and Interprofessional Education: The Learning of Anatomy by Medical Students from Different Backgrounds in a Graduate Entry Course was published without authorship. Authors should have been: Michelle Machado1 and Norman Eizenberg 1,2 1 Gippsland Medical School, Monash University 2 Department of Anatomy and Developmental Biology, Monash University, Clayton michelle.machado@monash.edu. © 2018, Springer Nature Limited.
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    Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland
    (Pergamon-Elsevier Science Ltd, 2017) Deo, Ravinesh C.; Sahin, Mehmet
    Forecasting solar radiation (G) is extremely crucial for engineering applications (e.g. design of solar furnaces and energy-efficient buildings, solar concentrators, photovoltaic-systems and a site-selection of sites for future power plants). To establish long-term sustainability of solar energy, energy practitioners utilize versatile predictive models of G as an indispensable decision-making tool. Notwithstanding this, sparsity of solar sites, instrument maintenance, policy and fiscal issues constraint the availability of model input data that must be used for forecasting the onsite value of G. To surmount these challenge, low-cost, readily-available satellite products accessible over large spatial domains can provide viable alternatives. In this paper, the preciseness of artificial neural network (ANN) for predictive modelling of G is evaluated for regional Queensland, which employed Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature(LST) as an effective predictor. To couple an ANN model with satellite-derived variable, the LST data over 2012-2014 are acquired in seven groups, with three sites per group where the data for first two (2012-2013) are utilised for model development and the third (2014) group for cross-validation. For monthly horizon, the ANN model is optimized by trialing 55 neuronal architectures, while for seasonal forecasting, nine neuronal architectures are trailed with time-lagged LST. ANN coupled with zero lagged LST utilised scaled conjugate gradient algorithm, and while ANN with time-lagged LST utilised Levenberg-Marquardt algorithm. To ascertain conclusive results, the objective model is evaluated via multiple linear regression (MLR) and autoregressive integrated moving average (ARIMA) algorithms. Results showed that an ANN model outperformed MLR and ARIMA models where an analysis yielded 39% of cumulative errors in smallest magnitude bracket, whereas MLR and ARIMA produced 15% and 25%. Superiority of an ANN model was demonstrated by site-averaged (monthly) relative error of 5.85% compared with 10.23% (MLR) and 9.60 (ARIMA) with Willmott's Index of 0.954 (ANN), 0.899 (MLR) and 0.848 (ARIMA). This work ascertains that an ANN model coupled with satellite-derived LST data can be adopted as a qualified stratagem for the proliferation of solar energy applications in locations that have an appropriate satellite footprint.
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    Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach
    (Pergamon-Elsevier Science Ltd, 2019) Deo, Ravinesh C.; Sahin, Mehmet; Adamowski, Jan F.; Mi, Jianchun
    Global advocacy to mitigate climate change impacts on pristine environments, wildlife, ecology, and health has led scientists to design technologies that harness solar energy with remotely sensed, freely available data. This paper presents a study that designed a regionally adaptable and predictively efficient extreme learning machine (ELM) model to forecast long-term incident solar radiation (ISR) over Australia. The relevant satellite-based input data extracted from the Moderate Resolution Imaging Spectroradiometer (i.e., normalized vegetation index, land-surface temperature, cloud top pressure, cloud top temperature, cloud effective emissivity, cloud height, ozone and near infrared-clear water vapour), enriched by geo-temporal input variables (i.e., periodicity, latitude, longitude and elevation) are applied for a total of 41 study sites distributed approximately uniformly and paired with ground-based ISR (target). Of the 41 sites, 26 are incorporated in an ELM algorithm for the design of a universal model, and the remainder are used for model cross-validation. A universally-trained ELM (with training data as a global input matrix) is constructed, and the spatially-deployable model is applied at 15 test sites. The optimal ELM model is attained by trial and error to optimize the hidden layer activation functions for feature extraction and is benchmarked with competitive artificial intelligence algorithms: random forest (RF), M5 Tree, and multivariate adaptive regression spline (MARS). Statistical metrics show that the universally-trained ELM model has very good accuracy and outperforms RF, M5 Tree, and MARS models. With a distinct geographic signature, the ELM model registers a Legates & McCabe's Index of 0.555-0.896 vs. 0.411-0.858 (RF), 0.434-0.811 (M5 Tree), and 0.113-0.868 (MARS). The relative root-mean-square (RMS) error of ELM is low, ranging from approximately 3.715-7.191% vs. 4.907-10.784% (RF), 7.111-11.169% (M5 Tree) and 4.591-18.344% (MARS). Taylor diagrams that illustrate model preciseness in terms of RMS centred difference, error analysis, and boxplots of forecasted vs. observed ISR also confirmed the versatility of the ELM in generating forecasts over heterogeneous, remote spatial sites. This study ascertains that the proposed methodology has practical implications for regional energy modelling, particularly at national scales by utilizing remotely-sensed satellite data, and thus, may be useful for energy feasibility studies at future solar-powered sites. The approach is also important for renewable energy exploration in data-sparse or remote regions with no established measurement infrastructure but with a rich and viable satellite footprint.