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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.
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]
Application of extreme learning machine for estimating solar radiation from satellite data
(Wiley-Blackwell, 2014) Sahin, Mehmet; Kaya, Yilmaz; Uyar, Murat; Yildirim, Selcuk
In this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell. Copyright (c) 2013 John Wiley & Sons, Ltd.
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.
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.
Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data
(Elsevier Sci Ltd, 2013) Sahin, Mehmet; Kaya, Yilmaz; Uyar, Murat
In this paper, the estimation capacities of MLR and ANN are investigated to estimate monthly-average daily SR over Turkey. The satellite data are used for 73 different locations over Turkey. Land surface temperature, altitude, latitude, longitude and month are offered as the input variables for modeling ANN and MLR to get SR. Estimations of SR are evaluated with the meteorological values by using the statistical bases. The obtained results indicated that the ANN model could achieve a satisfactory performance when compared to the MLR model. Moreover, it is understood that more accurate results in estimation of SR are obtained in the use of satellite data, rather than the use of meteorological station data. Finally, the built ANN model is used to estimate the yearly average of daily SR over Turkey. As a result, satellite-based SR map for Turkey is generated. (C) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
Comparison of modelling ANN and ELM to estimate solar radiation over Turkey using NOAA satellite data
(Taylor & Francis Ltd, 2013) Sahin, Mehmet
In this study, solar radiation (SR) is estimated at 61 locations with varying climatic conditions using the artificial neural network (ANN) and extreme learning machine (ELM). While the ANN and ELM methods are trained with data for the years 2002 and 2003, the accuracy of these methods was tested with data for 2004. The values for month, altitude, latitude, longitude, and land-surface temperature (LST) obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite are chosen as input in developing the ANN and ELM models. SR is found to be the output in modelling of the methods. Results are then compared with meteorological values by statistical methods. Using ANN, the determination coefficient (R-2), mean bias error (MBE), root mean square error (RMSE), and Willmott's index (WI) values were calculated as 0.943, -0.148 MJm(-2), 1.604 MJm(-2), and 0.996, respectively. While R-2 was 0.961, MBE, RMSE, and WI were found to be in the order 0.045 MJm(-2), 0.672 MJm(-2), and 0.997 by ELM. As can be understood from the statistics, ELM is clearly more successful than ANN in SR estimation.
Determination of land surface temperature using precipitable water based Split-Window and Artificial Neural Network in Turkey
(Elsevier Sci Ltd, 2014) Yildiz, B. Yigit; Sahin, Mehmet; Senkal, Ozan; Pestimalci, Vedat; Tepecik, Kadir
Land surface temperature (LST) calculation utilizing satellite thermal images is very difficult due to the great temporal variance of atmospheric water vapor in the atmosphere which strongly affects the thermal radiance incoming to satellite sensors. In this study, Split-Window (SW) and Radial Basis Function (RBF) methods were utilized for prediction of LST using precipitable water for Turkey. Coll 94 Split-Window algorithm was modified using regional precipitable water values estimated from upper-air Radiosond observations for the years 1990-2007 and Local Split-Window (LSW) algorithms were generated for the study area. Using local algorithms and Advanced Very High Resolution Radiometer (AVHRR) data, monthly mean daily sum LST values were calculated. In RBF method latitude, longitude, altitude, surface emissivity, sun shine duration and precipitable water values were used as input variables of the structure. Correlation coefficients between estimated and measured LST values were obtained as 99.23% (for RBF) and 94.48% (for LSW) at 00:00 UTC and 92.77% (for RBF) and 89.98% (for LSW) at 12:00 UTC. These meaningful statistical results suggest that RBF and LSW methods could be used for LST calculation. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.
ESTIMATING SOLAR RADIATION BY MACHINE LEARNING METHODS
(IEEE, 2015) Ertugrul, Edip; Sahin, Mehmet; Aggun, Fikri
Solar energy, which is clean and renewable energy source, is a popular subject. The estimation of solar radiation can be done instead of long term measurements. Therefore, the satellite and meteorological values of 53 different locations of Turkey were used for estimations of solar radiation. In this study a hybrid approach was proposed. The train dataset was reduced by employing two times similarity and the reduced dataset was utilized with support vector machine to predict global solar radiation. Additionally, the proposed method was validated by employing neural network, linear regression, k nearest neighbor, extreme learning machine, Gaussian process regression and kernel smooth regression. This study was showed that the machine learning methods can be used instead of long term measurement before investments.
Estimation of the vapour pressure deficit using NOAA-AVHRR data
(Taylor & Francis Ltd, 2013) Sahin, Mehmet; Yildiz, Bekir Yigit; Senkal, Ozan; Pestemalci, Vedat
In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7K, whereas the VPD values were retrieved with an RMSE of 6mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration.
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.
Forecasting of Air Temperature Based on Remote Sensing
(Gazi Univ, 2012) Sahin, Mehmet; Yildiz, Bekir Yigit; Senkal, Ozan; Pestemalci, Vedat
The aim of this research is to forecast air temperature based on remote sensing data. So, land surface temperature and air temperature values which were measured by Republic of Turkey Ministry of Forestry and Water Affairs (Turkish State Meteorological Service) during the period 1995-2001 at seven stations (Adana, Ankara, Balikesir, Dzmir, Samsun, Sanliurfa, Van) were compared. The monthly land surface temperature and air temperature were used to have correlation coefficients over Turkey. An empirical method was obtained from equation of correlation coefficients. Separately, Price algorithm was used for the estimation of land surface temperature values to get air temperatures. Then as statistical, air temperature values, belongs to meteorological data in Turkey (26-45 degrees E and 36-42 degrees N) throughout 2002, were evaluated. The research results showed that accuracy of estimation of the air temperature changes from 2.453 degrees K to 2.825 degrees K by root mean square error.
Modelling and Remote Sensing of Land Surface Temperature in Turkey
(Springer, 2012) Sahin, Mehmet; Yildiz, B. Yigit; Senkal, Ozan; Pestemalci, Vedat
This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26-45A degrees E and 36-42A degrees N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, EskiAYehir, A degrees stanbul, A degrees zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LSTANN) and Becker and Li temperature(LSTB-L) method values have been found as 0.077 K and 0.091 K (training stations), 0.045 K and 0.003 K (testing stations), respectively.
Modelling of air temperature using remote sensing and artificial neural network in Turkey
(Elsevier Sci Ltd, 2012) Sahin, Mehmet
The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg-Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey-Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991-1.254 K, respectively. (C) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
Precipitable water modelling using artificial neural network in Cukurova region
(Springer, 2012) Senkal, Ozan; Yildiz, B. Yigit; Sahin, Mehmet; Pestemalci, Vedat
Precipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Cukurova region, south of Turkey. We applied Levenberg-Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Cukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Cukurova region. Precipitable water was the output. Correlation coefficient (R-2) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water.
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.

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