Multilayer Artificial Neural Network Research Articles

Research on satellite structural health monitoring based on ultrashort femtosecond grating array and artificial neural network

The safety of spacecraft and satellite in orbit is very important, and structural health monitoring is needed. At present, the existing technology is limited by load and difficult to realize. In this paper, we propose a feasible method to detect and locate the damage of satellite by combining ultrashort femtosecond grating array inscribed on oxide-doped fiber with multilayer artificial neural network. An oxide-doped fiber with high robustness is designed, and ultrashort grating arrays are fabricated on the fiber by femtosecond laser point-by-point writing technology. The effects of impactor velocity and angle on impact response was investigated by numerical simulations and physical experiments. Subsequently, repeated impact experiments were conducted on the satellite to obtain the training dataset and testing dataset for two-dimensional convolutional neural network. The network with symmetric convention kernels has an 88.12% localization accuracy and a better performance in boundary region, and the network architecture with asymmetric convention kernels has a 90.31% accuracy and a better performance in middle region.

457 Prediction of methane emissions using rumination time and milk mid-infrared spectral data via artificial neural networks

Abstract Cattle methane emissions (ME) account for approximately 6% of global anthropogenic greenhouse gas emissions. Given the challenges in measuring ME directly from individual animals, there is a need for the development of novel indirect methods. Rumination time (RT) and milk mid-infrared spectral data (MIR) show promise for the indirect assessment of ME in dairy cows. Both traits have been used as indicators of reproduction, production, and gas emission traits. Methodologies combining the use of MIR and machine learning algorithms such as artificial neural networks (ANN) for the prediction of ME have been successful; however, the inclusion of RT has not been assessed. This study aimed to evaluate the impact of RT on milk MIR-based models using ANN for the prediction of ME. One-week averages for RT, ME, and MIR from first-lactation Canadian Holstein cows (n = 412) were calculated. Six data sets were evaluated using a multilayer perceptron ANN. All sets included age at calving, season of calving and days in milk as model factors, but varied in using milk MIR data points (1,060 or 235) and including or not including RT. The ANN architecture consisted of one input layer, one hidden layer with one or more neurons, and one output layer. Results showed that sets using both RT and milk MIR data achieved correlations from 0.5 to 0.6 between predicted and observed ME. Notably, the inclusion of RT did not improve the performance of the models. Predictions may be improved through the use of larger data sets, the use of daily records, and inclusion of data across herds and lactations. Optimizing parameters of the ANN could also improve predictions. Further research is needed to fully assess the potential of RT as a predictor of ME in dairy cows.

Determination of electron screening potential of 6Li(p, [formula omitted])3He reaction using Multi Layer Perceptron based neural network

Understanding nuclear reactions between light-charged nuclei in the sub-Coulomb energy region is crucial for several astrophysical processes. Accurate determination of the reaction cross-section within the astrophysically important Gamow range is challenging due to electron screening. Various methods, including polynomial fits, R-Matrix, and the indirect Trojan Horse Method (THM), have estimated electron screening energies that exceed the adiabatic limit. This study aims to derive the bare astrophysical S-factor for the reaction 6Li(p,α)3He and to extract electron screening energies using Multi-Layer Perceptron-based Artificial Neural Network (ANN) analysis. Experimental S-factors for 6Li(p,α)3He, obtained from the literature, are reanalyzed with the ANN algorithm to determine the energy-dependent S-factor. The bare astrophysical S-factor is calculated from data above 60 keV, where electron screening is negligible. The electron screening potential is then derived by comparing the shielded S-factor with the bare S-factor. The ANN-based analysis yields an electron screening potential of 220 eV, suggesting that ANN could be a viable tool for estimating electron screening potentials in light nuclei reactions.

Supercapacitor properties of partially oxidised-MXene quantum dots/graphene hybrids: Fabrication of flexible/wearable micro-supercapacitor devices

Recently, combining the synergistic properties of two-dimensional (2D) and zero-dimensional (0D) materials has attracted significant attention for energy applications. Here, we report the synthesis and characterization of partially oxidised-MXene quantum dots (PO-MXQDs) and reduced graphene oxide composite (PO-MXQDs/rGO) and investigate the resulting composites for energy storage applications towards supercapacitors. Density functional theory (DFT) simulations are used to examine the electrical and structural features of PO-MXQDs/rGO by predicting the chemical interaction involving charge transfer from PO-MXQDs to rGO. The multilayer Artificial Neural Network (ANN) model with hyperparameter tuning was developed at different input parameters to optimize material compositions, concentrations, and types of electrolytes to obtain the suitable conditions for best supercapacitive performance. The optimized PO-MXQDs/rGO showed a specific capacitance value of 1137.8 F.g−1 and was utilized for the fabrication of flexible micro-supercapacitors (FMSCs) device through the mask-assisted vacuum filtration process and asymmetric coin-cell type supercapacitor devices (ACCDs). The FMSCs deliver a higher areal capacitance (5.26 mF.cm−2), high areal energy density (0.46 μWh.cm−2), high areal power density (210.48 μW.cm−2), and the FMSC device has ultra-high cyclic stability up to 30000 cyclic voltammetry (CV) cycles. This method provides an in-depth study on developing 0D-2D hybrid materials with the wisdom of machine learning (ML) and DFT toward energy storage applications.

Artificial Neural Network–based Prediction Model to Minimize Dust Emission in the Machining Process

BackgroundDust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. MethodsThe multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. ResultsThe results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. ConclusionThis study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

New strategy to optimize in-situ fenton oxidation for TPH contaminated soil remediation via artificial neural network approach

In-situ remediation of total petroleum hydrocarbon (TPH) contaminated soils via Fenton oxidation is a promising approach. However, determining the proper injection amount of H2O2 and Fe source over the Fenton reaction in the complex geological conditions for in-situ TPH soil remediation remains a daunting challenge. Herein, we introduced a practical and novel approach using soft computational models, a multilayer perception artificial neural network (MPLNN), for predicting the TPH removal performance. In this study, we conducted 48 sets of TPH removal experiments using Fenton oxidation to determine the TPH removal performance of a wide range of different ground conditions and generated 336 data points. As a result, a negative Pearson correlation coefficient was obtained in the Fe injection mass and the natural presence of Fe mineral in the soil, indicating that the excess of Fe could significantly retarded the TPH removal performance in the Fenton reaction. In addition, the MPLNN model with 6-6-1 training using Scaled conjugate gradient backpropagation (SCG) with tangent sigmoid as the transfer function demonstrated a high accuracy for TPH removal prediction with the correlation determination of 0.974 and mean square error value of 0.0259. The optimized MPLNN model achieved less than 20% error for predicting TPH removal performance in actual TPH-contaminated soil via Fenton oxidation. Hence, the proposed MPLNN can be useful in improving the Fenton oxidation of TPH removal performance in-situ soil remediation.

Risk Management in Using Artificial Neural Networks

The article examines risks faced by banks during their lending processes and the mechanisms for managing these risks, utilizing modern statistical methods. Specifically, the study focused on the artificial neural network model as a technique of artificial intelligence that has successfully applied various classifications and discrimination tasks among institutions. A random sample of 46 institutions obtained loans from the branches of the National Bank of Algeria (BNA), Local Development Bank (BDL), Popular Credit of Algeria (CPA), and Agricultural and Rural Development Bank (BADR) in El Bayadh province, Algeria. Each of these institutions was characterized by 14 measurable variables with numerical values derived from the financial statements (balance sheets and income statements), as well as 3 qualitative non-accounting variables extracted from the loan applicants’ files (age of the institution, sector of activity (services/productive), institution status (viable/struggling). The sample of these 46 institutions was initially divided into two groups: 64% comprised financially stable institutions, and the other 36% were struggling institutions. The research checks whether the risk assessment of each of these 46 institutions using artificial neural networks will identify their institution status (viable/struggling) in the same way as it was in the base sample. The training phase recorded a prediction error rate of 0%, and the network testing phase misclassification rate was 5.6%. The overall correct classification rate for the multilayer artificial neural network was 92.9%, with a total error rate of 7.1%. The contribution rate of the non-accounting variable “sector of activity” was 100%, and the variable “age of the institution” was 94.4%. Other variables had minor percentages, underscoring the importance of qualitative variables in the classification process. Thus, the study proved that artificial neural network model is an effective model for distinguishing between viable and struggling institutions, significantly contributing to banking risk management.

A Radiomics Model for the Differentiation of Intracranial Solitary Fibrous Tumor/Hemangiopericytoma and Meningioma Based on Multiparametric Magnetic Resonance Imaging.

Although the imaging findings of intracranial solitary fibrous tumor (SFT)/hemangiopericytoma (HPC) and meningioma are similar, their treatment and prognosis are quite different. Accurate preoperative identification of these two types of tumors is crucial for individualized treatment. The aim of this study was to develop a radiomics model for the differentiation of intracranial SFT/HPC and meningioma based on multiparametric magnetic resonance imaging (mpMRI). A total of 99 patients from July 2012 to July 2018 with histologically and immunohistochemically confirmed SFT/HPC (n = 40) or meningiomas (n = 59) were retrospectively analyzed. A total of 1118 features were extracted based on its image shape, intensity and texture features. The logistic regression (LR) and multi-layer artificial neural network (ANN) classifiers were used to classify SFT/HPC and meningioma. The predictive performance was calculated using receiver operating characteristic curves (ROC). We found significant difference in terms of sex between the SFT/HPC and meningioma group (χ2= 4.829, P < 0.05), but no significant difference was found in age (P > 0.05). The most significant radiomics features included five shape and four first-order level features. For the LR classifier, the prediction accuracy of SFT/HPC was 71.0% and meningioma was 78.7%. For the ANN classifier, the prediction accuracy of SFT/HPC was 83.9% and meningioma was 80.9%. Both of the two classifiers achieved a high accuracy rate, but ANN was better. Radiomics features, especially when combined with an ANN classifier, can provide satisfactory performance in distinguishing SFT/HPC and meningioma.

Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.

Investigating the Impact of Data Normalization Methods on Predicting Electricity Consumption in a Building Using different Artificial Neural Network Models.

The study investigates the impact of data normalization on the prediction of electricity consumption in buildings using four multilayer Artificial Neural Networks (ANN) algorithms: Long Short-Term Memory Networks (LSTM), Levenberg-Marquardt Back-propagation (LMBP), Recurrent Neural Networks (RNN), and General Regression Neural Network (GRNN). Four data normalization approaches, Min-Max Scaling, Mean, Z-score, and Gaussian function were assessed on experimental datasets. The LSTM algorithm, when combined with Min-Max normalization, showed the most favorable predictive capabilities, with a low Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 10.3 and Normalized Mean Bias Error (NMBE) of 0.6. The remaining three normalization approaches showed satisfactory concordance with empirical data, but with slight disparities in precision. The LMBP model, when using Z-score normalization, had favorable performance in forecasting electricity consumption, but the discrepancies across the models were not significant. The Recurrent Neural Network (RNN) model, when used with Gaussian normalization, exhibited the most favorable performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 11.8 and Normalized Mean Biased Error (NMBE) at 0.6. The Generalized Regression Neural Network (GRNN) model, trained on unprocessed data, exhibited superior performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 19.2 and NMBE at 1.0. In conclusion, the study highlights the significant influence of data normalization on the predictive capabilities of various ANN models, suggesting that careful use of data normalization techniques can significantly improve the accuracy of electricity consumption forecasting in buildings.

A hybrid approach to remote sensing image classification based on pixel density distribution and adaptive neural networks

&lt;span lang="EN-US"&gt;The proposed study employed a method for identifying the main contents (category/class) that a Remote Sensing Image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward Artificial Neural Network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing.&lt;/span&gt;

Aerodynamic optimization method of intake grille of active clearance control system for turbines based on modified social spider algorithm (MSSA)

During the operation of an active clearance control (ACC) system of a turbine, the aerodynamic performance of the intake grille indirectly influences its control. To improve the performance, an aerodynamic optimization method is proposed, consisting of parameterization, an optimization algorithm, and a fitness evaluation. During parameterization, its geometry is represented by seven geometric variables. A modified social spider algorithm is used as the optimization algorithm. To evaluate the aerodynamic performance of the grille, a special fitness function is adopted, obtained using an adaptive topological multi-layer feedforward artificial neural network. To verify the feasibility of this method, experiments and numerical calculations are carried out on the original and optimized intake grilles. The results show that the average intake flow rate and average total pressure recovery coefficient of the optimized grille have increased by 17.3% and 4.9%, respectively.

Machine Learning-powered 28-day Mortality Prediction Model for Hospitalized Patients with Acute Decompensation of Liver Cirrhosis.

Chronic liver disease and cirrhosis are persistent global health threats, ranking among the top causes of death. Despite medical advancements, their mortality rates have remained stagnant for decades. Existing scoring systems such as Child-Turcotte-Pugh and Mayo End-Stage Liver Disease have limitations, prompting the exploration of more accurate predictive methods using artificial intelligence and machine learning (ML). We retrospectively reviewed the data of all adult patients with acute decompensated liver cirrhosis admitted to a tertiary hospital during 2015-2021. The dataset underwent preprocessing to handle missing values and standardize continuous features. Traditional ML and deep learning algorithms were applied to build a 28-day mortality prediction model. The subjects were 173 cirrhosis patients, whose medical records were examined. We developed and evaluated multiple models for 28-day mortality prediction. Among traditional ML algorithms, logistic regression outperformed was achieving an accuracy of 82.9%, precision of 55.6%, recall of 71.4%, and an F1-score of 0.625. Naive Bayes and Random Forest models also performed well, both achieving the same accuracy (82.9%) and precision (54.5%). The deep learning models (multilayer artificial neural network, recurrent neural network, and Long Short-Term Memory) exhibited mixed results, with the multilayer artificial neural network achieving an accuracy of 74.3% but lower precision and recall. The feature importance analysis identified key predictability contributors, including admission in the intensive care unit (importance: 0.112), use of mechanical ventilation (importance: 0.095), and mean arterial pressure (importance: 0.073). Our study demonstrates the potential of ML in predicting 28-day mortality following hospitalization with acute decompensation of liver cirrhosis. Logistic Regression, Naive Bayes, and Random Forest models proved effective, while deep learning models exhibited variable performance. These models can serve as useful tools for risk stratification and timely intervention. Implementing these models in clinical practice has the potential to improve patient outcomes and resource allocation.

Inferential control strategies using neural soft sensor in a high purity distillation column

High-purity distillation columns are processes in which you want to minimally increase the purity of a key component by separation. These processes are sensitive to disturbances, where small changes in the feed flowrate stream cause drastic changes in product compositions. Furthermore, when one wants to apply traditional control and optimization techniques to these processes, some difficulties have to be faced: the process is generally non-linear and has a long response time, there are many immeasurable disturbances, it is difficult to keep the process in steady-state, and the bottom and top compositions are highly coupled. Therefore, this paper presents a methodology for the development of soft sensors (SS), here in applied to an industrial 1,2-Dichloroethane separation plant. The process was simulated and validated with real data from the industrial plant. In the step second an algorithm based on multivariate statistics was developed for the selection of inputs SS variables. The configuration for training multilayer artificial neural network (ANN's) was optimize, accounting for the ANN's prediction capacity of responses and the rejection of disturbances inserted in the process. Thereafter, a temperature control was implemented to maintain the impurity compositions within specifications but failed due to the small temperature variations in the high purity column. To overcome these difficulty three inferential control strategies (impurity composition estimated by the SS) were implemented: a classic feedback control, a cascade control, and a ratio-cascade control. All control strategies acted to minimize the process disturbances effects. However, the inferential ratio-cascade control shown to be the most robust, because of the lowest integral error criteria value and percentage overshoot.

Artificial Intelligence and Microbiology

The concept of Artificial Intelligence (AI) is increasingly important in the healthcare sector today. Components of AI such as machine learning and deep learning are being utilized in various applications within the field of microbiology. This study examines the uses of AI in microbiology and its role in healthcare applications. Machine learning enables computer systems to analyze data using algorithms that mimic human intelligence, while deep learning processes information through multi-layered artificial neural networks. These technologies are used in many areas such as microbiological diagnosis, drug discovery, infection control, and patient monitoring. For instance, AI-supported systems are used in microbiological diagnosis to shorten diagnosis times and increase accuracy compared to traditional methods. Additionally, smart systems developed for preventing hospital-acquired infections alert hospital staff, thus reducing the risk of infection. AI also plays a significant role in the diagnosis of microorganisms such as viruses and fungi. Especially, AI-supported image analysis methods are utilized for rapid and accurate diagnosis. However, there are some challenges in the use of AI. Issues related to data privacy and ethics are among the factors limiting the applications of AI in microbiology and healthcare. Furthermore, the cost and complexity of algorithm implementation pose additional challenges. By discussing the applications of AI in microbiology and its potential in the future, this study sheds light on innovative developments in the healthcare sector.

Comparative Analysis of the Predictive Performance of an ANN and Logistic Regression for the Acceptability of Eco-Mobility Using the Belgrade Data Set

To solve the problem of environmental pollution caused by road traffic, alternatives to vehicles with internal combustion engines are often proposed. As such, eco-mobility microvehicles have significant potential in the fight against environmental pollution, but only on the condition that they are widely accepted and that they replace the vehicles that predominantly pollute the environment. With this in mind, this study aims to elucidate the main variables that influence the acceptability of these vehicles, using prediction models based on binary logistic regression and a multilayer artificial neural network—a multilayer perceptron (ANN). The data of a random sample obtained via an online questionnaire, answered by 503 inhabitants of Belgrade (Serbia), were used for training and testing the model. A multilayer perceptron with 9 and 7 neurons in two hidden layers, a hyperbolic tangent activation function in the hidden layer, and an identity function in the output layer performed slightly better than the binary logistic regression model. With an accuracy of 85%, a precision of 79%, a recall of 81%, and an area under the ROC curve of 0.9, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that a respondent’s relationship to their current environmental pollution, the frequency of their use of modes of transport such as bicycles and motorcycles, their mileage for commuting, and their personal income have the greatest influence on the acceptability of using eco-mobility vehicles.

Highly Flexible and Superelastic Graphene Nanofibrous Aerogels for Intelligent Sign Language.

Highly flexible and superelastic aerogels at large deformation have become urgent mechanical demands in practical uses, but both properties are usually exclusive. Here a trans-scale porosity design is proposed in graphene nanofibrous aerogels (GNFAs) to break the trade-off between high flexibility and superelasticity. The resulting GNFAs can completely recover after 1000 fatigue cycles at 60% folding strain, and notably maintain excellent structural integrity after 10000 cycles at 90% compressive strain, outperforming most of the reported aerogels. The mechanical robustness is demonstrated to be derived from the trans-scale porous structure, which is composed of hyperbolic micropores and porous nanofibers to enable the large elastic deformation capability. It is further revealed that flexible and superelastic GNFAs exhibit high sensitivity and ultrastability as an electrical sensors to detect tension and flexion deformation. As proof, The GNFA sensor is implemented onto a human finger and achieves the intelligent recognition of sign language with high accuracy by multi-layer artificial neural network. This study proposes a highly flexible and elastic graphene aerogel for wearable human-machine interfaces in sensor technology.

Estimation of the Regression Model Using M-Estimation Method and Artificial Neural Networks in the Presence of Outliers

This study aimed to predict using regression models in the presence of outliers in the study data. The research delved into outliers, their detection, and model estimation through robust methods, represented by the M estimator and multilayer artificial neural networks. A comparison between these methods was conducted, and they were applied to real data representing a survey of private-sector power generators for 2021. Model evaluation was performed using Mean Squared Error (MSE) and the determination coefficient. The results indicated that the M-estimator with the Huber function outperformed its counterpart with the Tukey function. The best-performing architecture for the artificial neural network was ML-FF (5, 16, 32, 46, 128, 1) with the relu activation function. This network effectively handled extreme values and exhibited strong predictive capabilities. The choice of activation function and the number of hidden layers significantly impacted the neural network's performance, with the results showing the superiority of this artificial neural network over the robust estimators. Paper type Research paper

Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained.

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.