scholarly journals Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks

2017 ◽  
Vol 14 (2) ◽  
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Narx Models ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive ◽  
Yearly Data

In this study, three Artificial Neural Network (ANN) models (Feedforward network, Elman, and Nonlinear Autoregressive Exogenous (NARX)) were used to predict hourly solar radiation in Amman, Jordan. The three models were constructed and tested by using MATLAB software. Meteorological data for the years from 2000 to 2010 were used to train the ANN while the yearly data of 2011 was used to test it. It was found that ANN technique may be used to estimate the hourly solar radiation with an excellent accuracy, and the coefficient of determination of Elman, feedforward and NARX models were found to be 0.97353, 0.97376, and 0.99017, respectively. The obtained results showed that NARX model has the best ability to predict the required solar data, while Elman and feedforward models have the lowest ability to predict it.

scholarly journals Using the artificial neural networks for prediction and validating solar radiation

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Zahraa E. Mohamed
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Horizontal Surface ◽  
The Other ◽  
Rate Coefficients ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Statistical Indicators ◽  
Better Than

AbstractThe main objective of this paper is to employ the artificial neural network (ANN) models for validating and predicting global solar radiation (GSR) on a horizontal surface of three Egyptian cities. The feedforward backpropagation ANNs are utilized based on two algorithms which are the basic backpropagation (Bp) and the Bp with momentum and learning rate coefficients respectively. The statistical indicators are used to investigate the performance of ANN models. According to these indicators, the results of the second algorithm are better than the other. Also, model (6) in this method has the lowest RMSE values for all cities in this study. The study indicated that the second method is the most suitable for predicting GSR on a horizontal surface of all cities in this work. Moreover, ANN-based model is an efficient method which has higher precision.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

2009 ◽  
Vol 46 (8) ◽  
pp. 955-968 ◽  
Author(s):  
Yusuf Erzin ◽  
S. D. Gumaste ◽  
A. K. Gupta ◽  
D. N. Singh
Keyword(s):  
Hydraulic Conductivity ◽  
Absolute Error ◽  
Silty Sand ◽  
Coefficient Of Determination ◽  
Performance Indices ◽  
Fine Grained ◽  
Centrifuge Tests ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-à-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.

Estimating bearing capacity of shallow foundations by artificial neural networks

2017 ◽  
Vol 3 (4) ◽  
pp. 151
Author(s):  
Mustafa Aytekin
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Large Set ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Capacity Data

In this study, the Artificial Neural Network, ANN is applied to data extracted from a large set of random data created by using Terzaghi and Meyerhof formulae. By using MS Excel, 3750 sets of data for Terzaghi's equation, 4000 for Meyerhof's equation were generated. A simulated ANN was trained on a subset of bearing capacity data, and the performance was tested on the remaining data. The performances of the ANN models were compared to Terzaghi and Meyerhof results. ANN models were as accurate as the other techniques in estimating the ultimate bearing capacity. The models estimated the ultimate bearing capacity with an average error of around 1% of the value obtained from Terzaghi and Meyerhof equations, and the coefficient of determination (r2) was almost equal to 1. Their sensitivity and specificity is dependent on the function and the algorithm used in the training process. Validation subset is crucial in preventing the over-fitting of the ANN models to the training data. ANN models are potentially useful technique for estimating the bearing capacity of the soil. Large training data sets are needed to improve the performance of data-derived algorithms, in particular ANN models.

scholarly journals Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data

2013 ◽  
Vol 13 (1) ◽  
pp. 18-27
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Learning Rule ◽  
Food And Agriculture ◽  
Air Temperatures ◽  
Food And Agriculture Organization ◽  
Cascade Correlation ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detailed meteorological data required for the equation of FAO-56 Penman-Monteith (P-M) method that was adopted by Food and Agriculture Organization (FAO) as a standard method in estimating reference evapotranspiration (ETo) are not often available, especially in developing countries. The Hargreaves equation (HG) has been successfully used in some locations to estimate ETo where sufficient data were not available to use the P-M method. This paper investigates the potential of two Artificial Neural Network (ANN) architectures, the multilayer perceptron architecture, in which a backpropagation algorithm (BPANN) is used, and the cascade correlation architecture (CCANN), in which Kalman’s learning rule is embedded in modeling the daily ETo with minimal meteorological data. An overview of the features of ANNs and traditional methods such as P-M and HG is presented, and the advantages and limitations of each method are discussed. Daily meteorological data from three automatic weather stations located in Greece were used to optimize and test the different models. The exponent value of the HG equation was locally optimized, and an adjusted HGadj equation was used. The comparisons were based on error statistical techniques using P-M daily ETo values as reference. According to the results obtained, it was found that taking into account only the mean, maximum and minimum air temperatures, the selected ANN models markedly improved the daily ETo estimates and provided unbiased predictions and systematically better accuracy compared with the HGadj equation. The results also show that the CCANN model performed better than the BPANN model at all stations.

Simultaneous UV-Vis spectrophotometric quantification of ternary basic dye mixtures by partial least squares and artificial neural networks

2016 ◽  
Vol 74 (10) ◽  
pp. 2497-2504 ◽  
Author(s):  
Seyed Karim Hassaninejad-Darzi ◽  
Mohammad Torkamanzadeh
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Prediction Models ◽  
Correlation Coefficients ◽  
Analyte Signal ◽  
Ann Models ◽  
Artificial Neural ◽  
Seawater Samples ◽  
Artificial Neural Network Ann

One of the main difficulties in quantification of dyes in industrial wastewaters is the fact that dyes are usually in complex mixtures rather than being pure. Here we report the development of two rapid and powerful methods, partial least squares (PLS-1) and artificial neural network (ANN), for spectral resolution of a highly overlapping ternary dye system in the presence of interferences. To this end, Crystal Violet (CV), Malachite Green (MG) and Methylene Blue (MB) were selected as three model dyes whose UV-Vis absorption spectra highly overlap each other. After calibration, both prediction models were validated through testing with an independent spectra-concentration dataset, in which high correlation coefficients (R2) of 0.998, 0.999 and 0.999 were obtained by PLS-1 and 0.997, 0.999 and 0.999 were obtained by ANN for CV, MG and MB, respectively. Having shown a relative error of prediction of less than 3% for all the dyes tested, both PLS-1 and ANN models were found to be highly accurate in simultaneous determination of dyes in pure aqueous samples. Using net-analyte signal concept, the quantitative determination of dyes spiked in seawater samples was carried out successfully by PLS-1 with satisfactory recoveries (90–101%).

Estimation of Global Solar Radiation Using Artificial Neural Networks in Abu Dhabi City, United Arab Emirates

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Maitha Al-Shamisi ◽  
Ali Assi ◽  
Hassan Hejase
Keyword(s):  
Solar Radiation ◽  
United Arab Emirates ◽  
Meteorological Data ◽  
Geographical Location ◽  
Global Solar Radiation ◽  
Abu Dhabi ◽  
Accurate Estimation ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The geographical location (Latitude: 24 deg 28′ N and Longitude: 54 deg 22′ E) of Abu Dhabi city in the United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for the estimation of monthly mean global solar radiation (GSR) on a horizontal surface in Abu Dhabi. The ANN models are presented and implemented on a 16-yr measured meteorological data set for Abu Dhabi comprising the maximum daily temperature, mean daily wind speed, mean daily sunshine hours, and mean daily relative humidity between 1993 and 2008. The meteorological data between 1993 and 2003 are used for training the ANN and data between 2004 and 2008 are used for testing the estimated values. Multilayer perceptron (MLP) and radial basis function (RBF) are used as ANN learning algorithms. The results attest to the capability of ANN techniques and their ability to produce accurate estimation models.

scholarly journals Application of the SHS-GC-FID method and HPLC-DAD method in the prediction of the shelf-life of gluten-free cookies

2017 ◽  
Vol 82 (12) ◽  
pp. 1343-1355
Author(s):  
Marijana Sakac ◽  
Lato Pezo ◽  
Pavle Jovanov ◽  
Natasa Nedeljkovic ◽  
Anamarija Mandic ◽  
...  
Keyword(s):  
Shelf Life ◽  
Hplc Method ◽  
Coefficient Of Determination ◽  
Relevant Parameter ◽  
Gluten Free ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Gas Chromatographic Method

The aim of this study was to compare the sensitivity of two analytical methods for the prediction of the shelf-life of unpacked and packed gluten-free rice?buckwheat cookies kept at ambient (23?1?C) and elevated (40?1?C) temperature during storage, namely the static headspace gas chromatographic method with flame ionisation detection (SHS-GC-FID) for volatile saturated aldehydes (propanal (C3), pentanal (C5), hexanal (C6), heptanal (C7) and octanal (C8)) and the HPLC method for malondialdehyde (MDA) determination. Both methods resulted in obtaining the same end-points of cookie shelf-life, i.e., 3 and 5 months for unpacked and packed cookies kept at elevated temperature, respectively, and 11 and 14 months for unpacked and packed cookies kept at ambient temperature, respectively. Two computational approaches, i.e., the second order polynomial (SOP) and artificial neural network (ANN) models, were used accordingly. The calculations of the contents of aldehydes and MDA could be predicted with an overall coefficient of determination of 0.722 using the ANN model compared to 0.312?0.773 for SOP models. According to sensitivity analysis, it might be suggested that the relevant parameter for the prediction of the end-point of cookie shelf-life is the MDA rather than the C3, C5, C6, C7 and C8 content.

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