Estimate of Global Solar Radiation by Using Artificial Neural Network in Qena, Upper Egypt

Ahmed, Emad A.; Adam, M. El-Nouby

doi:10.7763/jocet.2013.v1.35

Cited by 31 publications

(16 citation statements)

References 13 publications

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“…An ANN is a mathematical model that has been applied for the identification, modeling, optimization, forecasting, prediction and control of complex systems [26,27]. It can be trained for performing a particular task based on available empirical data [28].…”

Section: Methodsmentioning

confidence: 99%

Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach

Kadavi

Lee

2017

Applied Sciences

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Volcanic eruptions cause pyroclastic flows, which can destroy plantations and settlements. We used image data from Landsat 7 Bands 7, 4 and 2 and Landsat 8 Bands 7, 5 and 3 to observe and analyze the distribution of pyroclastic flow deposits for two volcanos, Mount Sinabung and Merapi, over a period of 10 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). The satellite data are used in conjunction with an artificial neural network method to produce maps of pyroclastic precipitation for Landsat 7 and 8, then we calculated the pyroclastic precipitation area using an artificial neural network method after dividing the images into four classes based on color. Red, green, blue and yellow were used to indicate pyroclastic deposits, vegetation and forest, water and cloud, and farmland, respectively. The area affected by a volcanic eruption was deduced from the neural network processing, including calculating the area of pyroclastic deposits. The main differences between the pyroclastic flow deposits of Mount Sinabung and Mount Merapi are: the sediment deposits of the pyroclastic flows of Mount Sinabung tend to widen, whereas those of Merapi elongated; the direction of pyroclastic flow differed; and the area affected by an eruption was greater for Mount Merapi than Mount Sinabung because the VEI (Volcanic Explosivity Index) during the last 10 years of Mount Merapi was larger than Mount Sinabung.

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Section: Methodsmentioning

confidence: 99%

Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach

Kadavi

Lee

2017

Applied Sciences

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“…The COMBILOG Datalogger (No. 1020, TH., and Friedrichs & Co.) was used to record hourly integral irradiance (irradiation integrated in one hour) values of UVB and G. Additional details of the instrument specifications and data-gathering procedures at SVU-MRS are available in previous studies such as those of El-Noubi (2006), Ahmed (2008), Adam (2012), El-Noubi (2012), Ahmed and Adam (2013), and .…”

Section: Measurementsmentioning

confidence: 99%

Comparative analysis of cloud effects on ultraviolet-B and broadband solar radiation: Dependence on cloud amount and solar zenith angle

Adam

Ahmed

2016

Atmospheric Research

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“…Temperature-based correlations have been applied for the estimation of global solar radiation in the absence of other meteorological parameters [31][32][33]. Artificial neural network (ANN) models are employed successfully for the estimation of global solar radiation [34,35].…”

Section: Introductionmentioning

confidence: 99%

Empirical model development for the estimation of clearness index usingmeteorological parameters

Alam¹,

Rehman²,

Rehman³

et al. 2019

Turk J Elec Eng & Comp Sci

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The clearness index is an indispensable parameter required for the design and analysis of solar energy systems. In the absence of measured values for a specific location, the clearness index can be estimated from other measured meteorological variables. In this study three meteorological parameters, sunshine hours, monthly mean values of the temperature difference ( ∆ T), and cloudiness, are used to develop empirical models for the estimation of clearness index. The empirical models are developed for five major cities in Pakistan (Karachi, Multan, Lahore, Islamabad, and Quetta). For empirical model development, long-term data (1991 to 2010) of monthly average clearness index, sunshine hours, average daily minimum and maximum temperatures, and cloudiness have been used. The accuracy of the models has been tested by statistical indicators that include mean percentage error (MPE), coefficient of determination (R 2 ), mean absolute relative error (MARE), mean bias error (MBE), and root mean square error (RMSE). The error analysis revealed that the proposed models are suitable for the estimation of the clearness index. It is also concluded that multiple regression models give better estimates of clearness index for all the stations (0.80 ≤ R 2 ≤ 0.86) compared to single parameter model and therefore are recommended. The study indicated that clear sky conditions prevail throughout the months at all the investigated sites (0.58 ≤ K T ≤ 0.68), which is a good indicator for solar energy utilization.The statistical indicators also suggest that multilinear regression model M-3 gives a better representation of the climate system and using three parameters reduces the uncertainties in the developed model.

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Estimate of Global Solar Radiation by Using Artificial Neural Network in Qena, Upper Egypt

Cited by 31 publications

References 13 publications

Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach

Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach

Comparative analysis of cloud effects on ultraviolet-B and broadband solar radiation: Dependence on cloud amount and solar zenith angle

Empirical model development for the estimation of clearness index usingmeteorological parameters

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