Environmental Impacts and Sustainability
Mohammad Hossein Jahangir; Mahnaz Abolghasemi; Seyedeh Mahsa Mousavi Reineh
Abstract
Drought is considered as a destructive disaster that can have irreversible effects on different aspects of life. In this study, artificial neural network was used as a powerful means of modeling nonlinear and indefinite processes in order to simulate drought intensities at 7 synoptic stations of Khorasan ...
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Drought is considered as a destructive disaster that can have irreversible effects on different aspects of life. In this study, artificial neural network was used as a powerful means of modeling nonlinear and indefinite processes in order to simulate drought intensities at 7 synoptic stations of Khorasan Razavi from more than 35 years ago up to the year 2014. Input data were the calculations of the two indicators of PNPI and SPI by DIC software, and the output layer (drought intensity) was taken to the Matlab software and employed as the teaching data (from 25 years), experiment (from 5 years), and validation (from another 5 years). The 3-9-1 structure of the network of layers had the maximum accuracy with the error rate of less than 2 % and high correlation (more than 90 %). After trial and error for each station through sigmoid stimulation function in the Perceptron network, it was observed that the stations of Mashhad and Quchan had the minimum error and the maximum error was related to the station of Neyshabur. The results of comparisons and observations showed that the artificial neural network had high efficiency in simulation of the data. The obtained correlation amount of 0.999 for the base station represented the small error of the model in prediction. Drought forecasting was performed in this study by the trained algorithm in the artificial neural network without using the observation data. The results showed that rainfall, temperature, and speed models had a positive role in forecasting the provinces that would experience drought. Due to its lower amount of error, SPI indicator was selected for mapping, the findings of which showed that the highest drought intensity belonged to the near normal to normal wet lands.
Renewable Energy Resources and Technologies
Aondoyila Kuhe; Victor Terhemba Achirgbenda; Mascot Agada
Abstract
The optimum design of solar energy systems strongly depends on the accuracy of solar radiation data. However, the availability of accurate solar radiation data is undermined by the high cost of measuring equipment or non-functional ones. This study developed a feed-forward backpropagation artificial ...
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The optimum design of solar energy systems strongly depends on the accuracy of solar radiation data. However, the availability of accurate solar radiation data is undermined by the high cost of measuring equipment or non-functional ones. This study developed a feed-forward backpropagation artificial neural network model for prediction of global solar radiation in Makurdi, Nigeria (7.7322 N long. 8.5391 E) using MATLAB 2010a Neural Network toolbox. The training and testing data were obtained from the Nigeria metrological station (NIMET), Makurdi. Five meteorological input parameters including maximum and temperature, mean relative humidity, wind speed, and sunshine hour were used, while global solar radiation was used as the output of the network. During training, the root mean square error, correlation coefficient and mean absolute percentage error (%) were 0.80442, 0.9797, and 3.9588, respectively; for testing, a root mean square value, correlation coefficient, and mean absolute percentage error (%) were 0.98831, 0.9784, and 5.561, respectively. These parameters suggest high reliability of the model for the prediction of solar radiation in locations where solar radiation data are not available.
Saeed Edalati; Mehran Ameri; Masoud Iranmanesh
Abstract
In this study, an artificial neural network based model for prediction of solar energy potential in Kerman province in Iran has been developed. Meteorological data of 12 cities for period of 17 years (1997–2013) and solar radiation for five cities around and inside Kerman province from the Iranian ...
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In this study, an artificial neural network based model for prediction of solar energy potential in Kerman province in Iran has been developed. Meteorological data of 12 cities for period of 17 years (1997–2013) and solar radiation for five cities around and inside Kerman province from the Iranian Meteorological Office data center were used for the training and testing the network. Meteorological and geographical data were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the predictions and actual global solar radiation intensities for training and testing datasets were higher than 97%, suggesting a high reliability of the model for evaluating solar radiation in locations where solar radiation data are not available. The predicted solar radiation values are illustrated in the form of maps that were made by ArcGIS.
