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Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological dataView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000690</doi_batch_id><timestamp>1713603036</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 1</title></titles><publication_date><month>03</month><year>2024</year></publication_date><journal_volume><volume>13</volume></journal_volume><issue>1</issue></journal_issue><journal_article><titles><title>Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>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.</p></abstract><publication_date><month>04</month><day>30</day><year>2013</year></publication_date><pages><first_page>18</first_page><last_page>27</last_page></pages><publisher_item><identifier id_type="pii">758</identifier></publisher_item><doi_data><doi>10.30955/gnj.000758</doi><timestamp>1713603036</timestamp><resource>https://journal.gnest.org/publication/758</resource></doi_data></journal_article></journal></body></doi_batch> </div>
