Using of Learning Vector Quantization Network for Pan Evaporation Estimation

A modern technique is presented to study the evaporation process which is considered as an important component of the hydrological cycle. The Pan Evaporation depth is estimated depending upon four metrological factors viz. (temperature, relative humidity, sunshine, and wind speed). Unsupervised Arti...

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Main Authors: Kamel A. Abdulmuhsin, Iftekhar A. Al-Ani
Format: Article
Language:English
Published: Tikrit University 2009-06-01
Series:Tikrit Journal of Engineering Sciences
Online Access:https://tj-es.com/ojs/index.php/tjes/article/view/602
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author Kamel A. Abdulmuhsin
Iftekhar A. Al-Ani
author_facet Kamel A. Abdulmuhsin
Iftekhar A. Al-Ani
author_sort Kamel A. Abdulmuhsin
collection DOAJ
description A modern technique is presented to study the evaporation process which is considered as an important component of the hydrological cycle. The Pan Evaporation depth is estimated depending upon four metrological factors viz. (temperature, relative humidity, sunshine, and wind speed). Unsupervised Artificial Neural Network has been proposed to accomplish the study goal, specifically, a type called Linear Vector Quantitization, (LVQ). A step by step method is used to cope with difficulties that usually associated with computation procedures inherent in these kind of networks. Such systematic approach may close the gap between the hesitation of the user to make use of the capabilities of these type of neural networks and the relative complexity involving the computations procedures. The results reveal the possibility of using LVQ for of Pan Evaporation depth estimation where a good agreement has been noticed between the outputs of the proposed network and the observed values of the Pan Evaporation depth with a correlation coefficient of 0.986.
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spelling doaj.art-b02c1c8873b14ab3943b91b03824074a2023-07-12T12:55:52ZengTikrit UniversityTikrit Journal of Engineering Sciences1813-162X2312-75892009-06-0116210.25130/tjes.16.2.07Using of Learning Vector Quantization Network for Pan Evaporation EstimationKamel A. Abdulmuhsin0Iftekhar A. Al-Ani1Water Resources Eng. Dept., University of Mosul, IraqWater Resources Technical Institute, Mosul, IraqA modern technique is presented to study the evaporation process which is considered as an important component of the hydrological cycle. The Pan Evaporation depth is estimated depending upon four metrological factors viz. (temperature, relative humidity, sunshine, and wind speed). Unsupervised Artificial Neural Network has been proposed to accomplish the study goal, specifically, a type called Linear Vector Quantitization, (LVQ). A step by step method is used to cope with difficulties that usually associated with computation procedures inherent in these kind of networks. Such systematic approach may close the gap between the hesitation of the user to make use of the capabilities of these type of neural networks and the relative complexity involving the computations procedures. The results reveal the possibility of using LVQ for of Pan Evaporation depth estimation where a good agreement has been noticed between the outputs of the proposed network and the observed values of the Pan Evaporation depth with a correlation coefficient of 0.986. https://tj-es.com/ojs/index.php/tjes/article/view/602
spellingShingle Kamel A. Abdulmuhsin
Iftekhar A. Al-Ani
Using of Learning Vector Quantization Network for Pan Evaporation Estimation
Tikrit Journal of Engineering Sciences
title Using of Learning Vector Quantization Network for Pan Evaporation Estimation
title_full Using of Learning Vector Quantization Network for Pan Evaporation Estimation
title_fullStr Using of Learning Vector Quantization Network for Pan Evaporation Estimation
title_full_unstemmed Using of Learning Vector Quantization Network for Pan Evaporation Estimation
title_short Using of Learning Vector Quantization Network for Pan Evaporation Estimation
title_sort using of learning vector quantization network for pan evaporation estimation
url https://tj-es.com/ojs/index.php/tjes/article/view/602
work_keys_str_mv AT kamelaabdulmuhsin usingoflearningvectorquantizationnetworkforpanevaporationestimation
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