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Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms

The coefficients of uniformity Christiansen's uniformity coefficient (CU) and distribution uniformity (DU) are an important parameter for designing irrigation systems, and are an accurate measure for water lose. In this study, three machine learning algorithms Random forest (RF), extreme gradie...

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Autores principales: Elhussiny, Khadiga T., Hassan, Ahmed M., Habssa, Ahmed Abu, Mokhtar, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684584/
https://www.ncbi.nlm.nih.gov/pubmed/38017247
http://dx.doi.org/10.1038/s41598-023-47688-3
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author Elhussiny, Khadiga T.
Hassan, Ahmed M.
Habssa, Ahmed Abu
Mokhtar, Ali
author_facet Elhussiny, Khadiga T.
Hassan, Ahmed M.
Habssa, Ahmed Abu
Mokhtar, Ali
author_sort Elhussiny, Khadiga T.
collection PubMed
description The coefficients of uniformity Christiansen's uniformity coefficient (CU) and distribution uniformity (DU) are an important parameter for designing irrigation systems, and are an accurate measure for water lose. In this study, three machine learning algorithms Random forest (RF), extreme gradient boosting (XGB) and random forest-extreme gradient boosting (XGB-RF) were developed to predict the water distribution uniformity based on operating pressure, heights of sprinkler, discharge, nozzle diameter, wind speed, humidity, highest and lowest temperature for three different impact sprinklers (KA-4, FOX and 2520) for square and triangular system layout based on four scenarios (input combinations). The main findings were; the highest CU value was 86.7% in the square system of 2520 sprinkler under 200 kPa, 0.5 m height and 0.855 m(3)/h (Nozzle 2.5 mm). Meanwhile, in the triangular system, it was 87.3% under the same pressure and discharge and 1 m height. For applied machine learning, the highest values of R(2) were 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario for CU. Moreover, for the DU, the highest values of R(2) were 0.701, 0.479 and 0.826 in RF, XGB and XGB-RF respectively in the first scenario. The obtained results revealed that the sprinkler height had the lowest impact on modeling of the water distribution uniformity.
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spelling pubmed-106845842023-11-30 Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms Elhussiny, Khadiga T. Hassan, Ahmed M. Habssa, Ahmed Abu Mokhtar, Ali Sci Rep Article The coefficients of uniformity Christiansen's uniformity coefficient (CU) and distribution uniformity (DU) are an important parameter for designing irrigation systems, and are an accurate measure for water lose. In this study, three machine learning algorithms Random forest (RF), extreme gradient boosting (XGB) and random forest-extreme gradient boosting (XGB-RF) were developed to predict the water distribution uniformity based on operating pressure, heights of sprinkler, discharge, nozzle diameter, wind speed, humidity, highest and lowest temperature for three different impact sprinklers (KA-4, FOX and 2520) for square and triangular system layout based on four scenarios (input combinations). The main findings were; the highest CU value was 86.7% in the square system of 2520 sprinkler under 200 kPa, 0.5 m height and 0.855 m(3)/h (Nozzle 2.5 mm). Meanwhile, in the triangular system, it was 87.3% under the same pressure and discharge and 1 m height. For applied machine learning, the highest values of R(2) were 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario for CU. Moreover, for the DU, the highest values of R(2) were 0.701, 0.479 and 0.826 in RF, XGB and XGB-RF respectively in the first scenario. The obtained results revealed that the sprinkler height had the lowest impact on modeling of the water distribution uniformity. Nature Publishing Group UK 2023-11-28 /pmc/articles/PMC10684584/ /pubmed/38017247 http://dx.doi.org/10.1038/s41598-023-47688-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Elhussiny, Khadiga T.
Hassan, Ahmed M.
Habssa, Ahmed Abu
Mokhtar, Ali
Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
title Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
title_full Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
title_fullStr Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
title_full_unstemmed Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
title_short Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
title_sort prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684584/
https://www.ncbi.nlm.nih.gov/pubmed/38017247
http://dx.doi.org/10.1038/s41598-023-47688-3
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