Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks
Pressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for...
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| Format: | Article |
| Language: | English |
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Ankara University
2023-01-01
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| Series: | Journal of Agricultural Sciences |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/1764820 |
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| author | Ezgi Kurtulmuş Ferhat Kurtulmuş Hayrettin Kuşçu Bilge Arslan Ali Osman Demir |
| author_facet | Ezgi Kurtulmuş Ferhat Kurtulmuş Hayrettin Kuşçu Bilge Arslan Ali Osman Demir |
| author_sort | Ezgi Kurtulmuş |
| collection | DOAJ |
| description | Pressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for pipe diameter selection are based on linear, non-linear, and dynamic programming models. The ultimate aim of these techniques is to produce solutions to problems with less cost and computation time. In this study, a novel approach for determining pipe diameter was proposedusing Artificial Neural Networks (ANN) as an alternative to existing models. For this purpose, three pressurized irrigation systems were investigated. Different ANN architectures were created and tested using hydrant level parameters of the irrigation systems, such as irrigated area per hydrant, hydrant discharge, pipe length, and hydrant elevation. Different training algorithms, transfer functions, and hidden neuron numbers were tried to determine the best ANN model for each irrigation system. Using multilayer feed-forward ANN architecture, the highest coefficients of determination were found to be 0.97, 0.93, and 0.83 for irrigation systems investigated. It was concluded that pipe diameters could be determined by using artificial neural networks in the planning of pressurized irrigation systems. |
| format | Article |
| id | doaj-art-aae03fc2b0ff4ba4a8b0a273f8e8f06d |
| institution | DOAJ |
| issn | 1300-7580 2148-9297 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Ankara University |
| record_format | Article |
| series | Journal of Agricultural Sciences |
| spelling | doaj-art-aae03fc2b0ff4ba4a8b0a273f8e8f06d2025-08-20T03:04:11ZengAnkara UniversityJournal of Agricultural Sciences1300-75802148-92972023-01-012918910210.15832/ankutbd.93633545Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural NetworksEzgi Kurtulmuş0Ferhat Kurtulmuş1Hayrettin Kuşçu2Bilge Arslan3Ali Osman Demir4BURSA ULUDAG UNIVERSITYBURSA ULUDAG UNIVERSITYBURSA ULUDAG UNIVERSITYBURSA ULUDAG UNIVERSITYBURSA ULUDAG UNIVERSITYPressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for pipe diameter selection are based on linear, non-linear, and dynamic programming models. The ultimate aim of these techniques is to produce solutions to problems with less cost and computation time. In this study, a novel approach for determining pipe diameter was proposedusing Artificial Neural Networks (ANN) as an alternative to existing models. For this purpose, three pressurized irrigation systems were investigated. Different ANN architectures were created and tested using hydrant level parameters of the irrigation systems, such as irrigated area per hydrant, hydrant discharge, pipe length, and hydrant elevation. Different training algorithms, transfer functions, and hidden neuron numbers were tried to determine the best ANN model for each irrigation system. Using multilayer feed-forward ANN architecture, the highest coefficients of determination were found to be 0.97, 0.93, and 0.83 for irrigation systems investigated. It was concluded that pipe diameters could be determined by using artificial neural networks in the planning of pressurized irrigation systems.https://dergipark.org.tr/tr/download/article-file/1764820machine learningoptimization techniquesirrigation water managementnetwork performance analysishydraulic parameters |
| spellingShingle | Ezgi Kurtulmuş Ferhat Kurtulmuş Hayrettin Kuşçu Bilge Arslan Ali Osman Demir Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks Journal of Agricultural Sciences machine learning optimization techniques irrigation water management network performance analysis hydraulic parameters |
| title | Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks |
| title_full | Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks |
| title_fullStr | Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks |
| title_full_unstemmed | Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks |
| title_short | Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks |
| title_sort | determination of pipe diameters for pressurized irrigation systems using linear programming and artificial neural networks |
| topic | machine learning optimization techniques irrigation water management network performance analysis hydraulic parameters |
| url | https://dergipark.org.tr/tr/download/article-file/1764820 |
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