Evaluation of Machine-Learning Approaches in the Automation of Irrigation Canals Using a Variable-Height WeirSource: Journal of Irrigation and Drainage Engineering:;2024:;Volume ( 150 ):;issue: 006::page 04024030-1DOI: 10.1061/JIDEDH.IRENG-10327Publisher: American Society of Civil Engineers
Abstract: Automatic check structures can be important for water distribution in irrigation networks. In this research, a control algorithm was developed for a variable height whirling (VHW) weir, as a regulating structure equipped with a control mechanism. A local feedback controller was established for adjusting the flow depth upstream of the weir within a marginal target range. The control performance of the VHW weir was investigated using two methods: (1) K nearest neighbor (KNN); and (2) artificial neural network (ANN). The required data for methods were compiled in a long trapezoidal canal using different water depth targets. The inputs consisted of the discharge at the canal entrance, the variation of the discharge in three sequential periods, the water level deviation from the target value, and the offtake discharge. The model output was the set point of the instantaneous weir angle value, which represents the crest weir height, for maintaining the water depth within the target range. Different statistical indicators were employed to investigate the control performance. The results indicated that the ANN models, which were applied to cases with and without offtake in operation, provided 0.95 and 0.93 correlation coefficients, respectively. Also, the proposed neural model performed slightly better than the KNN algorithm, which yielded marginally higher error in output predictions.
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contributor author | Shahla Zamani | |
contributor author | Atefeh Parvaresh Rizi | |
contributor author | Salah Kouchakzadeh | |
contributor author | Hedieh Sajedi | |
date accessioned | 2025-04-20T10:20:50Z | |
date available | 2025-04-20T10:20:50Z | |
date copyright | 9/28/2024 12:00:00 AM | |
date issued | 2024 | |
identifier other | JIDEDH.IRENG-10327.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4304527 | |
description abstract | Automatic check structures can be important for water distribution in irrigation networks. In this research, a control algorithm was developed for a variable height whirling (VHW) weir, as a regulating structure equipped with a control mechanism. A local feedback controller was established for adjusting the flow depth upstream of the weir within a marginal target range. The control performance of the VHW weir was investigated using two methods: (1) K nearest neighbor (KNN); and (2) artificial neural network (ANN). The required data for methods were compiled in a long trapezoidal canal using different water depth targets. The inputs consisted of the discharge at the canal entrance, the variation of the discharge in three sequential periods, the water level deviation from the target value, and the offtake discharge. The model output was the set point of the instantaneous weir angle value, which represents the crest weir height, for maintaining the water depth within the target range. Different statistical indicators were employed to investigate the control performance. The results indicated that the ANN models, which were applied to cases with and without offtake in operation, provided 0.95 and 0.93 correlation coefficients, respectively. Also, the proposed neural model performed slightly better than the KNN algorithm, which yielded marginally higher error in output predictions. | |
publisher | American Society of Civil Engineers | |
title | Evaluation of Machine-Learning Approaches in the Automation of Irrigation Canals Using a Variable-Height Weir | |
type | Journal Article | |
journal volume | 150 | |
journal issue | 6 | |
journal title | Journal of Irrigation and Drainage Engineering | |
identifier doi | 10.1061/JIDEDH.IRENG-10327 | |
journal fristpage | 04024030-1 | |
journal lastpage | 04024030-10 | |
page | 10 | |
tree | Journal of Irrigation and Drainage Engineering:;2024:;Volume ( 150 ):;issue: 006 | |
contenttype | Fulltext |