Integrating image processing and machine learning for phase specific estimation of Manning roughness coefficient in furrow irrigation

Accurate estimation of Manning’s roughness coefficient ( n ) is vital for hydrological modeling and optimizing furrow irrigation systems, yet traditional methods remain limited due to spatial-temporal variability, the need for expert users, the inherent limitations of hydraulic equations, and labor-...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 37062 - 26
Main Authors: Rad, Hadi Rezaei, Ebrahimian, Hamed, Liaghat, Abdolmajid, Omid, Mahmoud, Khalaji, Fatemeh, Arani, Mahshid Shabani
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 23.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
704/172
704/242
Accuracy
Algorithms
Data collection
Data reduction
Furrow irrigation
Humanities and Social Sciences
Hydraulics
Image processing
Irrigation
Irrigation phases
Irrigation systems
Learning algorithms
Machine learning
Manning roughness coefficient
multidisciplinary
Precision agriculture
Roughness coefficient
Science
Science (multidisciplinary)
Soil moisture
Soils
Support vector machines
Sustainability management
Water management
Manning roughness coefficient
Furrow irrigation
Image processing
Irrigation phases
Machine learning
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Accurate estimation of Manning’s roughness coefficient ( n ) is vital for hydrological modeling and optimizing furrow irrigation systems, yet traditional methods remain limited due to spatial-temporal variability, the need for expert users, the inherent limitations of hydraulic equations, and labor-intensive measurements. This study introduces a novel algorithm that integrates high-resolution image processing with machine learning techniques to dynamically predict n during advance and storage phases in bare furrows. Three scenarios were evaluated: (i) full-field data (including inflow/outflow rates, advance/recession times, infiltration, slope, soil moisture, furrow images and etc.), (ii) images only, and (iii) images plus selected field data (inflow rate, slope, cross-sectional area, clod size and irrigation events). Manning’s n was computed using the SIPAR_ID model and Manning’s equation in advance and storage phases, yielding ranges of 0.017–0.636 (advance phase) and 0.015–0.317 (storage phase), with respective means of 0.083 and 0.054. The Random Forest algorithm in Scenario (i) achieved near-perfect performance (99% precision, recall, and F1-score), while Scenario (iii) preserved 95–96% accuracy with significantly reduced data inputs. Notably, excluding hydraulic variables (Scenario ii) led to a ~ 50% performance drop, highlighting their importance. This approach offers a robust, cost-effective solution for n estimation, bridging the gap between precision, practicality, and real-time application in sustainable water management and precision agriculture.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-20805-0