رویکردی نوین در تخمین ضریب زبری مانینگ در فازهای مختلف آبیاری جویچه‌ای با بهره‌گیری از پردازش تصویر و یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 پژوهشکده کشاورزی هسته ای، پژوهشگاه علوم و فنون هسته ای، سازمان انرژی اتمی، کرج- ایران

2 گروه آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

3 گروه مهندسی ماشین‌های کشاورزی، پردیس کشاورزی ومنابع طبیعی، دانشگاه تهران، کرج، ایران

4 گروه پردازش سیگنال، دانشکده مهندسی کامپیوتر و برق، دانشگاه آرهوس، آرهوس، دانمارک

چکیده

این تحقیق به بررسی کارایی استفاده تلفیقی از تکنیک‌های پردازش تصویر و روش‌های یادگیری ماشین برای تخمین ضریب زبری مانینگ در آبیاری جویچه‌ای در فازهای پیشروی و ذخیره پرداخته است. برای این منظور، مقادیر مختلف دبی ورودی، نوبت‌، مرحله و دورهای متفاوت آبیاری در دو نوع بافت خاک در نظر گرفته شد. تصاویری از سطح جویچه‌ها قبل و بعد از هر آبیاری ثبت گردید و ضریب زبری در فازهای پیشروی و ذخیره به ترتیب با استفاده از مدل SIPAR_ID و معادله مانینگ تخمین زده شد. سپس با استفاده از این داده‌ها، الگوریتمی بر مبنای استفاده تلفیقی از تکنیک‌های پردازش تصویر و روش‌های یادگیری ماشین در سه سناریوی مختلف توسعه یافت.  نتایج نشان داد که الگوریتم با استفاده از تصاویر یا داده‌های مزرعه‌ای به‌صورت مجزا نمی‌تواند به‌درستی آموزش ببیند و دقت بسیار پایینی دارد؛ چراکه برخی از ویژگی‌ها صرفاً از تصاویر و برخی دیگر از داده‌های مزرعه‌ای قابل‌دسترسی هستند. نتایج همچنین بیانگر، دقت بسیار مناسب الگوریتم در تخمین ضریب زبری مانینگ در فازهای پیشروی و ذخیره با استفاده از تلفیق تصاویر و برخی داده‌های مزرعه‌ای نظیر سطح مقطع جریان و دبی، بود. در سناریوی منتخب، روش جنگل تصادفی و CART با شاخص‌های precision، recall و F1-score برابر با ۹۵، ۹۶ و ۹۵ درصد، بهترین عملکرد را در تخمین ضریب زبری مانینگ نسبت به دیگر روش‌های یادگیری ماشین داشتند. در نهایت پیشنهاد شد که تحقیقات مشابهی با در نظر گرفتن سایر عوامل مؤثر بر زبری (نظیر پوشش گیاهی) و در شرایط متفاوت مزرعه‌ای (نظیر بافت و ساختمان خاک متفاوت) صورت پذیرد و الگوریتم متناسب با آن مجدداً آموزش ببیند تا کارایی و جامعیت آن ارتقا یابد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Novel Approach for Manning’s Roughness Coefficient Estimation in Furrow Irrigation Phases Using Image Processing and Machine Learning

نویسندگان [English]

  • Hadi Rezaei rad 1
  • Hamed Ebrahimian 2
  • Abdolmajid Liaghat 2
  • Mahmoud Omid 3
  • Nima Teimouri 4
1 Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj, Iran
2 Department of Irrigation & Reclamation Engineering, Faculty of Agriculture Engineering & Technology, University of Tehran, Karaj, Iran
3 Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
4 Signal Processing & Machine Learning Section, Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
چکیده [English]

This study investigates the effectiveness of combining image processing techniques and machine learning methods to estimate the Manning roughness coefficient in furrow irrigation during the advance and storage phases. For this purpose, various input discharge values, irrigation cycles, phases, and soil texture types were considered. Images of the furrow surface were captured before and after each irrigation event, and the roughness coefficient in the advance and storage phases was estimated using the SIPAR_ID model and the Manning equation, respectively. Based on this data, an algorithm was developed that integrated image processing techniques with machine learning methods and was tested in three different scenarios. The results showed that the algorithm, when using either images or field data separately, could not be properly trained and had very low accuracy, as some features were only accessible from images and others from field data. The results also revealed that the algorithm, when combining images with certain field data such as flow cross-section and discharge, performed very well in estimating the Manning roughness coefficient during both the advance and storage phases. In this scenario, the Random Forest and CART methods, with precision, recall, and F1-score values of 95%, 96%, and 95% respectively, outperformed other machine learning methods in estimating the Manning roughness coefficient. Finally, it was suggested that similar studies be conducted considering other factors affecting roughness under different conditions, and that the algorithm be retrained accordingly to improve its performance and comprehensiveness.

کلیدواژه‌ها [English]

  • Manning roughness coefficient
  • image processing
  • machine learning
  • advance and phase
  • phases

Introduction

Efficient water management in furrow irrigation is significantly influenced by various parameters, such as soil texture, furrow geometry, slope, and especially Manning's roughness coefficient. Accurate estimation of this coefficient is essential for simulating water flow. However, its temporal and spatial variability presents challenges, as it is influenced by factors like water infiltration into the soil, changes in soil composition, and the presence of vegetation. Traditional methods often estimate roughness with limited accuracy, overlooking these temporal and spatial variations and assuming the coefficient remains constant during irrigation. Such assumptions can lead to significant errors in flow simulation in surface irrigation. In this study, we propose an integrated approach that combines image processing and machine learning methods to provide accurate and rapid estimation of Manning's roughness coefficient in both the advance and storage stages.

Materials and Methods

In this study, we developed an algorithm that combines image processing techniques and machine learning methods, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (KNN), Decision Tree (CART), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), to estimate the Manning roughness coefficient in furrow irrigation. This algorithm is specifically designed for the advance and storage phases of irrigation in bare furrows. The accuracy of Manning roughness coefficient estimation was evaluated using the developed algorithm under three scenarios: (i) using image data alone, (ii) using only field data, and (iii) combining both image and field data.

To achieve this, six different inflow rates were tested across two categories, low and high flow, with average flow rates of 0.27 and 0.53 L/s, respectively. Additional parameters included three irrigation events (first to third), two irrigation phases (advance and storage), two irrigation intervals (5 and 10 days), and two soil textures. Images of the furrow surface were taken from a height of 100 cm above the soil under controlled lighting conditions before and after each irrigation event. The Manning roughness coefficient was determined for the advance and storage phases using the SIPAR_ID model and Manning equation, respectively.

Results and Discussion

In the first step, Manning’s roughness coefficient values were determined for the advance and storage phases, yielding accurate results that supported their use in the algorithm. In terms of numerical accuracy, the model’s R² values for the advance phase ranged between 0.91 and 1.0, with a mean of 0.995, indicating strong predictive power. The root mean square error (RMSE) for this phase was between 0.2 and 2.6 minutes, with an average of 0.36 minutes, while the relative error (RE) remained consistently low, below 4.24%. For the storage phase, Manning’s roughness coefficient values averaged 0.073 in field E and 0.041 in field F, demonstrating a decline in roughness as successive irrigation events smoothed the soil surface. These results, which aligned with physical observations, confirmed the high accuracy of the initial estimates and justified their use in the developed algorithm. Following this validation, the performance of the algorithm was evaluated under three scenarios. The hybrid approach that integrated both image and field data outperformed the other two scenarios, achieving the highest accuracy in estimating Manning’s roughness coefficient. Using only image data resulted in lower accuracy, with Random Forest achieving an accuracy of 60%, recall of 60%, and precision of 55%, underscoring the necessity of combining data sources. In the hybrid scenario, Random Forest provided the best classification results, achieving a precision of 95%, recall of 96%, and F1-score of 95%. The CART model also showed competitive performance, with accuracy and precision metrics closely following those of Random Forest. These findings indicate that the combined approach of image and field data provides a more reliable and precise estimation of Manning’s roughness coefficient across different irrigation conditions and phases.

Conclusion

This study introduces a novel method for estimating the Manning roughness coefficient in furrow irrigation systems by combining image processing and machine learning techniques. This approach provides a more efficient and precise solution, particularly in furrows without vegetation. Future work should include additional parameters, such as lighting conditions, image angles, as well as various hydraulic and field conditions, to refine the algorithm and enhance its applicability under diverse real-world situations. This research advances the development of automated, precise estimation methods for improved irrigation management and water use efficiency.

Author Contributions

HRR, HE, AL, and MO conceptualized the research topic and formulated the objectives and methodology of the research. HRR carried out the field experiments as well as the data curation of the research work. HRR and MO developed the algorithm. HRR, HE, MO, and AL participated in the writing of the manuscript. All the authors read and approved the final manuscript for publication.

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability Statement

All data generated and analyzed during this study are included in this article.

Acknowledgements

The authors would like to acknowledge the fnancial support of University of Tehran Science and Technology Park for this research under grant number 5888656.

Ethical considerations

The study was approved by the Ethics Committee of the University of ABCD (Ethical code: IR.UT.RES.2024.500). The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

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تحقیقات آب و خاک ایران
دوره 56، شماره 4
تیر 1404
صفحه 1011-1039

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