| تعداد نشریات | 285 |
| تعداد نشریات سازمان | 83 |
| تعداد شمارهها | 3,139 |
| تعداد مقالات | 34,918 |
| تعداد مشاهده مقاله | 50,383,264 |
| تعداد دریافت فایل اصل مقاله | 90,276,677 |
کاربرد هوش مصنوعی در پیشبینی ضریب دبی در سرریزهای کنگرهای با پلان هارمونیک | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 25، پاییز - شماره پیاپی 96، آذر 1403، صفحه 82-61 اصل مقاله (2.55 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2025.368037.1602 | ||
| نویسندگان | ||
| امل سواعدی1؛ محمدرضا زایری* 2؛ مهدی قمشی3؛ مهدی دریائی4 | ||
| 1دانشجوی کارشناسی ارشد هیدروانفورماتیک، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 2استادیار گروه سازه های آبی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 3استاد گروه سازه های آبی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 4دانشیار گروه سازه های آبی-دانشکده مهندسی آب و محیط زیست-دانشگاه شهید چمران اهواز-اهواز-ایران | ||
| چکیده | ||
| با رشد جمعیت، تقاضا برای ذخیرهسازی آب در مخازن افزایش یافته است و نیاز به جایگزینی یا افزایش ظرفیت دبی بسیاری از سرریزها از طریق بهینهسازی هندسی و طرح افقی آنها به وجود آمده است. یکی از راهکارهای کاربردی برای افزایش راندمان سرریزها، اصلاح هندسۀ پلان و افزایش طول سرریز در عرضی ثابت است. این امر باعث افزایش ضریب دبی (Cd) سرریز میشود. در این مطالعه، با جمعآوری دادههای آزمایشگاهی دو منبع مختلف، در مجموع از 233 سری داده شامل مؤلفههای هندسی و هیدرولیکی استفاده شد. سه مدل یادگیری ماشین، شامل رگرسیون بردار پشتیبان (SVR) و مدل ناپارامتریک جنگل تصادفی (RF) و رگرسیون اسپیلاین تطبیقی چندگانه (MARS)، با پنج سناریوی ورودی مختلف برای پیشبینی دقیق ضریب دبی سرریز کنگرهای با پلان نیمدایره هارمونیک توسعه داده شد. نتایج تحقیق نشان داد که متغیرهای ورودی شامل، نسبت شعاع به ارتفاع سرریز (R/P)، نسبت عرض سرریز به ارتفاع سرریز (B/P)، تعداد سیکل (N) و نسبت هد هیدرولیکی به ارتفاع سرریز (HT/P)، در مدل MARS با مقادیر 0/008= RMSE و 0/94=R2 و مدل SVR با مقادیر 0/009= RMSE و 0/93=R2، نتایج بهتری را در تخمین (Cd) ارائه میکنند. مشاهده گردید در مقادیر (HT/P) پایینتر از 0/05، میزان ضریب دبی عبوری سرریز با 4 سیکل نیمدایره (4=N)، بیشتر از سایز سرریزها بوده است. همچنین، با استفاده از دو روش مختلف آنالیز حساسیت مشاهده گردید پارامتر عمق نسبی (HT/P) مؤثرترین مؤلفه در پیشبینی ضریب دبی بوده است. | ||
| کلیدواژهها | ||
| آنالیز حساسیت SHAP؛ یادگیری ماشین؛ ضریب دبی؛ سرریزهای کنگرهای | ||
| عنوان مقاله [English] | ||
| Application of Artificial Intelligence in Predicting the Discharge Coefficient of Labyrinth Weirs with Harmonic Plans | ||
| نویسندگان [English] | ||
| Amal Savaedi1؛ mohamadreza zayeri2؛ Mehdi Ghomeshi3؛ Mehdi Daryaee4 | ||
| 1Masters Student in Hydroinformatics, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| 2Assistant Professor, Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 3Professor, Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| 4Associate Professor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
| چکیده [English] | ||
| Extended Abstract Introduction Dams, as barriers constructed across rivers, are comprised of essential components such as the body, spillway, and drainage systems. Various labyrinth spillway designs, including triangular, trapezoidal, circular, and polygonal horizontal layouts, extend the effective flow path over a fixed width compared to linear spillways. Researchers aim to identify optimal designs balancing high performance and cost-efficiency. Recent advancements highlight the integration of optimization methods and computational fluid dynamics (CFD) to improve labyrinth spillway designs. Studies have explored the hydraulic and geometric factors affecting discharge coefficients (Cd) and flow velocity. Research includes the application of artificial intelligence (AI) models such as artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFIS), and regression techniques to predict Cd. Notable contributions demonstrate that AI models effectively capture complex nonlinear relationships between geometric parameters and flow rates, outperforming traditional methods. For instance, models like support vector machines (SVM) and adaptive regression spline (MARS) have demonstrated high accuracy in predicting Cd. Despite advancements, precise predictive models for labyrinth spillways with harmonic plans remain underdeveloped. This study addresses this gap by introducing new methodologies, including SVM, random forests (RF), and MARS, to predict Cd. It also quantifies the influence of dimensionless parameters on Cd, synthesizing experimental data to enhance understanding and bridge existing research gaps. Methodology In this study, soft computing models were developed using experimental results from Arham Namazi and Mozaffari (2023) and Yıldız et al. (2024). To evaluate the accuracy of proposed soft computing equations in estimating the discharge coefficient (Cd) for circular labyrinth weirs arranged harmonically in open channels, the following experimental data were utilized: Yıldız et al. (2024): conducted 215 experiments for weirs with three different heights (P = 20 cm, P = 30 cm, and P = 40 cm) and three different cycle numbers (N = 2, N = 3, and N = 4). Arham Namazi and Mozaffari (2023): performed 18 experiments with a fixed weir height (P = 15 cm) configured as a single cycle (N = 1). In total, 233 experimental results were collected for soft computing-based modeling. Among these, 175 samples (75%) were used for model training, and 58 samples (25%) were allocated for testing the developed models. Results and Discussion Violin plots for both measured and predicted data inferred by various machine learning models are presented. Violin plots are typically used to compare the distribution of data across different groups in terms of their shape. Additionally, a small box plot is embedded within each violin plot, where the ends of the rectangle represent the first and third quartiles, and the central point denotes the median. it can be observed that all three models—RF, SVM, and MARS—predict similar first and third quartiles and medians, compared to the measured data. In contrast, the first or third quartiles in the equations proposed by Arham Namazi and Mozaffari (2023) and Equation 18 show significant deviations from the measured values. Furthermore, from the perspective of the overall data distribution, the SVM and MARS algorithms demonstrate distributions more similar to the measured data compared to the RF algorithm. This highlights the superior predictive capability of the support vector machine (SVM) approach. Conclusions Labyrinth weirs are consistently proposed as an effective solution for enhancing flood discharge efficiency, particularly in cases where space for weir construction is limited. These weirs have a longer crest length compared to linear weirs, allowing floods to pass at shallower depths. Due to the complex relationship between the discharge coefficient and its associated parameters, empirical equations often fail to predict the discharge coefficient with acceptable accuracy. In this study, three different machine learning models were developed to predict the discharge coefficient of semicircular labyrinth weirs with harmonic designs. The results confirm the advantages of the Support Vector Machine (SVM) algorithm. Key findings of the study are summarized as follows: Parameter Sensitivity Analysis: To minimize prediction errors in the machine learning models, a sensitivity analysis was conducted to identify the relative head is importance of different input parameters. Based on this analysis, five input combinations were designed and applied to the machine learning models. Optimal Input Combination: Statistical comparisons between predicted and experimental data revealed that the optimal input combination effectively predicted the discharge coefficient for this type of weir. Model Performance: Using the best input combination, the results showed that the SVM and MARS algorithms outperformed tree-based models, such as Random Forest (RF), in prediction accuracy for harmonic weirs with varying cycles. MARS Model Evaluation: Although the MARS model performed well, comparisons with other regression models from previous studies demonstrated that MARS delivered satisfactory and improved accuracy over those models. | ||
| کلیدواژهها [English] | ||
| Machine Learning, SHAP Sensitivity Analysis, Discharge Coefficient, Labyrinth Weirs | ||
| مراجع | ||
|
Arham Namazi, F. S., & Mozaffari, J. (2023). Investigation of labyrinth weirs discharge coefficient with the same length. Flow Measurement and Instrumentation, 94, 102468. https://doi.org/10.1016/j.flowmeasinst.2023.102468 Behdarvandi, B., Zayeri, M. R., Ghomeshi, M., & Daryaee, M. (2024). Performance of Support Vector Machines, Random Forest, and GMDH Methods for Predicting Flow Rate from Rectangular Flap Gates. Irrigation and Water Engineering. https://doi.org/10.22125/iwe.2024.460044.1811 Bilhan, O., Aydin, M. C., Emiroglu, M. E., & Miller, C. J. (2018). Experimental and CFD Analysis of Circular Labyrinth Weirs. Journal of Irrigation and Drainage Engineering, 144(6), 04018007. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001301 Bonakdari, H., Ebtehaj, I., Gharabaghi, B., Sharifi, A., & Mosavi, A. (2020). Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming (No. arXiv:2002.02751). arXiv. https://doi.org/10.48550/arXiv.2002.02751 Borghei, S. M., & Parvaneh, A. (2011). Discharge characteristics of a modified oblique side weir in subcritical flow. Flow Measurement and Instrumentation, 22(5), 370–376. https://doi.org/10.1016/j.flowmeasinst.2011.04.009 Borisova, N. M., & Belikov, V. V. (2023). Optimization of Spillway Design of Reconstructed Shavan Dam Using Numerical 2D-Modeling. Power Technology and Engineering, 57(1), 67–74. Scopus. https://doi.org/10.1007/s10749-023-01624-7 Dutta, D., Mandal, A., & Afzal, M. S. (2020). Discharge performance of plan view of multi-cycle W-form and circular arc labyrinth weir using machine learning. Flow Measurement and Instrumentation, 73, 101740. https://doi.org/10.1016/j.flowmeasinst.2020.101740 Emami, S., Parsa, J., Emami, H., & Abbaspour, A. (2021). An ISaDE algorithm combined with support vector regression for estimating discharge coefficient of W-planform weirs. Water Supply, 21(7), 3459–3476. Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67. Ghaderi, D., Ebrahimnezhadian, H., & Mollazadeh, M. (2024). Three-dimensional analysis of the performance of circular stepped spillways in the skimming flow regime. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(7). Scopus. https://doi.org/10.1007/s40430-024-05004-8 Haghbin, M., Sharafati, A., Aghamajidi, R., Asadollah, S. B. H. S., Noghani, M. H. M., & Jalón, M. L. (2022). Determination of discharge coefficient of stepped morning glory spillway using a hybrid data-driven method. Flow Measurement and Instrumentation, 85, 102161. Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (Vol. 2). Springer. https://link.springer.com/book/10.1007/978-0-387-21606-5 Hussein, B. S., & Jalil, S. A. (2024). Influence of Labyrinth Side Weir Shape Modification on the Hydrodynamic Performance: Experimental and Numerical Study. Arabian Journal for Science and Engineering. Scopus. https://doi.org/10.1007/s13369-024-09563-9 Ikinciogullari, E. (2024). Energy dissipation performance of labyrinth and harmonic stepped spillways. Journal of Hydroinformatics, 26(10), 2668–2682. Scopus. https://doi.org/10.2166/hydro.2024.221 Kisi, O., Emiroglu, M. E., Bilhan, O., & Guven, A. (2012). Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches. Expert Systems with Applications, 39(3), 3454–3460. Li, S., Shen, G., Parsaie, A., Li, G., & Cao, D. (2024). Discharge modeling and characteristic analysis of semi-circular side weir based on the soft computing method. Journal of Hydroinformatics, 26(1), 175–188. Lundberg, S. (2017). A unified approach to interpreting model predictions. arXiv Preprint arXiv:1705.07874. Momeni, E., Nazir, R., Jahed Armaghani, D., & Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122–131. https://doi.org/10.1016/j.measurement.2014.08.007 Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. K. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention, 136, 105405. https://www.sciencedirect.com/science/article/pii/S0001457519311790 Rao, S. S., & Shukla, M. K. (1971). Characteristics of flow over weirs of finite crest width. Journal of the Hydraulics Division, 97(11), 1807–1816. https://doi.org/10.1061/JYCEAJ.0003138 Safarrazavi Zadeh, M., Esmaeili Varaki, M., & Biabani, R. (2021). Experimental study on flow over sinusoidal and semicircular labyrinth weirs. ISH Journal of Hydraulic Engineering, 27(sup1), 304–313. https://doi.org/10.1080/09715010.2019.1644679 Sangsefidi, Y., Mehraein, M., & Ghodsian, M. (2018). Experimental study on flow over in-reservoir arced labyrinth weirs. Flow Measurement and Instrumentation, 59, 215–224. https://www.sciencedirect.com/science/article/pii/S0955598616303156 Shafiei, S., Najarchi, M., & Shabanlou, S. (2020a). A novel approach using CFD and neuro-fuzzy-firefly algorithm in predicting labyrinth weir discharge coefficient. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(1), 44. https://doi.org/10.1007/s40430-019-2109-9 Shafiei, S., Najarchi, M., & Shabanlou, S. (2020b). Simulation of labyrinth weir discharge coefficient by modern artificial intelligence models. Modares Civil Engineering Journal, 20(1), 204–218. Siasar, H., & Honar, T. (2019). Application of support vector machine, CHAID and random forest models, in estimated daily reference evapotranspiration in northern Sistan and Baluchestan Province. Iranian Journal of Irrigation & Drainage, 13(2), 378–388. Simsek, O., Gumus, V., & Ozluk, A. (2023). Prediction of discharge coefficient of the trapezoidal broad-crested weir flow using soft computing techniques. Neural Computing and Applications, 35(24), 17485–17499. Suleimany, J. M. F. S., Aurahman, T. H., & S.mamand, B. (2022). Flow simulation over semicircular labyrinth weir using ANSYS-fluent. Tikrit Journal of Engineering Sciences, 29(1), 83–98. Scopus. https://doi.org/10.25130/tjes.29.1.7 Yıldız, A., Marti, A. İ., & Göğüş, M. (2024). The hydraulic investigation of harmonic plan weirs. Flow Measurement and Instrumentation, 95, 102512. https://doi.org/10.1016/j.flowmeasinst.2023.102512 Zare, H., Vaghefi, M., Mahmoudi, A., & Behroozi, A. M. (2023). Experimental Exploration of Flow Hydraulics and Discharge Coefficient for an Inclined Circular Labyrinth Weir. Water Resources Management, 37(11), 4521–4536. Scopus. https://doi.org/10.1007/s11269-023-03577-y Zayeri, M. (2023). Discharge Prediction in Flumes with Trapezoidal Contraction by Machine Learning Techniques. Irrigation and Drainage Structures Engineering Research, 24(90), 55–70. Zhang, W. (2020). MARS Applications in Geotechnical Engineering Systems: Multi-Dimension with Big Data. Springer Singapore. https://doi.org/10.1007/978-981-13-7422-7
| ||
|
آمار تعداد مشاهده مقاله: 522 تعداد دریافت فایل اصل مقاله: 216 |
||