پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 163 |
| تعداد شماره‌ها | 6,761 |
| تعداد مقالات | 72,826 |
| تعداد مشاهده مقاله | 131,644,108 |
| تعداد دریافت فایل اصل مقاله | 103,420,075 |
ارزیابی مدل‌های هوشمندGPR-PSO و KNN-PSO در برآورد توزیع غلظت رسوبات معلق | ||
| تحقیقات آب و خاک ایران | ||
| دوره 56، شماره 3، خرداد 1404، صفحه 701-714 اصل مقاله (1.55 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2025.377078.669846 | ||
| نویسندگان | ||
| محسن نصرآبادی* 1؛ یاسر مهری2؛ علی عبدالرزاق صبار3؛ محمدجواد نحوی نیا1 | ||
| 1گروه علوم و مهندسی آب، دانشکده کشاورزی و محیط‌زیست، دانشگاه اراک، اراک، ایران | ||
| 2گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 3گروه علوم و مهندسی آب، دانشکده کشاورزی و محیط‌زیست، دانشگاه اراک، اراک، ایران. | ||
| چکیده | ||
| توزیع عمودی غلظت رسوبات معلق یکی از اساسی‌ترین پارامترها در هیدرولیک انتقال رسوبات در رودخانه‌ها محسوب می‌شود. این پارامتر نقش مهمی در محاسبه دبی کل رسوبات در کانال‌ها و رودخانه‌ها دارد. به همین دلیل اندازه‌گیری دقیق این پارامتر همواره یکی از اهداف پژوهشگران بوده است. یکی از راه‌های برآورد دقیق این پارامتر، استفاده از مدل‌های هوشمند است. برای این منظور، در این تحقیق برای پیش‌بینی توزیع غلظت رسوبات (C/Ca)، چهار مدل داده‌کاوی KNN، KNN-PSO، GPR، GPR-PSO استفاده شده است. تمامی مدل‌ها در محیط نرم‌افزار MATLAB کدنویسی شدند. با توجه به نتایج مشخص شد که بهینه‌‌سازی انجام شده بر روی مدل KNN و GPR تاثیر‌گذار بوده و سبب افزایش عملکرد (دقت) این مدل‌ها شده است. با مقایسه بین مدل‌ها، نشان داده شد که مدل GPR-PSO دقت بیشتری نسبت به سایر مدل‌ها دارد. دقت این مدل در مرحله آموزش برابر با 0297/0 = RMSE، 9878/0 = R2 و 9776/0 = KGE بوده و در مرحله آزمون برابر با 0226/0 = RMSE، 9907/0 = R2 و 9715/0 = KGE است. از لحاظ دقت، بعد از GPR-PSO، مدل KNN-PSO با 0295/0 = RMSE، 9870/0 = R2 و 9864/0 = KGE در مرحله آموزش و 0374/0 = RMSE، 9808/0 = R2 و 9569/0 = KGE در مرحله آزمون قرار گرفت. پس از مدل‌های یادشده، GPR و KNN قرار گرفتند. همچنین با تحلیل نتایج مشخص شد که دو پارامتر y/D و y/a، مهم‌ترین پارامترها در تعیین نتایج دقیق‌تر هستند. | ||
| کلیدواژه‌ها | ||
| توزیع غلظت؛ رسوبات معلق؛ مدل‌های داده‌کاوی؛ رگرسیون فرآیند گاوسی | ||
| عنوان مقاله [English] | ||
| Evaluation of GPR-PSO and KNN-PSO data-mining models for prediction of suspended sediment concentration distribution | ||
| نویسندگان [English] | ||
| Mohsen Nasrabadi1؛ Yaser Mehri2؛ Ali Abdolrazaq Sabbar3؛ MohammadJavad Nahvinia1 | ||
| 1Department of Water Science and Engineering, Faculty of Agriculture and Environment, Arak University, Arak, Iran. | ||
| 2Depratment of Irrigation and Reclamation Engineering, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 3Department of Water Science and Engineering, Faculty of Agriculture and Environment, Arak University, Arak, Iran | ||
| چکیده [English] | ||
| The vertical distribution of suspended sediment concentration (SSC) is one of the most important parameters in the hydraulics of sediment transport in rivers. This parameter plays an important role in calculating the total sediment discharge in channels and rivers. For this reason, accurate measurement of this parameter has always been one of the goals of researchers. One way to accurately predict this parameter is to use intelligent models. For this purpose, in this study, four data mining models, KNN, KNN-PSO, GPR, and GPR-PSO, have been used to predict the distribution of sediment concentration (C/Ca). All models were coded in the MATLAB software environment. According to the results, it was found that the optimization performed on the KNN and GPR models was effective and increased the performance of these models. By comparing the models, it was shown that the GPR-PSO model has more accuracy than other models. The accuracy of this model in the training phase is equal to RMSE = 0.0297, R2 = 0.9878, and KGE = 0.9776, and in the testing phase equal to RMSE = 0.0226, R2 = 0.9907, and KGE = 0.9715. After GPR-PSO, the KNN-PSO model was ranked with RMSE = 0.0295, R2 = 0.9870, and KGE = 0.9864 in the training phase and RMSE = 0.0374, R2 = 0.9808, and KGE = 0.9569 in the testing phase. After the aforementioned models, GPR and KNN were respectively ranked. Also, by analyzing the results, it was determined that the two parameters y/D and y/a are the most important parameters in determining the most accurate results. | ||
| کلیدواژه‌ها [English] | ||
| Concentration distribution, suspended sediments, data mining models, Gaussian process regression | ||
| مراجع | ||
|
Abbaszadeh, H., Daneshfaraz, R., Sume, V., & Abraham, J. 2024. Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions. AQUA—Water Infrastructure, Ecosystems and Society, 73(3), 637-661. Abbaszadeh, H., Norouzi, R., Sume, V., Kuriqi, A., Daneshfaraz, R., & Abraham, J. 2023. Sill role effect on the flow characteristics experimental and regression model analytical. Fluids, 8(8), p. 235. Adnan, R.M., Liang, Z., El-Shafie, A., Zounemat-Kermani, M. and Kisi, O., 2019. Prediction of suspended sediment load using data-driven models. Water, 11(10), p.2060. Chien, N., 1955. Effects of Heavy Sediment Concentration Near the Bed on Velocity and Sediment Distribution. Missoury River Division, Corps of Engineers, US Army. Cigizoglu, H.K., 2004. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances in Water Resources, 27(2), pp.185-195. Dong, X., Yu, Z., Cao, W., Shi, Y. and Ma, Q., 2020. A survey on ensemble learning. Frontiers of Computer Science, 14, pp.241-258. Gupta, L.K., Pandey, M., Raj, P.A. and Shukla, A.K., 2023. Fine sediment intrusion and its consequences for river ecosystems: a review. Journal of Hazardous, Toxic, and Radioactive Waste, 27(1), p.04022036. Hassanpour, F., Sharifazari, S., Ahmadaali, K., Mohammadi, S. and Sheikhalipour, Z., 2019. Development of the FCM-SVR hybrid model for estimating the suspended sediment load. KSCE Journal of Civil Engineering, 23, pp.2514-2523. Hassanzadeh, Y. & Abbaszadeh, H. 2023. Investigating Discharge Coefficient of Slide Gate-Sill Combination Using Expert Soft Computing Models. Journal of Hydraulic Structures, 9(1), pp.63-80. Hassanzadeh, Y., 2007. Evaluation of sediment load in a natural river. Water International, 32(1), pp.145-154. Hassanzadeh, Y., Abbaszadeh, H. 2023. Investigating Discharge Coefficient of Slide Gate-Sill Combination Using Expert Soft Computing Models', Journal of Hydraulic Structures, 9(1), pp. 63-80. doi: 10.22055/jhs.2023.43683.1251 Hassanzadeh, Y., Abbaszadeh, H., Abedi, A., & Abraham, J. 2024. Numerical simulation of the effect of downstream material on scouring-sediment profile of combined spillway-gate. AQUA—Water Infrastructure, Ecosystems and Society, jws2024360. Heddam, S., Naghibi, A., Khosravi, K. and Singh, S.K., 2024. Suspended sediment load prediction and tree-based algorithms. In Remote Sensing of Soil and Land Surface Processes (pp. 257-269). Elsevier. Kaveh, K., Kaveh, H., Bui, M.D. and Rutschmann, P., 2021. Long short-term memory for predicting daily suspended sediment concentration. Engineering with Computers, 37, pp.2013-2027. Khozani, Z.S., Safari, M.J.S., Mehr, A.D. and Mohtar, W.H.M.W., 2020. An ensemble genetic programming approach to develop incipient sediment motion models in rectangular channels. Journal of Hydrology, 584, p.124753. Marashi, A., Kouchakzadeh, S. & Yonesi, H.A. 2023. Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA. Journal of Hydroinformatics. 25(4), 1312-1328. Mehri, Y., Nasrabadi, M. and Omid, M.H., 2021. Prediction of suspended sediment distributions using data mining algorithms. Ain Shams Engineering Journal, 12(4), pp.3439-3450. Mehri, Y., Soltani, J. & Khashehchi, M. 2019. Predicting the coefficient of discharge for piano key side weirs using GMDH and DGMDH techniques. Flow Measurement and Instrumentation, 65, pp. 1-6. Melesse, A.M., Ahmad, S., McClain, M.E., Wang, X. and Lim, Y.H., 2011. Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management, 98(5), pp.855-866. Nasrabadi, M., Mehri, Y., Ghassemi, A. and Omid, M.H., 2021. Predicting submerged hydraulic jump characteristics using machine learning methods. Water Supply, 21(8), pp.4180-4194. Nasrabadi, M., Riahi, S., Samadi Borujeni, H. 2014. Evaluation of the Distribution Equations of Suspended Sediment Concentration in Open Channels, Iranian Water Research, 8(1), 175-185 (in Persian). Omid, M. H. and Nasrabadi, M. 2012. Sedimentation Engineering. Tehran University Press. First Edition. 790 pages (in Persian). Pal, D. and Ghoshal, K., 2016. Vertical distribution of fluid velocity and suspended sediment in open channel turbulent flow. Fluid Dynamics Research, 48(3), p.035501. Prandtl, L., 1932. Zur turbulenten Strömung in Rohren und längs Platten. In Ergebnisse der aerodynamischen Versuchsanstalt zu Göttingen Lfg. 4 (pp. 18-29). De Gruyter. Ribeiro, M.H.D.M. and dos Santos Coelho, L., 2020. Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Applied soft computing, 86, p.105837. Rouse, H., 1937. Modern conceptions of the mechanics of fluid turbulence. Transactions of the American Society of Civil Engineers, 102(1), pp.463-505. Samantaray, S. and Sahoo, A., 2022. Prediction of suspended sediment concentration using hybrid SVM-WOA approaches. Geocarto International, 37(19), pp.5609-5635. Shafai-Bejestan, M. 2008. Theory and Application of Sediment Transport Hydraulics. Shahid Chamran University Press, Ahvaz. 550 pages (in Persian). Taki, M., Rohani, A., Soheili-Fard, F. and Abdeshahi, A., 2018. Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models. Journal of cleaner production, 172, pp.3028-3041. Trojovský, P. and Dehghani, M., 2022. Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22(3), p.855. Ulke, A., Tayfur, G. and Ozkul, S., 2009. Predicting suspended sediment loads and missing data for Gediz River, Turkey. Journal of Hydrologic Engineering, 14(9), pp.954-965. Vanoni, V.A., 1946. Transportation of suspended sediment by water. Transactions of the American Society of Civil Engineers, 111(1), pp.67-102. Zenko, B., Todorovski, L. and Dzeroski, S., 2001, November. A comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods. In Proceedings 2001 IEEE international conference on data mining (pp. 669-670). IEEE. Zhu, Y.M., Lu, X.X. and Zhou, Y., 2007. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology, 84(1-2), pp.111-125. Zounemat-Kermani, M., Batelaan, O., Fadaee, M. and Hinkelmann, R., 2021. Ensemble machine learning paradigms in hydrology: A review. Journal of Hydrology, 598, p.126266. | ||
|
آمار تعداد مشاهده مقاله: 56 تعداد دریافت فایل اصل مقاله: 42 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب