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مدلسازی مبتنی بر هوش محاسباتی در برآورد بار معلق رودخانه (ایستگاه باقرآباد قمرود) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 23، شماره 88، آذر 1401، صفحه 99-125 اصل مقاله (1.43 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2023.361853.1540 | ||
| نویسندگان | ||
| امیر مرادی نژاد* 1؛ عباس پارسائی2؛ سعید خسروبیگی3؛ احمد حسینی4؛ محمودرضا طباطبایی5 | ||
| 1استادیار بخش حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان مرکزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران. | ||
| 2استادیار، گروه سازه های آبی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز ایران | ||
| 3دکتری آبخیزداری و رئیس بخش فنی و مهندسی اداره کل منابع طبیعی استان مرکزی. | ||
| 4استادیار بخش مهندسی رودخانه و سواحل، پژوهشکده حفاظت خاک و آبخیزداری کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران ایران. | ||
| 5استادیار پژوهشی، پژوهشکده حفاظت خاک و آبخیزداری کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران ایران. | ||
| چکیده | ||
| برآورد مقدار رسوب رودخانه در طراحی و اجرای سازههای آبی و ساماندهی رودخانه اهمیت بهسزایی دارد. شبیهسازی و ارزیابی رسوب رودخانه از مسائل مهم و کاربردی در مدیریت منابع آب است. بنابراین دست یافتن به شیوه های نوین و خلاقانه که بتواند در این راستا موثر واقع گردد دارای اهمیت زیادی است. هدف از این پژوهش ارزیابی و مقایسة مدلهای فازی- عصبی تطبیقی (ANFIS)، ماشین بردار پشتیبان (SVM) و برنامهریزی بیان ژن (GEP) در برآورد بار رسوب ایستگاه باقرآباد رودخانه قمرود استان مرکزی است. بدین منظور عملکرد سه نوع مدل ماشین بردار پشتیبان (SVM)، سیستم عصبی فازی-تطبیقی (ANFIS) و برنامهریزی بیان ژن (GEP) در شبیه سازی بار رسوبی رودخانه ها بررسی و نتایج بهدست آمده از این سه روش با یکدیگر و با نتایج منحنی سنجه مقایسه شد. نتایج بیانگر عملکرد قابل قبول مدل ها نسبت به منحنی سنجه است. برتری مدل (GEP) با بیشترین ضریب تعیین R2 با مقدار 0/99 و کمترین ریشه میانگین مربعات خطا RMSE بر حسب تن در روز با مقدار 0/010 نشان داده شد. در این خصوص کارآیی مدلSVR با R2 با مقدار 0/95 و RMSE برابر 1/49 تا حدی بهتر از مدل ANFIS با R2 با مقدار 0/91 و RMSE برابر 7/94 بود. نتایج به دست آمده همچنین نشان داد هر سه روش داده کاوی بررسی شده نسبت به منحنی سنجة رسوب، نتایج بهمراتب بهتری ارائه می کننند. | ||
| کلیدواژهها | ||
| برنامهریزی بیان ژن؛ شبکه عصبی؛ رسوب؛ ماشین بردار پشتیبان | ||
| عنوان مقاله [English] | ||
| Modeling based on computational intelligence in river suspended load estimation (Baqorabad Station of the Qamroud River) | ||
| نویسندگان [English] | ||
| amir moradinejad1؛ Abbas Parsaie2؛ saeid khosrobeghi3؛ Seyed Ahmad Hosseini4؛ Mahmodreza Tabatabaei5 | ||
| 1Assistant Prof., Soil Conservation and Watershed Management Research Department, Markazi Agricultural and Natural Resources Research and Education Center, Arak, Agricultural Research Education & Extention Organization (AREEO). Tehran, Iran. | ||
| 2Assistant Professor, Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 3PhD in Watershed Management and Head of Technical and Engineering Department of the General Department of Natural Resources of Markazi Province | ||
| 4Assistant Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran. | ||
| 5Assistant Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran. | ||
| چکیده [English] | ||
| Extended Abstract Introduction Estimating the sediment load of rivers is one of the important and practical issues in the studies and design of water engineering projects, such as the design and development of irrigation and drainage networks, water extraction from rivers, etc. Sediment concentration can be calculated by direct or indirect methods, which are usually expensive and time-consuming direct methods. Various factors affect this phenomenon, which makes their analysis difficult. Statistical and regression models are the most common methods of analysis, which often provide erroneous results due to the linear solution of these phenomena. Therefore, they cannot model the sedimentation phenomenon with acceptable accuracy. Hydraulic models cannot always be trusted due to the need for a lot of data, the unavailability of the required data, and the inaccuracy of the data due to human error in simulating sediments. Due to their ability to solve complex and nonlinear phenomena, intelligent fuzzy and neural conductor systems have found many applications in various water engineering problems, including sedimentation. This research aims to evaluate and compare adaptive neural fuzzy models (ANFIS), machine Support vector (SVM), and GEP gene expression programming in estimating the sediment load of selected rivers in Central Province. For this purpose, the performance of three types of support vector machine (SVM), adaptive neural system (ANFIS), and GEP gene expression programming in the simulation of the sediment load of rivers was studied. Methodology In this research, first, the long-term daily statistics of temperature, rainfall, average flow rate, and sediment concentration of Bagharabad Abad hydrometric station and sediment measuring station located on the main branch of the of Qamroud River were collected. Then, the data sufficiency test for analysis, checking the correlation between parameters of river discharge, precipitation, temperature with sediment discharge, and determining the long-term average of suspended sediment in the studied stations were performed. In the next step, a suitable combination of input variables was selected. The design of input parameter pattern can be based on the relationship between flow and sediment flow parameters, rainfall, temperature, flow, and sediment flow. Determining the most appropriate time delay of input parameters in flow and sediment modeling. Estimation of sediment discharge using support vector machine (SVM), gene expression programming (GEP), and fuzzy-adaptive neural system (Anfis), comparing three data mining methods with each other as well as with gauge curve and observational data. The appropriate design of the structure of soft calculation models is used, in this research, the number of data required for training (usually more than 70 percent), research data as training, and also determining the required data (usually between 20 and 30 percent) is used for validation and testing. Results and Discussion In this research, the best performance of the GEP model has been obtained for pattern number 13. In this model, the R2 explanatory coefficient and the RMSE error obtained from the model are 0.97 and 0.033, respectively. The coefficient of explanation R2 and the RMSE error of the models in predicting suspended sediment values in the test phase were obtained as 0.53, 3.18 for the ANFIS model, and 0.70, 15.16 for the SVR model, respectively. Comparing the results of ANFIS and GEP models with the SVR model indicates the superiority of the GEP model, in predicting the amount of suspended sediment according to Verdi model number 13. According to the obtained results, it can be seen that the performance of GEP model was better compared to other models. SVR and ANFIS models are ranked second and third. According to the obtained results, it can be said that the GEP model as a powerful and high-speed model, can be used to model the suspended sediment in the Qomroud catchment area of Bagherabad station. Conclusions The results show the acceptable performance of the models compared to the gauge curve. Also, the results showed the superiority of the GEP model with the highest coefficient of determination R2 with a value of 0.99 and the lowest root mean square error RMSE in terms of tons per day with a value of 0.010. In this regard, the efficiency of the SVR model was somewhat better than the ANFIS model. The results showed that all three investigated data mining methods provide far better results than the sediment gauge curve. | ||
| کلیدواژهها [English] | ||
| Gene expression programming, neural network, sedimentation, support vector machine | ||
| مراجع | ||
|
Asadi, M., and Fathzadeh, A. (2017) Investigating the effectiveness of models based on computing intelligence in estimating the suspended load of the river (case study: Gilan province. Journal of rangeland and watershed management, natural resources of Iran, 1(71). 45-60. (In Persian). Aytek A. and O. Kisi. 2008. A genetic programming approach to suspended sediment modeling. Journal of Hydrology, 351: 288–298. Dehghani, A. A., Zanganeh, M. A., Mosaedi, A. and Kohestani, N. (2008) Comparison of suspended load estimation using two methods of sedimentation curve and artificial neural network. Journal of Agricultural Sciences and Natural Resources, 16: 36-51. (In Persian). Duan, W.L.; He, B.; Takara, K.; Luo, P.P.; Nover, D. and Hu, M.C. (2015). Modeling suspended sediment sources and transport in the Ishikari River basin, Japan, using SPARROW, Hydraulic Earth Systems Sciences, Eder, A.P.; Strauss, T.; Krueger, B.; 1and, J.N. and Quinton, B. (2010). A Comparative calculation of suspended sediment loads with respect to hysteresis effects (in the Petzenkirchen catchment), Austria, Journal of Ferreira C. 2001. Algorithm for solving gene expression programming: a new adaptive problems. Complex Systems, 13(2): 87-129. Jain, A., and Kumar, A.M. 2007. Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing. 7(2). 585-592. https://doi.org/10.1016/j.asoc.2006.03.002 Jalali, M., Soleimani, K., Mujadadi, H. And Omidhar, A. 2007 estimation of the suspended sediment load of Abloon-Nakaroud hydrometric station using sediment gauge curve and artificial neural network, the 4th National Conference of Iran Watershed Science and Engineering, 1 March 1386, Karaj, (In Persian). Jang, J.S.R. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, 23(3): 665-685. Keshtegar, B., Piri, J., Hussan, W.U., Ikram, K., Yaseen, M., Kisi, O., Adnan, R.M., Adnan, M. & Waseem, M. (2023) Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model. Journal of Water 2023, 15, 1437. https://doi.org/10.3390/w15071437 Kisi, O. and Shiri, J. (2012). River Suspended Sediment Estimation by Climate Variables Implication: Comparative Study among Soft Computing Techniques. Computer and Geosciences, 43: 73-82. Kisi, O., Yuksel, I., Dogan, E., (2008). Modelling daily suspended sediment of rivers in Turkey using several datadriven techniques. Hydrological Sciences Journal. 53 (6), 1270–1285. https://doi.org/10.1623/hysj.53.6.1270 Kissi O., A. Hosseinzadeh Dalir, M. Cimen and J. Shiri. 2012. Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology,450(1): 48-58. Mehrizi Haeri, AA, (2012) Data mining: concepts, methods and applications. Master's thesis in economic and social statistics, Faculty of Economics, Allameh Tabatabai University. (In Persian). Moeiri, M.M., Nikpour, M.R., Hosseinzadeh Delir, A. and Farsadizadeh, 2008. Comparison of Artificial Neural Networks, Fuzzy-Adaptive Neural Networks and Sediment Gauge Curve Methods in River Suspended Sediment Estimation (Case Study: Aji River- tea). Water and Soil Science Journal, (20)2: 71-82. (In Persian). Mosaedi, A., Zanganeh, M.A., Miftah, M., Dehghani, A.A.and Khoshrosh, M.(2008) Evaluation of hydrological methods of suspended load estimation. (Case study: Atrak River, Golestan province).The 10th National Seminar on Irrigation and Evaporation Reduction, 19 to 21 Bahman.Kerman. (In Persian). Nikpour, M. R.and Sani Khani, H.(2016) Modeling of river suspended sediments using soft calculations (Darah-Rood River).Irrigation and water engineer's scientific and research quarterly.eighth yearNo. 30:29-44. (In Persian). Nourani, V. 2009. Using Artificial Neural Network (ANNs) For Sediment Load Forecasting of Talkherood River Nourani, V., Gokcekus, H., & Gelete, G. (2020). Estimation of suspended sediment load using artificial intelligence-based ensemble model.Complexity, 1-19. Onderka, M.; Krein, A. and Wrede, S. (2012). Dynamics of storm-driven suspended sediments in headwater catchment described by multivariable modeling, Journal of Soils Sediments, 12: 620-635. Rahul, A.K., Shivhare, N., Kumar, S., Dwivedi, S.B., & Dikshit, P.K.S. (2021). Modelling of daily suspended sediment concentration using FFBPNN and SVM algorithms. Journal of Soft Computing in Civil Engineering, 5(2),120-134. Rajaee, T., Mirbagheri, S. A., Kermani, M. Z., Nourani, V. (2009) Daily Suspended Sediment Concentration Simulation Using ANN and Neuro-Fuzzy Models, Science of the Total Environment, 407 (17), pp. 4916-4927. Rezazadeh Jodi, A. and Sattari, M. (2014). Estimation of the depth of the washout hole of the bridge foundation in river structures with the Gaussian process regression method. Applied Research Journal of Irrigation and Drainage Structures Engineering, 16 (65) 19-36. (In Persian). Russel S.O. and P.F. Campbell. 1996. Reservoir operating rules with fuzzy programming. Journal of Water Resources Planning and Management, 122 (3): 165–170. Sattari, M., Rezazade Jodi, A., Safdari, F., & Kahramanzadeh, F. (2016). Performance evaluation of M5 tree model and support vector regression methods in river suspended sediment modeling. Journal of Water and Soil Sheikh Alipour, Z., Hasanpour, F. and Azimi, A. (2014). Comparison of artificial intelligence methods in estimating suspended sediment load (case study: Sistan River). Water and Soil Conservation Research, (2) 7, pp. 41-60. (In Persian). Shojaeezadeh, S.A., Nikoo, M.R., McNamara, J.P., AghaKouchak, A., & Sadegh, M. (2018). Stochastic modeling of suspended sediment load in alluvial rivers. Advances in Water Resources, 119, 188-196. (In Persian). Vapnik, V., and Cortes, C. 1995. Support vector networks. Machine Learning. 20: 273-297. | ||
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