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پیشبینی آبدهی رود فریزی با بهرهگیری از محاسبههای نرم | ||
| پژوهش های آبخیزداری | ||
| مقاله 7، دوره 36، شماره 4 - شماره پیاپی 141، دی 1402، صفحه 81-97 اصل مقاله (2.51 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2023.360554.1504 | ||
| نویسندگان | ||
| صابر جمالی1؛ فرشته رحیمی آغ چشمه2؛ محمد جواد امیری* 3 | ||
| 1دانشجوی دکتری، گروه علوم و مهندسی آب، دانشکده ی کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| 2دانشجوی دکتری، گروه مرتع و آبخیزداری، دانشکده ی محیطزیست و منابعطبیعی، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| 3دانشیار، گروه علوم و مهندسی آب، دانشکده ی کشاورزی، دانشگاه فسا، فسا، ایران | ||
| چکیده | ||
| مقدمه و هدف پیشبینی دقیق جریان رود بهعنوان یک منبع مهم آب شیرین روی زمین، در مهندسی و مدیریت منابع آب ضروری است؛ از این رو، توسعهی فنآوری که آبدهی رود را پیشبینی کند، ضروری است. در این زمینه مدلهای مختلفی بهوسیلهی پژوهشگران پرشماری ارائهشده است. این مدل ها به دو دستهی مدلهای فیزیکی مبتنی بر اصول آبشناسی/آبی و مدلهای مبتنی بر محاسبههای نرم تقسیم میشوند. تمام مقالههای چاپشده شواهدی از اهمیت کاربرد مدلهای مبتنی بر محاسبههای نرم برای مشکلات آبشناختی، بهویژه آبدهی رود هستند. مواد و روشها در این پژوهش برای پیشبینی آبدهی رود فریزی از مدل برنامهریزی بیان ژن (GEP) و ماشین بردار پشتیبان (SVM) استفاده شد. در این پژوهش، از گروه روزانه آبدهی در ده سال (1399-1390) مربوط به ایستگاه آبسنجی موشنگ استفاده شد. میانگین بلندی منطقهی بررسیشده 2171 متر از سطح دریا با طول جغرافیایی "30 '49 °58 تا "30 '4 °58 شرقی و عرض جغرافیایی "1 '20 °36 تا "1 '32 °36 است. از دادههای آبدهی روزانه رود از 1 تا 5 روز قبل بهعنوان ورودی مدلهای GEP و SVM استفاده شد. بهمنظور اطمینان از همگنی و تکمیل کمبود دادههای آبدهی استفادهشده از آزمون ران و ضریب همبستگی میان ایسـتگاههای همجوار اسـتفاده شد. سپس دادهها بهشکل تصادفی به دو بخش، 80% برای آموزش و 20% بـرای آزمون و تعیین خطای مدلسازی تفکیک شدند. در مراحل آموزش و اعتبارسنجی براساس ریشهی میانگین مربعات خطا (RSME)، ضریب همبستگی (R)، اریبی مدل، کارایی مدل کلینگ گوپتا (KGE) و نش-ساتکلیف (NSE) عملکرد مدل بررسی شد. در این پژوهش بهمنظور برآورد جریان ورودی به رود فریزی با کاربرد مدل SVM، سه نوع تابع کرنل رایج در آبشناسی شامل تابعهای پایهی خطی، چند جملهای و شعاعی بررسی شد. نتایج و بحث از میان تابعهای گوناگون، تابع مبنای شعاعی بهدلیل داشتن کمترین اندازهی خطا برای متغیرها انتخاب شد. بهتـرین الگوی ورودی، الگوی شمارهی 5 بود که در آن متغیرهای آبدهی پیشین با پنج گام زمانی تأخیر استفاده شد، و در مرحلهی آموزش در مـدل GEP و SVM بهترین عملکرد را در پیشبینی آبدهی روزانهی ایسـتگاه موشنگ داشت. عملکرد مدل اعمالشده نشان داد که SVM (RSME = 1.15، R = 0.985، NSE = 0.85 و KGE = 0.79) در مرحلهی اعتبارسنجی برای پیشبینی آبدهی روزانهی رود از مدل GEP (RSME = 1.65، R = 0.964، NSE = 0.78 و KGE = 0.69) دقیقتر است. نتیجه گیری و پیشنهادها این پژوهش نشان داد که روش محاسبههای نرم (مانند SVM و GEP)، ابزار قدرتمندی در پیشبینی جریان رود است. با کاربرد این مدلها میتوان میان سنجههای ورودی و خروجی رابطهی مطلوب ایجاد کرد و امکان شبیهسازی دقیق جریان میانگین و حداکثر روزانه را فراهم ساخت. | ||
| کلیدواژهها | ||
| آبخیز فریزی؛ بیان ژن؛ رواناب؛ محاسبههای نرم؛ هوش مصنوعی | ||
| عنوان مقاله [English] | ||
| Prediction of Ferizi River-Flow Using Data-Driven Models | ||
| نویسندگان [English] | ||
| Saber Jamali1؛ Fereshte Rahimi Aghcheshme2؛ Mohammad Javad Amiri3 | ||
| 1PhD Candidate, Department of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 2Ph.D. Candidate, Department of Range and Watershed management, Faculty of Natural Resource and Environment, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 3Associate Professor, Department of Water Science and Engineering, Faculty of Agriculture, Fasa University, Fasa, Iran | ||
| چکیده [English] | ||
| Introduction and Objective Rivers serve as crucial sources of freshwater on Earth, and precise prediction of river flows plays a vital role in effective water resource management. To address this need, researchers have proposed various models. These models can be categorized into two types: (1) hydrological/hydraulic physically based models and (2) data-driven models. The cited papers provide evidence of the significance of employing data-driven models for hydrological issues, particularly in predicting river discharge. Materials and Methods In this study, the researchers utilized a gene expression programming model (GEP) and support vector machine (SVM) to predict the discharge of the Ferizi River. The dataset used in the study covered a period of ten years (2011-2020) and consisted of daily Ferizi river discharge readings obtained from the Moushang hydrometry station. The study area had an average altitude of 2171 meters above sea level, with a longitude range of 58° 49' 30" to 59° 4' 30" E and a latitude range of 36° 20' 1" to 36° 32' 1". For the prediction models, daily river discharge data from 1 to 5 days ahead were utilized as input variables for both the GEP and SVM models. To ensure data homogeneity and address any deficiencies, the researchers employed the run test and calculated the correlation coefficient between neighboring stations. Subsequently, the dataset was randomly divided into two groups: 80% for model training and 20% for model testing, as well as evaluating the modeling error. The performance of the models was assessed during the training and validation stages using various evaluation metrics, including root mean square error (RMSE), coefficient of correlation (R), bias, Kling Gupta (KGE), and Nash–Sutcliffe model efficiency (NSE). Additionally, in order to estimate the inflow to the Ferizi River using the SVM model, three common types of kernel functions in hydrology were examined, namely linear kernel, polynomial, and radial basis functions. Results and Discussion Among the different functions considered, the radial basis function was selected due to its lower error compared to other functions when applied to the variables. The best input model, Model No. 5, incorporated previous flow variables with a delay of five time steps. In the training phase, both the GEP and SVM models exhibited the highest performance in forecasting the daily flow of Moshang station. The performance of the applied models suggests that SVM (RSME = 1.15, R = 0.985, NSE = 0.85, and KGE = 0.79) demonstrates higher precision in the validation stage for river discharge prediction compared to GEP (RSME = 1.65, R = 0.964, NSE = 0.78, and KGE = 0.69). Conclusion and Suggestions This study has demonstrated that the utilization of soft computing techniques, such as SVM and GEP, is a powerful tool in predicting river flow. These techniques are capable of establishing a favorable relationship between input and output parameters, enabling accurate simulation of average and maximum daily flow. | ||
| کلیدواژهها [English] | ||
| Artificial intelligence, Ferizi watershed, gene expression, runoff, soft computing | ||
| مراجع | ||
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Abdollahi S, Raeisi J, Khalilianpour M, Ahmadi F, Kisi O. 2017. Daily mean stream flow prediction in perennial and non-perennial rivers using four data driven techniques. Water Resources Management. 31(15): 4855-4874. https://doi.org/10.1007/s11269-017-1782-7. Ahmadi F, Radmanesh F, Mirabbasi R. 2016. Comparison between genetic programming and support vector machine methods for daily river flow forecasting (Case study: Barandoozchay River). Water and Soil. 28(6): 1162-1171. (In Persian). https://doi.org/10.22067/jsw.v0i0.32406. Ahmadyousefi S, Bahremand A, Sheikh V, Komaki CB. 2019. Determining the snow coefficient in order to simulate the snow melting in the Shemshak watershed using the WetSpa model. Iranian Journal of Watershed Management Science. 13(47): 1-8. (In Persian). https://doi.org/20.1001.1.20089554.1398.13.47.3.1 Alinezhadi M, Mousavi SF, Hosseini K. 2021. Comparison of gene expression programming (GEP) and parametric and non-parametric regression methods in the prediction of the mean daily discharge of Karun river (A case study: Mollasani hydrometric station). Journal of Water and Soil Science. 25(1): 43-62. (In Persian). https://doi.org/10.47176/jwss.25.1.1012 Azinmehr M, Bahremand A, Kabir A. 2016. Parameter sensitivity and uncertainty analysis of the model WetSpa in the flow hydrograph simulation using PEST in Dinvar Basin Karkheh. Journal of Watershed Management Research. 7(1): 82-72. (In Persian). https://doi.org/10.18869/acadpub.jwmr.7.13.82. Bahremand A, Corluy J, Liu Y, De Smedt F, Poórová J, Velcická L. 2005. Stream flow simulation by WetSpa model in Hornad river basin Slovakia. Floods from defence to management. London: Taylor-Francis Group. pp. 67-74. Bayazidi M, Asadzadeh F, Kaki M. 2018. The application of intelligent techniques for predicting daily flow at Telvar basin river. Iranian journal of Ecohydrology. 5(1): 203-213. (In Persian). https://doi.org/10.22059/ije.2018.223015.386. Behmanesh J, Mostafavi S, Zamanzad Ghavidel S. 2017. Use of soft calculations at estimation and prediction of environmental flow discharge (Case study: Khorkhoreh Chay river). Journal of Civil and Environmental Engineering. 47(3): 9-22. (In Persian). Dehghani R, Yonesi H, Torabi Poudeh H. 2017. Comparing the performance of support vector machines gene expression programming and bayesian networks in predicting river flow (Case study: Kashkan River). Journal of Water and Soil Conservation. 24(4): 161-177. (In Persian). https://doi.org/10.22069/jwfst.2017.12398.2701. Dehghani R, Torabi H, Younesi H, Shahinejad B. 2021. Application of wavelet support vector machine (WSVM) model in predicting river flow (Case study: Dez Basin). Watershed Engineering and Management. 13(1):98-110. (In Persian). .https://doi.org/10.22092/ijwmse.2020.128735.1748. Delafrouz H, Ghaheri A, Ghorbani MA. 2018. A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction. Soft Computing. 22(7):2205-2215. https://doi.org/10.1007/s00500-016-2480-8. Fatemi SE, Darabi Cheghabaleki S, Hafezparast M. 2022. The effect of preprocessing and reducing the input dimensions of the flow prediction model on optimized support vector regression by genetic algorithm. Advanced Technologies in Water Efficiency. 1(1): 24-47. (In Persian). https://doi.org/10.22126/atwe.2021.6660.1002. Ghorbani MA, Khatibi R, Goel A, FazeliFard MH, Azani A. 2016. Modeling river discharge time series using support vector machine and artificial neural networks. Environmental Earth Sciences. 75(8): 1-13. https://doi.org/10.1007/s12665-016-5435-6. Ghorbani MA, Khatibi R, Karimi V, Yaseen ZM, Zounemat-Kermani M. 2018. Learning from multiple models using artificial intelligence to improve model prediction accuracies: application to river flows. Water Resources Management. 32(13): 4201-4215. https://doi.org/10.1007/s11269-018-2038-x. Hussain D, Khan AA. 2020. Machine learning techniques for monthly river flow forecasting of Hunza River Pakistan. Earth Science Informatics. 13(3): 939-949. https://doi.org/10.1007/s12145-020-00450-z. Isazadeh M, Biazar S, Ashrafzadeh A, Khanjani R. 2019. Estimation of aquifer qualitative parameters in Guilans Plain using gamma test and support vector machine and artificial neural network models. Journal of Environmental Science and Technology. 21(2):1-21. (In Persian) https://doi.org/10.22034/jest.2019.13946. Khashei-Siuki A, Sarbazi M. 2015. Evaluation of ANFIS ANN and geostatistical models to spatial distribution of groundwater quality (Case study: Mashhad Plain in Iran). Arabian Journal of Geosciences. 8:903–912. https://doi.org/10.1007/s12517-013-1179-8. Khodakhah H, Aghelpour P, Hamedi Z. 2022. Comparing linear and non-linear data-driven approaches in monthly river flow prediction based on the models SARIMA LSSVM ANFIS and GMDH. Environmental Science and Pollution Research. 29(15): 21935-21954. https://doi.org/10.1007/s11356-021-17443-0 Nivesh S, Negi D, Kashyap PS, Aggarwal S, Singh B, Saran B, Sihag P. 2022. Prediction of river discharge of Kesinga sub-catchment of Mahanadi basin using machine learning approaches. Arabian Journal of Geosciences. 15(16): 1-19. https://doi.org/10.1007/s12517-022-10555-y. Nozari H, Tavakoli F. 2018. Stream flow prediction using support vector machine based on discharge and precipitation time series on upstream stations (Case study: Taleh Zang hydrometric station). Modeling in Engineering. 16(54): 95-104. (In Persian) https://doi.org/10.22075/jme.2017.11363.1112. Pandhiani SM, Sihag P, Shabri AB, Singh B, Pham QB. 2020. Time-series prediction of stream flows of Malaysian rivers using data-driven techniques. Journal of Irrigation and Drainage Engineering. 146(7): 04020013. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001463. Rahimi B, Hafezparast Mavaddat M. 2020. Comparison of SVM GEP and IHACRES models in prediction of runoff changes due to climate change (Case study: Jamishan Dam). Iranian Journal of Soil and Water Research. 51(10): 2483-2499. (In Persian). https://doi.org/10.22059/ijswr.2020.303779.668640. Sahoo A, Samantaray S, Ghose DK. 2021. Prediction of flood in Barak River using hybrid machine learning approaches: a case study. Journal of the Geological Society of India. 97(2):186-198. https://doi.org/10.1007/12594-021-1650-1. Salarijazi M, Ghorbani K, Sohrabian E, Abdolhosseini M. 2016. Prediction of daily stream-flow using data driven models. Iranian Journal of Irrigation and Drainage. 10(4):479-488. (In Persian). Samadi M, Fathabadi A. 2019. Application of time series ANN and SVM models in forecasting the Gorgan dam inflow rate. Environment and Water Engineering. 4(4): 299-309. (In Persian). https://doi.org/10.22034/jewe.2018.128256.1256. Sattari M, Rezazadeh A, Safdari F, Ghahramanian F. 2016. Performance evaluation of M5 tree model and support vector regression methods in suspended sediment load modeling. Water and Soil Resources Conservation. 6(1): 109-124. (In Persian). Shaofu M, Al-Juboori AM, Alwan AH, Abdel-Salam ASG. 2021. On the investigation of monthly river flow generation complexity using the applicability of machine learning models. Complexity. pp. 1-14. https://doi.org/10.1155/2021/3721661 Sharifi Garmdareh E, Vafakhah M, Eslamian S. 2019. Assessment the performance of support vector machine and artificial neural network systems for regional flood frequency analysis (a case study: Namak Lake watershed). Journal of Water and Soil Science. 23(1): 351-366. (In Persian). https://doi.org/10.29252/jstnar.23.1.26. Solgi A, Zareie H, Golabi M. 2017. Performance assessment of gene expression programming model using data preprocessing methods to modeling river flow. Journal of Water and Soil Conservation. 24(2):185-201. (In Persian) https://doi.org/10.22069/jwfst.2017.11353.2573. Taylor KE. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres. 106(D7): 7183-7192. https://doi.org/10.1029/2000JD900719. Zeinalie M, Golabi M, Niksokhan M, Sharifi M. 2020. Modeling daily river flow using simulator meta-models (Case study: Gamasiab River). Journal of Environmental Science and Technology. 22(4): 121-133. (In Persian). https://doi.org/ 10.22034/jest.2020.33087.4089. | ||
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