سال 15، شماره 1 - ( بهار 1404 )
جلد 15 شماره 1 صفحات 104-83 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mohammadi-Raigani Z, Gholami H, Mohamadi M. Evaluating the Performance of Machine Learning Models for Predicting Suspended Sediment Load (case study: Taleghan watershed, Iran). E.E.R. 2025; 15 (1) :83-104
URL: http://magazine.hormozgan.ac.ir/article-1-867-fa.html
URL: http://magazine.hormozgan.ac.ir/article-1-867-fa.html
محمدی رایگانی زینب، غلامی حمید، محمدی مجتبی. ارزیابی عملکرد مدل‌های یادگیری ماشین در پیش‌بینی بار رسوب معلق (مطالعه موردی: حوضه آبخیز طالقان). پژوهش هاي فرسايش محيطي. 1404; 15 (1) :83-104
گروه مهندسی منابع طبیعی، دانشگاه هرمزگان، بندرعباس ، hadesert64@gmail.com
چکیده: (890 مشاهده)
مدلسازی و پیش‌بینی بار رسوب معلق (SSL) یک موضوع مهم در مدیریت یکپارچه محیطی و منابع آب است، زیرا رسوب بر کیفیت آب و زیستگاه‌های آبی تأثیر می‌گذارد. از سوی دیگر، کمی‌سازی و درک تعاملات غیرخطی در دینامیک رسوب همواره به عنوان یک چالش اساسی مطرح بوده است. هدف از این مطالعه پیش‌بینی بار رسوب معلق روزانه در حوضه طالقان در شمال غرب تهران با بکارگیری و مقایسه عملکرد شش مدل یادگیری ماشین (ML) شامل Cforest، Ctree، Cubist، LASSO، qrnn و xgbTree بود. داده‌های ده متغیر ورودی (دبی، رسوب روزانه، بارش، بارش تجمعی، شاخص برف، دبی و رسوب با تاخیر زمانی یک روزه (t-1)) از سال 2000 تا 2018 برای آموزش و آزمایش (به ترتیب 75 درصد آموزش و 25 درصد آزمایش)، مدل‌های ML استفاده شد. همچنین تکنیک آنالیز مؤلفههای اصلی (PCA) برای کاهش تعداد متغیرهای ورودی بکار گرفته شد. عملکرد مدلها برای پیش‌بینی SSL با استفاده از چندین معیار کمی و گرافیکی، از جمله ریشه میانگین مربعات خطا (RMSE)، ضریب نش-ساتکلیف (NSE) و میانگین خطای مطلق (MAE)، نمودار تیلور، نمودار تغییرات زمانی و نمودار پراکندگی ارزیابی شد. مقایسه دقت پیش‌بینی مدلها نشان داد که الگوریتمهای ML می‌توانند SSL روزانه را به‌طور رضایت‌بخش پیش‌بینی کنند، به‌ویژه مدل‌های xgbTree (63/301RMSE= : 97/0NSE= )، Cubist (46/330RMSE= : 96/0NSE= ) و qrnn (85/349RMSE= : 96/0NSE= )، که کمترین خطای پیش‌بینی و بالاترین معیارهای کارایی را نشان دادند. علاوه بر این، نمودار تیلور تأیید کرد که مدل‌های xgbTree، Cubistو qrnn بهترین تطابق بین مقادیر مشاهده شده و پیش‌بینی‌شده را برای پارامترهای هیدرولیکی مختلف به دست آوردند. این نتایج حاکی از آن است که عملکرد مدل‌های یادگیری ماشین به عنوان یک روش مناسب برای پیش‌بینی و تحلیل دینامیک رسوب در حوضه‌های آبخیز است.
نوع مطالعه: مستخرج از پایان‌نامه / رساله / طرح پژوهشی |
موضوع مقاله:
مدلسازی و تحلیل زمانی و مکانی رخداد انواع مختلف فرسایش محیطی
دریافت: 1403/8/27 | انتشار: 1404/1/3
دریافت: 1403/8/27 | انتشار: 1404/1/3
فهرست منابع
1. Abrahart, R.J., White, S.M., 2001. Modeling sediment transfer in Malawi: comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets. Physics and Chemistry of the Earth B 26 (1), 19-24. [DOI:10.1016/S1464-1909(01)85008-5]
2. Alp, M., Cigizoglu, H.K., 2007. Suspended sediment estimation by feed forward back propagation method using hydro meteorological data. Environmental Modelling & Software 22 (1), 2-13. [DOI:10.1016/j.envsoft.2005.09.009]
3. Bou-Fakhreddine, B., Mougharbel, I., Faye, A., Abou Chakra, S., Pollet, Y., 2018. Daily river flow prediction based on Two-Phase Constructive Fuzzy Systems Modeling: A case of hydrological - meteorological measurements asymmetry. J. Hydrol. 558, 255-265.
https://doi.org/10.1016/j.jhydrol.2018.01.035 [DOI:10.1016/j.jhydrol.2018.01.035.]
4. Chiang, J.-L., Tsai, Y.-S., )2011(. Suspended sediment load estimate using support vector machines in Kaoping river basin, in: Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference On. pp. 1750-1753. [DOI:10.1109/CECNET.2011.5769267]
5. Choubin, Bahram, Darabi, Hamid, Rahmati, Omid, Sajedi-Hosseini, Farzaneh, & Kløve, Bjørn. (2018). River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Science of The Total Environment, 615, 272-281. doi:
https://doi.org/10.1016/j.scitotenv.2017.09.293 [DOI:10.1016/j.scitotenv.2017.09.293.]
6. Çimen, M., 2016. Estimation of daily suspended sediments using support vector machines. Hydrol. Sci. J. 6667.
7. Cobaner, M., Unal, B., Kisi, O., )2009(. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydrometeorological data. J. Hydrol. 367, 52-61. [DOI:10.1016/j.jhydrol.2008.12.024]
8. Darabi, H., Mohamadi, S., Karimidastenaei, S., Kisi, S., Ehteram, M., ELShafie, A., Torabi Haghighi, A., 2021. Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models. Soft Computing (2021) 25:7609-7626,
https://doi.org/10.1007/s00500-021-05721-5 [DOI:10.1007/s00500-021-05721-5.]
9. Dawson, Christian W, Abrahart, Robert J, & See, Linda M. (2007). HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling & Software, 22(7), 1034-1052. [DOI:10.1016/j.envsoft.2006.06.008]
10. Diop, L., Bodian, A., Djaman, K., Yaseen, Z.M., Deo, R.C., El-shafie, A., Brown, L.C., )2018(. The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River. Environ. Earth Sci. 77, 182.
https://doi.org/10.1007/s12665-018-7376-8 [DOI:10.1007/s12665-018-7376-8.]
11. Gholami, Hamid, Mohammadifar, Aliakbar, Golzari, Shahram, Kaskaoutis, Dimitris G, & Collins, Adrian L. (2021 b). Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran. Aeolian Research, 50, 100682. [DOI:10.1016/j.aeolia.2021.100682]
12. Ghorbani, M.A., Deo, R.C., Yaseen, Z.M., H. Kashani, M., Mohammadi, B., 2017. Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor. Appl. Climatol. [DOI:10.1007/s00704- 017-2244-0]
13. Haghighi AT, Darabi H, Shahedi K, Solaimani K, Kløve B (2019) A scenario-based approach for assessing the hydrological impacts of land use and climate change in the Marboreh Watershed. Iran. Environ Model Assess 25:1-17. [DOI:10.1007/s10666-019-09665-x]
14. Hassanpour, F., et al., 2019. Development of the FCM-SVR hybrid model for estimating the suspended sediment load. KSCE Journal of Civil Engineering, 23 (6), 2514-2523. doi:10.1007/s12205-019-1693-7. [DOI:10.1007/s12205-019-1693-7]
15. Hosseini, Majid, Ghafouri, A., Amin, M., Tabatabaei, Mohsen, Goodarzi, Massoud, & Abdeh Kolahchi, Abdolnabi. (2012). Effects of Land Use Changes on Water Balance in Taleghan Catchment, Iran. Journal of Agricultural Science and Technology, 14, 1159-1172.
16. Hothorn, T., Hornik, K., & Zeileis, A. (2015). ctree: Conditional inference trees. The comprehensive R archive network, 8, 1-34.
17. Houborg, R., & McCabe, M. F. (2018). A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 173-188. [DOI:10.1016/j.isprsjprs.2017.10.004]
18. Idrees, M. B., Jehanzaib, M., Kim, D., Kim, T.D., 2021. Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir. Stochastic Environmental Research and Risk Assessment
https://doi.org/10.1007/s00477-021-01982-6 [DOI:10.1007/s00477-021-01982-6(0123456789().,-volV)(0123456789,-().volV).]
19. Khan, A.I., Topping, B.H.V., Bahreininejad, A., 1993. Parallel training of neural networks for finite element mesh generation. In: Topping, B.H.V., Khan, A.I. (Eds.), Neural Networks and Combinatorial Optimization in Civil&Structural Engineering. Civil-Comp Press, Edinburgh, pp. 81-94. [DOI:10.4203/ccp.16.5.1]
20. Khosravi, Khabat, Mao, Luca, Kisi, Ozgur, Yaseen, Zaher Mundher, & Shahid, Shamsuddin. (2018). Quantifying hourly suspended sediment load using data mining models: Case study of a glacierized Andean catchment in Chile. Journal of Hydrology, 567, 165-179. doi:
https://doi.org/10.1016/j.jhydrol.2018.10.015 [DOI:10.1016/j.jhydrol.2018.10.015.]
21. Kisi, Ozgur, Dailr, Ali Hosseinzadeh, Cimen, Mesut, & Shiri, Jalal. (2012). Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology, 450-451, 48-58. doi:
https://doi.org/10.1016/j.jhydrol.2012.05.031 [DOI:10.1016/j.jhydrol.2012.05.031.]
22. Lafdani, E Kakaei, Nia, A Moghaddam, & Ahmadi, A. (2013). Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology, 478, 50-62. [DOI:10.1016/j.jhydrol.2012.11.048]
23. Li, Y., Wang, Z., Han, R., Shi, S., Li, J., Shang, R., ... & Gu, Y. (2023). Quantum recurrent neural networks for sequential learning. Neural Networks, 166, 148-161. [DOI:10.1016/j.neunet.2023.07.003]
24. Liu, Qian-Jin, Shi, Zhi-Hua, Fang, Nu-Fang, Zhu, Hua-De, & Ai, Lei. (2013). Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the Wavelet-ANN approach. Geomorphology, 186, 181-190. doi:
https://doi.org/10.1016/j.geomorph.2013.01.012 [DOI:10.1016/j.geomorph.2013.01.012.]
25. Lu H, Meng Y, Yan K, Gao Z (2019) Kernel principal component analysis combining rotation forest method for linearly inseparable data. Cogn Syst Res 53:111-122 [DOI:10.1016/j.cogsys.2018.01.006]
26. Luo, Y., Xue, Y., Liu, W., Song, H., Huang, Y., Tang, G., ... & Sun, Z. (2022). Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection. BMC Infectious Diseases, 22(1), 965. [DOI:10.1186/s12879-022-07954-7]
27. Ma, J., Yu, Z., Qu, Y., Xu, J., & Cao, Y. (2020). Application of the XGBoost machine learning method in PM2. 5 prediction: a case study of Shanghai. Aerosol and Air Quality Research, 20(1), 128-138. [DOI:10.4209/aaqr.2019.08.0408]
28. Masters, Timothy. (1993). Practical neural network recipes in C++: Morgan Kaufmann. [DOI:10.1016/B978-0-08-051433-8.50017-3]
29. Mehri, Y., Nasrabadi, M., Omid, M.H., (2021). Prediction of suspended sediment distributions using data mining algorithms. Ain Shams Engineering Journal,
https://doi.org/10.1016/j.asej.2021.02.034 [DOI:10.1016/j.asej.2021.02.034.]
30. Melesse, A. M., Ahmad, S., McClain, M. E., Wang, X., & Lim, Y. H. (2011). Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management, 98(5), 855-866. doi:
https://doi.org/10.1016/j.agwat.2010.12.012 [DOI:10.1016/j.agwat.2010.12.012.]
31. Melesse, A., Jayachandran, K., Zhang, K., 2008. Modeling coastal eutrophication at Florida Bay using neural networks. Journal of Coastal Research 24, 190-196. [DOI:10.2112/06-0646.1]
32. MixSIR model. CATENA 164, 32-43.
33. Muthukrishnan, R., & Rohini, R. (2016, October). LASSO: A feature selection technique in predictive modeling for machine learning. In 2016 IEEE international conference on advances in computer applications (ICACA) (pp. 18-20). Ieee. [DOI:10.1109/ICACA.2016.7887916]
34. Nadiri, A.A., Gharekhani, M., Khatibi, R., Sadeghfam, S., Moghaddam, A.A., )2017(. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Sci. Total Environ. 574, 691-706.
https://doi.org/10.1016/j.scitotenv.2016.09.093 [DOI:10.1016/j.scitotenv.2016.09.093.]
35. Nagy, H.M., Watanabe, K., Hirano, M., 2002. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering 128 (6), 588-595. [DOI:10.1061/(ASCE)0733-9429(2002)128:6(588)]
36. Noori, R., Sabahi, M.S., Karbassi, A.R., Baghvand, A., Taati Zadeh, H., 2010d. Multivariate statistical analysis of surface water quality based on correlations and variations in the data set. Desalination 260, 129-136. [DOI:10.1016/j.desal.2010.04.053]
37. Nosrati, K., Collins, A.L., Madankan, M., 2018a. Fingerprinting sub-basin spatial sediment sources using different multivariate statistical techniques and the Modified [DOI:10.1016/j.catena.2018.01.003]
38. Nosrati, Kazem, Mohammadi-Raigani, Zeinab, Haddadchi, Arman, & Collins, Adrian L. (2021). Elucidating intra-storm variations in suspended sediment sources using a Bayesian fingerprinting approach. Journal of Hydrology, 596, 126115. doi:
https://doi.org/10.1016/j.jhydrol.2021.126115 [DOI:10.1016/j.jhydrol.2021.126115.]
39. Pourghasemi, H.R., Yousefi, S., Kornejady, A., Cerda, A., (2017). Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci. Total Environ. 609, 764-775. [DOI:10.1016/j.scitotenv.2017.07.198]
40. Rahul, Atul Kumar, Shivhare, Nikita, Kumar, Shashi, Dwivedi, Sumita, & Dikshit, P. K. S. (2021). Modelling of Daily Suspended Sediment Concentration Using FFBPNN and SVM Algorithms.
41. Raigani, Zeinab Mohammadi, Nosrati, Kazem, & Collins, Adrian L. (2019). Fingerprinting sub-basin spatial sediment sources in a large Iranian catchment under dry-land cultivation and rangeland farming: Combining geochemical tracers and weathering indices. Journal of Hydrology: Regional Studies, 24, 100613. doi:
https://doi.org/10.1016/j.ejrh.2019.100613 [DOI:10.1016/j.ejrh.2019.100613.]
42. Rajaee, T., Nourani, V., Zounemat-Kermani, M., Kisi, O., (2011). River suspended sediment load prediction: application of ANN and wavelet conjunction model. J. Hydrol. Eng. 16 (8), 613-627. [DOI:10.1061/(ASCE)HE.1943-5584.0000347]
43. Ren J, Zhao M, Zhang W, Xu Q, Yuan J, Dong B (2020) Impact of the construction of cascade reservoirs on suspended sediment peak transport variation during flood events in the Three Gorges Reservoir. CATENA 188:104409. [DOI:10.1016/j.catena.2019.104409]
44. Shojaeezadeh SA, Nikoo MR, McNamara JP, AghaKouchak A, Sadegh M (2018). Stochastic modeling of suspended sediment load in alluvial rivers. Adv Water Resour 119:188-196. [DOI:10.1016/j.advwatres.2018.06.006]
45. Solomatine, D.P., Torres, L.A., 1996. Neural network approximation of a hydrodynamicmodel. In: Optimizing Reservoir Operation, Proceedings of the Second International Conference on Hydroinformatics , Zurich, pp. 201-206.
46. Tao, H., Diop, L., Bodian, A., Djaman, K., Ndiaye, P.M., Yaseen, Z.M., 2018. Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso. Agric. Water Manag. [DOI:10.1016/j.agwat.2018.06.018]
47. Taylor, Karl E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192. doi: [DOI:10.1029/2000JD900719]
48. Tokar, A. Sezin, & Johnson, Peggy A. (1999). Rainfall-Runoff Modeling Using Artificial Neural Networks. Journal of Hydrologic Engineering, 4(3), 232-239. doi: doi:10.1061/(ASCE)1084-0699(1999)4:3(232) [DOI:10.1061/(ASCE)1084-0699(1999)4:3(232)]
49. Wen, C.G., Lee, C.S., )1998(. A neural network approach to multi-objective optimization for water quality management in a river basin. Water Resources Research 34 (3), 427-436. [DOI:10.1029/97WR02943]
50. Yang CT, Marsooli R, Aalami MT (2009). Evaluation of total load sediment transport formulas using ANN. Int J Sedim Res 24(3):274-286. [DOI:10.1016/S1001-6279(10)60003-0]
51. Yang, C.C., Prasher, S.O., Tan, C.S., 1998. An artificial neural network model for water table management systems. In: Drainage in the 21st Century: Food Production and the Environment. Proceedings of the Seventh International Drainage Symposium, Orlando, Florida, USA, 8-10 March 1998, ASAE, St. Joseph, MI.
52. Yu, H., Wen, X., Feng, Q., Deo, R.C., Si, J., Wu, M., 2018. Comparative Study of Hybrid- Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China. Water Resour. Manag.
https://doi.org/10.1007/s11269-017-1811-6 [DOI:10.1007/s11269-017-1811-6.]
ارسال پیام به نویسنده مسئول
| بازنشر اطلاعات | |
 |
این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
