تعداد نشریات | 44 |
تعداد شمارهها | 1,316 |
تعداد مقالات | 16,175 |
تعداد مشاهده مقاله | 52,734,615 |
تعداد دریافت فایل اصل مقاله | 15,418,019 |
مقایسه مدلهای آموزش عمیق در پیشبینی جریان رودخانه در غرب کشور و بر روی رودخانه کشکان | ||
دانش آب و هیدرولیک | ||
مقاله 1، دوره 34، شماره 4، دی 1403، صفحه 1-19 اصل مقاله (1.01 M) | ||
نویسنده | ||
علی اکبر کرموند* | ||
گروه مهندسی عمران، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد علوم تحقیقات، تهران، ایران. | ||
چکیده | ||
شبیهسازی جریان رودخانه به با دقت بالا لازمه علم مدیریت رودخانه میباشد. در مواجه با چالش قدیمیِ مدلسازی روزانه جریان رودخانه، آموزش عمیق به عنوان ابزاری نوین مطرح شده است. در مطالعه حاضر، با تمرکز بر انتخاب سناریوی مناسب از ورودیهایِ مدل آموزشِ عمیق، شبیهسازی جریان روزانه رودخانه کشکان در چندین نوبت به روش آموزش عمیق LSTM و GRUانجام شده است. پیش از این، مدلسازی آموزش عمیق بهروش GRU و با استفاده از دادههای بومی اندازه گیری جریان رودخانه انجام نشده است. منطقه، مستعد سیل و کوهستانی بوده و ایستگاه هیدرومتری با سابقه وقوع سیل، واقع بر روی رودخانه کشکان انتخاب شده است. با استفاده از 4 رویکرد از روشهای حذف دادههای پرت، ورودی به دو مدل LSTM وGRU انتخاب شده و هشت مدل تولید شده است. ورودیهای ممکنه، عبارت بوده است از میانگین بارش منطقه، شاخص پوشش گیاهی نرمال شده، رطوبت خاک سطحی، جریانات آب زیرزمینی و همچنین خود جریان رودخانه کشکان در ایستگاه هیدرومتری. نتایج نشان داد بهترین عملکرد را به ترتیب، مدل GRU با ورودیهای اصلاح شده به روش حذف Z-Score، ماهالانوبیس با مقادیر RMSE میانگین و KGE و 41/5 و 99/0 و 23/6 و 7/0 در آموزش و 17/8 و79/0و 21/4 و 81/0در اعتبارسنجی و 01/5 و 68/0 و21/7 و 52/0و در مرحله تست میباشند. نتایج، روش LSTM را در شبیهسازی جریان رد نمیکند، اما سناریوهای برشمرده شده در روش GRU قدرت بالاتری در تشخیص الگوی پیچیده جریان روزانه رودخانه نشان دادند. | ||
کلیدواژهها | ||
آموزش عمیق؛ پیشبینی؛ جریان رودخانه؛ حذف دادههای پرت؛ علم داده | ||
مراجع | ||
Adriano de Melo G, Sugimoto DN, Tasinaffo PM, Moreira Santos AH, Cunha AM and Vieira Dias LA, 2019. A new approach to river flow forecasting: LSTM and GRU multivariate models. IEEE Latin America Transactions 17(12):1978–1986. https://ieeexplore.ieee.org/document/9011542
Ch S, Anand N, Panigrahi BK and Mathur S, 2013. Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23. https://linkinghub.elsevier.com/retrieve/pii/S0925231212005838
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H and Bengio Y, 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Pp.1724–1734. Proceedings of the EMNLP 2014 - Conference on Empirical Methods in Natural Language Processing.
Choi J, Won J, Jang S and Kim S, 2022. Learning enhancement method of long short-term memory network and its applicability in hydrological time series prediction. Water 14(18):2910. https://www.mdpi.com/2073-4441/14/18/2910
Collura T and Tarrant J, 2020. Principles and statistics of individualized live and static Z-Scores. Neuro Regulation 7(1):45–55. https://www.neuroregulation.org/article/view/19753
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A and Michaelsen J, 2015. The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Scientific Data. Nature Publishing Group 2(1):1–21. https://www.nature.com/articles/sdata201566
Geravand F, Hosseini SM and Ataie-Ashtiani B, 2020. Influence of river cross-section data resolution on flood inundation modeling: Case study of Kashkan river basin in western Iran. Journal of Hydrology 584,124743. https://doi.org/10.1016/j.jhydrol.2020.124743
Gers FA, 1999. Learning to forget: continual prediction with LSTM. 9th International Conference on Artificial Neural Networks: ICANN ’99. IEE, 850–855. https://digital-library.theiet.org/content/conferences/10.1049/cp_19991218
Gessesse AA and Melesse AM, 2019. Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara Region, Ethiopia. Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation. Elsevier 81–92
Hochreiter S and Schmidhuber J, 1997. Long Short-Term Memory. Neural Computation 9(8):1735–1780. https://direct.mit.edu/neco/article/9/8/1735-1780/6109
Hosseini FS, Sigaroodi SK, Salajegheh A, Moghaddamnia A and Choubin B, 2021. Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories. Environmental Science and Pollution Research 28(44):62487–62498. https://link.springer.com/10.1007/s11356-021-14534-w
Hunt KMR, Matthews GR, Pappenberger F and Prudhomme C, 2022. Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States. Hydrology and Earth System Sciences 26(21):5449–5472. https://hess.copernicus.org/articles/26/5449/2022/
Islam AS, 2010. Improving flood forecasting in Bangladesh using an artificial neural network. Journal of Hydroinformatics 12(3):351–364. https://iwaponline.com/jh/article/12/3/351/3040/.
Ismail Fawaz H, Forestier G, Weber J, Idoumghar L and Muller PA, 2019. Deep learning for time series classification: a review. Data Mining and Knowledge Discovery 33(4):917–963. https://link.springer.com/10.1007/s10618-019-00619-1
Javadinejad S, Dara R and Jafary F, 2020. Examining the association between dust and sediment and evaluating the impact of climate change on dust and providing adaptation. Resources Environment and Information Engineering 2(1):61–70. https://www.syncsci.com/journal/index.php/REIE/article/view/470
Karamvand A, Hosseini SA and Sharafati A, 2022. SMAP products for prediction of surface soil moisture by ELM network model and agricultural drought index. Acta Geophysica. Springer International Publishing. https://doi.org/10.1007/s11600-022-00973-7
Kingma DP and Ba JL, 2015. Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15
Kratzert F, Klotz D, Brenner C, Schulz K and Herrnegger M, 2018. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrology and Earth System Sciences 22(11):6005–6022. https://hess.copernicus.org/articles/22/6005/2018/
Lance VP and Digiacomo PM, 2019. NOAA Coastwatch/Oceanwatch/Polarwatch: A bridge from ocean satellite data to applications and information. OCEANS 2019 MTS/IEEE Seattle, OCEANS 2019. Institute of Electrical and Electronics Engineers Inc.
Le Ho, Lee and Jung, 2019. Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water 11(7):1387. https://www.mdpi.com/2073-4441/11/7/1387
Le X-H, Nguyen D-H, Jung S, Yeon M and Lee G, 2021. Comparison of Deep Learning Techniques for River Streamflow Forecasting. IEEE Access 9:71805–71820. https://ieeexplore.ieee.org/document/9423961/
Nagesh Kumar D, Srinivasa Raju K and Sathish T, 2004. River Flow Forecasting using Recurrent Neural Networks. Water Resources Management 18(2):143–161. http://link.springer.com/10.1023/B:WARM.0000024727.94701.12
Nifa K, Boudhar A, Ouatiki H, Elyoussfi H, Bargam B and Chehbouni A, 2023. Deep learning approach with LSTM for daily streamflow prediction in a semi-arid Area: A case study of Oum Er-Rbia River Basin, Morocco. Water 15(2):262. https://www.mdpi.com/2073-4441/15/2/262
Reis GB, da Silva DD, Fernandes Filho EI, Moreira MC, Veloso GV, Fraga M de S and Pinheiro SAR, 2021. Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow. Journal of Environmental Management 290:112625. https://linkinghub.elsevier.com/retrieve/pii/S0301479721006873
Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng CJ, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M and Toll D, 2004. The Global Land Data Assimilation System. Bulletin of the American Meteorological Society. American Meteorological Society 85(3):381–394. https://journals.ametsoc.org/view/journals/bams/85/3/bams-85-3-381.xml
Sánchez N, González-Zamora Á, Piles M and Martínez-Fernández J, 2016. A new soil moisture agricultural drought index (SMADI) Integrating MODIS and SMOS products: A case of study over the Iberian Peninsula. Remote Sensing 8(4):287. http://www.mdpi.com/2072-4292/8/4/287
Savitzky A and Golay MJE, 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry. American Chemical Society 36(8):1627–1639. https://pubs.acs.org/doi/abs/10.1021/ac60214a047
Sharma P and Machiwal D, 2021. Advances in Streamflow Forecasting. Advances in Streamflow Forecasting. Elsevier. https://linkinghub.elsevier.com/retrieve/pii/C20190021632
Shewalkar A, Nyavanandi D and Ludwig SA, 2019. Performance evaluation of deep neural networks Applied to speech recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence and Soft Computing Research 9(4):235–245. https://www.sciendo.com/article/10.2478/jaiscr-2019-0006
Staudemeyer RC and Morris ER, 2019. Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks. 1–42. http://arxiv.org/abs/1909.09586
Teng W, Rui H, Strub R and Vollmer B, 2016. Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community. Journal of the American Water Resources Association, 825–835
Uchida T, Kosugi K and Mizuyama T, 2002. Effects of pipe flow and bedrock groundwater on runoff generation in a steep headwater catchment in Ashiu, central Japan. Water Resources Research 38(7). https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2001WR000261
van Kuppevelt D, Meijer C, Huber F, van der Ploeg A, Georgievska S and van Hees VT, 2020. Mcfly: Automated deep learning on time series. SoftwareX 12:100548. https://linkinghub.elsevier.com/retrieve/pii/S2352711019300202
Wang W, Gelder PHAJM Van, Vrijling JK and Ma J, 2006. Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology 324(1–4):383–399. https://linkinghub.elsevier.com/retrieve/pii/S0022169405004981
Wegayehu EB and Muluneh FB, 2022. Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models. Advances in Meteorology.
Wei S, Zuo D and Song J, 2012. Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network. Journal of Hydroinformatics 14(4):974–991. https://iwaponline.com/jh/article/14/4/974/3201/.
Winter TC, 2007. The Role of Ground Water in Generating Streamflow in Headwater Areas and in Maintaining Base Flow 1. JAWRA Journal of the American Water Resources Association 43(1):15–25. https://onlinelibrary.wiley.com/doi/10.1111/j.1752-1688.2007.00003.x
Yafouz A, Ahmed AN, Zaini N, Sherif M, Sefelnasr A and El-Shafie A, 2021. Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms. Engineering Applications of Computational Fluid Mechanics 15(1):902–933. https://www.tandfonline.com/doi/full/10.1080/19942060.2021.1926328
Zealand CM, Burn DH and Simonovic SP, 1999. Short term streamflow forecasting using artificial neural networks. Journal of Hydrology 214(1–4):32–48. https://linkinghub.elsevier.com/retrieve/pii/S002216949800242X
Zounemat-Kermani M, 2014. Principal component analysis (pca) for estimating chlorophyll concentration using forward and generalized regression neural networks. Applied Artificial Intelligence 28(1):16–29. http://www.tandfonline.com/doi/abs/10.1080/08839514.2014.862771
| ||
آمار تعداد مشاهده مقاله: 29 تعداد دریافت فایل اصل مقاله: 23 |