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برآورد ضریب رواناب رگبار با استفاده از هوش مصنوعی (مطالعۀ موردی: حوضۀ آبخیز کسیلیان) | ||
| اکوهیدرولوژی | ||
| مقاله 14، دوره 8، شماره 2، تیر 1400، صفحه 499-512 اصل مقاله (1.06 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2021.319659.1476 | ||
| نویسندگان | ||
| حسین پوراسدالله1؛ مهدی وفاخواه* 2؛ بهارک معتمد وزیری3؛ علیرضا مقدم نیا4؛ حسین اسلامی5 | ||
| 1دانشجوی دکتر‌ی واحد علوم تحقیقات دانشگاه آزاد اسلامی، تهران | ||
| 2استاد دانشگاه تربیت مدرس، تهران | ||
| 3استادیار واحد علوم تحقیقات دانشگاه آزاد اسلامی، تهران | ||
| 4دانشیار گروه مرتع و آبخیزداری دانشگاه تهران | ||
| 5استادیار واحد شوشتر دانشگاه آزاد اسلامی، شوشتر | ||
| چکیده | ||
| در تحقیق پیش رو تخمین ضریب رواناب با توجه به تأثیر پوشش گیاهی انجام شده است. ابتدا مدلسازی ضریب رواناب با استفاده از دادههای سیلاب و رگبار ساعتی طی دورۀ آماری 1366ـ 1388 انجام شده و ضرایب رواناب حوضۀ آبخیز کسیلیان تهیه شد. در مرحلۀ بعد، با استفاده از مدلهای شبکۀ عصبی مصنوعی (ANN)، شبکۀ عصبی‌ـ فازی تطبیقی (ANFIS) و رگرسیون بردار پشتیبان (SVR) و عوامل مؤثر شامل شدت بارش، مقدار شاخص ، بارش 5 روز قبل و شاخص نرمال‌شدۀ اختلاف پوشش گیاهی (NDVI) ضریب رواناب در مقیاس رگبار برآورد شد. سپس، صحت و اهمیت هر یک از عوامل مؤثر بر ضریب رواناب حوضۀ آبخیز کسیلیان ارزیابی شد. نتایج نشان داد از بین سه مدل ANN، ANFIS و SVR، مدل ANN با مجذور میانگین مربعات خطا، ضریب تبیین، میانگین خطای اریبی و ضریب نش‌ـ ساتکلیف به‌ترتیب 08/0، 85/0، 84/0 و 01/0 در مرحلۀ آموزش و 12/0، 76/0، 74/0 و 03/0- در مرحلۀ آزمایش به عنوان مدل کارا در ارتباط با پیشبینی ضریب رواناب است. در مجموع، پیشنهاد میشود با توجه به اینکه ضریب رواناب کارکرد زیادی در فرایندهای هیدرولوژیک و بروز سیل دارد، بنابراین تخمین بهینۀ آن میتواند به مدیریت بهتر حفاظت آب و خاک و مدیریت فرسایش و رسوب حوضۀ آبخیز کمک کند. | ||
| کلیدواژه‌ها | ||
| حفاظت آب و خاک؛ شاخص نرمال‌شدۀ اختلاف پوشش گیاهی؛ شبکۀ عصبی مصنوعی؛ مدیریت رواناب | ||
| عنوان مقاله [English] | ||
| Estimation of event based runoff coefficient using artificial intelligence models (Case study: Kasilian watershed) | ||
| نویسندگان [English] | ||
| Hossein Pourasadoullah1؛ Mehxi Vafakhah2؛ Baharak Motamedvaziri3؛ Alireza Moghaddam Nia4؛ Hossein Eslami5 | ||
| 1Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2خیابان حافظ-کوچه هاشمی نژاد شمالی-کوی گلشن ۴ | ||
| 3Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 4Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, College of Agriculture & Natural Resources, University of Tehran, Daneshkadeh Ave., karaj, Iran | ||
| 5Faculty of Agriculture, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran | ||
| چکیده [English] | ||
| In this research, estimation of the Runoff Coefficient (RC) is carried out depending on land cover. Initially, RC modeling was performed using 54 hourly rainfall and corresponding runoff data during the period 1987–2010 in the Kasilian watershed. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) models and effective factors including rainfall intensity, Φ index (the average loss), five-day previous rainfall and Normalized Difference Vegetation Index (NDVI) were used to estimate RC. The results showed that the ANN model was more efficient than the other two models and had Mean Bias Error (MBE), Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (NSE) and Normalized Root Mean Square Error (NRMSE) equal to 0.08, 0.85, 0.84 and 0.37, respectively for the training phase and 0.12, 0.76, 0.74 and 0.47 for the test phase. In general, it is suggested that RC plays a major role in hydrological mechanisms and flooding. Thus, optimal estimation of RC can be helpful in better management of soil and water conservation and erosion and sediment management in this area. | ||
| کلیدواژه‌ها [English] | ||
| Artificial neural network, Normalized difference vegetation index, Runoff management, Soil and water conservation | ||
| مراجع | ||
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[1]. Kavian AA, 2014. Application of SWAT semi-physical distribution model in simulating the effect of land use change on runoff of Haraz dam watershed (a study on the scale of useful life of the dam), Research project of Sari University of Agricultural Sciences and Natural Resources. (In Persian) [2]. Kwaad, F., 1991. Summer and winter regimes of runoff generation and soil erosion on cultivated loess soils (The Netherlands). Earth Surf. Process. Landforms 16, 653–662. [3]. Shi P.J., Yuan Y., Zheng J., Wang J., Wang J.A., Ge Y., Qiu G.Y., 2007.The effect of land use/cover change on surface runoff in Shenzhen region, China, Catena, 69: 31-35 [4]. Xu, E., Zhang, H., 2020. Change pathway and intersection of rainfall, soil, and land use influencing water-related soil erosion. Ecological Indicators.113, 106281. [5]. Sharifi AR, Dinapajoohi, Fakhri Fard A., Moghaddamnia AR, 2013. Optimal combination of variables for simulation of runoff in Imameh watershed using gamma test, Journal of Water Science and Soil, 23 (4), 59-72. (In Persian) [6]. Dinka, M.O., Klik, A., 2019. Effect of land use–land cover change on the regimes of surface runoff—the case of Lake Basaka catchment (Ethiopia). Environmental Monitoring and Assessment. 191, 278. [7]. Kumari,p., Kumar, p. and P.V. Singh. 2018. Rainfall-Runoff Modelling Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. Indian Journal of Ecology (2018) 45(2): 281-285 [8]. Merz R., Bloschl G., Parajka J., 2006.Spatio-temporal variability of event runoff coefficients, Journal of Hydrology, 331: 591-604. [9]. Ataei M., 2017, Rainfall-runoff modeling using artificial neural network (ANN) and HEC-HMS methods (Case study: Gharasoo catchment), 2nd International Conference on Civil Engineering, Architecture and Crisis Management, Tehran, Allameh University Majlis. [10]. Zhang X.M., Yu X.X., Zhang M.L., Li J.L., 2007.Response of land use/coverage change to hydrological dynamics at watershed scale in the Loess Plateau of China, ActaEcologicaSinica,27(2):414−423.http://glovis.usg [11]. McIntyre N., Al-Qurashi, A., Wheater, H.S., 2007.Analysis of rainfall-runoff events from an arid catchment in Oman, Hydrological Sciences Journal, 52(6): 1103-1118. [12]. Dastorani, M. T., Moghadamnia, A., Piri, J., & Rico-Ramirez, M. (2010). Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment. 166(1), 421-434. [13]. Sen Z.,2008.Instantaneous runoff coefficient variation and peak discharge estimation model, Journal of Hydrologic Engineering, 13(4): 270-277. [14] Solaimani, K. 2009. Rainfall-runoff prediction based on artificial neural network (a case study: Jarahi watershed). American-Eurasian Journal of Agriculture and Environmental Sciences. 5: 6. 856-865. [15]. Curtu, R., Fonley, M., 2015. Nonlinear response in runoff magnitude to fluctuating rain patterns. Chaos An Interdiscip. Journal of Nonlinear Science. 25, 36409. [16]. Moeyersons, J., Imwangana, F.M., Dewitte, O., 2015. Site-and rainfall-specific runoff coefficients and critical rainfall for mega-gully development in Kinshasa (DR Congo). Natural Hazards. 79, 203–233. [17]. Mahdavi MB, 2002. Applied Hydrology, Volume 2, Second Edition, University of Tehran Press, 437 p. (In Persian) [18]. Zeinali, V., Vafakhah, M., Sadeghi, S.H., 2019. ‘Impact of Urbanization on Temporal Distribution Pattern of Storm Runoff Coefficient. Environmental Monitoring and Assessment. 191, 595. https://doi.org/10.1007/s10661-019-7734-3 [19]. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351(1974), 309. [20]. Parida B.P., Moalafhi D.B., Kenabatho P.K., 2006.Forecasting runoff coefficients using ANN for water resources management: The case of Notwane catchment in Eastern Botswana, Physics and Chemistry of the Earth, 31: 928–934. [21]. Li, Y., Xie, Z., Qin, Y., Zheng, Z., 2019. Responses of the Yellow River basin vegetation: climate change. International Journal of Climate Change Strategies and Management. [22]. Minhaj, M. 2005. Fundamentals of Neural Networks, Amirkabir University of Technology, Tehran. (In Persian) [23]. Mousavi, S. J., Ponnambalam, K., & Karray, F. 2007. Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets and Systems, 158(10), 1064–1082. doi:10.1016/j.fss.2006.10.024 [24]. Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, 9, 155-161. [25]. Eslamian SS, Mamanpoosh AR, Nasri Z., Etemadi H., 2006. Analysis of runoff coefficients and correlation between runoff and rainfall in Bazaft basin, the first conference on regional water resources exploitation in Karun basins And Zayandehrud, Shahrekord, 14-15 September 2006, 1081-1090. (In Persian) [26]. Tapia R.P., Cornejo M.T., Arellano L.R., Diaz C.J., and Daz C.F., 2006.Instantaneous runoff coefficients for Tutuven river basin, Maule Region, Chile, Bosque (Valdivia), 27(2): 83-91. [27]. Yuan, Z., Yan, D., Xu, J., Wang, Y., Yao, L., Yu, Z., 2019. Effects of the precipitation pattern and vegetation coverage variation on the surface runoff characteristics in the eastern Talhang Mountain. Applied Ecology and Environmental Research.17, 5753–5764. [28]. Martiny, N., Camberlin, P., Richard, Y., Philippon, N., 2006. Compared regimes of NDVI and rainfall in semi‐arid regions of Africa. International Journal of Remote Sensing. 27, 5201–5223. [29]. Piao, S., Fang, J., Zhou, L., Guo, Q., Henderson, M., Ji, W., Li, Y., Tao, S., 2003. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research: Atmospheres. 108. [30]. Tokar A.S., and Markus M. 2000. Precipitation runoff modeling using artificial neural network and conceptual models. Journal of Hydrologic Engineering. ASCE. 5: 156-161. [31]. Maria, C.V.R., Haroldo, F.C.V., and Nelson, J.F. 2005. Artificial neural network technique for rainfall forecasting applied to the São Paulo region, Journal of Hydrology. 301: 1-4. 146-162. [32]. Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J. and D. Pulido-Velazquez. 2018. A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water Journal. 10, 192; doi:10.3390/w10020192 [33]. Dastorani, M. T., Sharifi Darani, H., Talebi, A., & Moghadam Nia, A. (2011). Evaluation of the application of artificial neural networks and adaptive neuro-fuzzy inference systems for rainfall-runoff modeling in Zayandeh-rood dam basin. Journal of Water and Wastewater. 80, 114-125. | ||
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