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مروری بر روش‌های پیش‌بینی و هشدار سیلاب واریزه‌ای | ||
| مدیریت آب و آبیاری | ||
| دوره 11، شماره 3، آبان 1400، صفحه 607-616 اصل مقاله (531.2 K) | ||
| نوع مقاله: مقاله مروری | ||
| شناسه دیجیتال (DOI): 10.22059/jwim.2021.329769.914 | ||
| نویسندگان | ||
| میترا تنهاپور1؛ محمد ابراهیم بنی حبیب* 2 | ||
| 1دانشجوی دکتری، گروه مهندسی آب، دانشکده پردیس ابوریحان، دانشگاه تهران، تهران، ایران | ||
| 2استاد، گروه مهندسی آب، دانشکده پردیس ابوریحان، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| سیلاب‌های واریزه‌ای می‌توانند خسارات شدیدی برای زندگی و اموال انسان‌ها به ویژه در مناطق پرجمعیت کوهستانی ایجاد کنند. در اثر تغییر اقلیم، فراوانی وقوع سیلاب واریزه‌ای روند افزایشی دارد. بنابراین ارزیابی روش‌های پیش‌بینی این پدیده جهت شناسایی رویکرد مناسب برای کاهش خطر و آگاهی مردم ضرورت دارد. در سال‌های اخیر، عمدتاً از رویکردهای آستانه‌های بارندگی، مدل‌های رگرسیون لجستیک و داده‌کاوی برای پیش‌بینی این جریان‌ها استفاده شده است. در این مطالعه مروری بر روش‌های یاد شده برای پیش‌بینی سیلاب واریزه‌ای نشان می‌دهد، جهت انتخاب روش مناسب برای پیش‌بینی سیلاب واریزه‌ای بهتر است بر اساس شرایط و ویژگی‌های منطقه مورد مطالعه تصمیم‌گیری شود طوری‌که ممکن است یک یا ترکیبی از این روش‌ها نتایج مناسبی ارائه دهد. به طور کلی در میان روش‌های مذکور، رویکردهای مبتنی بر داده، به دلیل سهولت کاربرد، دقت بالا و عدم نیاز به تعداد زیاد داده‌های مشاهداتی به ویژه در مناطقی که با مشکل کمبود داده مواجه هستند، به عنوان روش برتر در این تحقیق توصیه می‌شود. مطالعه حاضر می‌تواند برای شناسایی رویکردهای پیش‌بینی سیلاب واریزه‌ای جهت کاهش خسارات ناشی از آن مؤثر باشد. | ||
| کلیدواژه‌ها | ||
| آستانه های بارندگی؛ داده کاوی؛ سیلاب واریزه ای؛ مدل رگرسیون لجستیک | ||
| عنوان مقاله [English] | ||
| A Review on the methods of the debris-flow prediction and warning | ||
| نویسندگان [English] | ||
| Mitra Tanhapour1؛ Mohammad Ebrahim Banihabib2 | ||
| 1PhD candidate, Department of Water Engineering, Faculty of Aburaihan, University of Tehran, Tehran, Iran | ||
| 2Professor, Department of Water Engineering, Faculty of Aburaihan, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Debris flows can create severe damage for humans' life and estate especially in the mountainous areas. The frequency of debris flow occurrence has an increasing trend due to climate change. Therefore, it is necessary to evaluate the prediction methods of this phenomenon to identify an appropriate approach for reducing its danger and people awareness. In recent years, rainfall threshold, regression logistic model and data mining methods were mainly employed for predicting these flows. In this study, a review on the mentioned methods for predicting debris flows reveals that it is better to select the convenient method for predicting debris flow based on the conditions and characteristics of the case study so that one or a combination of these methods may provide appropriate results. Generally, among the mentioned methods, data mining approaches are recommended as superior method in this study because of easy application, high accuracy and lack of requirement for the large number of observed data especially in areas that have data shortage problem. The current study can be effective for identifying debris flows prediction approaches to reduce its damage. | ||
| کلیدواژه‌ها [English] | ||
| Data mining, Debris flow, Logistic regression model, Rainfall threshold | ||
| مراجع | ||
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Bai, T., Jiang, Z., & Tahmasebi, P. (2021). Debris flow prediction with machine learning: smart management of urban systems and infrastructures. Neural Computing and Applications, 33,15769–15779. Banihabib, M. E., & Masumi, A., (1999). Effect of High-Concentrated Sediment Transport on Inundation of Rivers: Case Study Masuleh Flood. In: Proceeding of 2nd Iranian Hydraulic Conference, Iranian Hydraulic Association, Tehran, Iran. Banihabib, M. E. (2003). Mud and Debris Floods, In: Proceeding of Flash Flood Prevention & Mitigation, Gorgon, Iran. Banihabib, M. E., & Forghani, A. (2017). An assessment framework for the mitigation effects of check dams on debris flow. Catena, 152, 277-284. Banihabib, M. E., & Elahi, M. (2009). Empirical Equation for Abrasion of Stilling Basin Caused by Impact of Sediment. In: Proceeding of World Environmental and Water Resources Congress: Great Rivers © 2009 ASCE, Kansas City, USA, 1-10. Banihabib, M. E., & Tanhapour, M. (2020). An empirical equation to determine the threshold for rainfall-induced landslides developing to debris flows. Landslides, 17, 2055-2065. Banihabib, M. E., Jurik, L., Kazemi, M. S., Soltani, J., & Tanhapour, M. (2020). A Hybrid Intelligence Model for the Prediction of the Peak Flow of Debris Floods. Water, 12(8), 2246. Caine, N. (1980). The rainfall intensity-duration control of shallow landslides and debris flows. Geografiska Annaler: Series A, Physical Geography, 62(1-2), 23-27. Cama, M., Lombardo, L., Conoscenti, C., Agnesi, V., & Rotigliano, E. (2015). Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy). Natural Hazards and Earth System Sciences, 15(8), 1785-1806. Cannon, S. H., Boldt, E. M., Laber, J. L., Kean, J. W., & Staley, D. M. (2011). Rainfall intensity–duration thresholds for postfire debris-flow emergency-response planning. Natural Hazards, 59(1), 209-236. Cannon, S. H., Gartner, J. E., Rupert, M. G., Michael, J. A., Rea, A. H., & Parrett, C. (2010). Predicting the probability and volume of post-wildfire debris flows in the intermountain western United States. Bulletin, 122(1-2), 127-144. Cannon, S. H., Gartner, J. E., Wilson, R. C., Bowers, J. C., & Laber, J. L. (2008). Storm rainfall conditions for floods and debris flows from recently burned areas in southwestern Colorado and southern California. Geomorphology, 96(3-4), 250-269. Chang, M., Dou, X., Hales, T. C., & Yu, B. (2021). Patterns of rainfall-threshold for debris-flow occurrence in the Wenchuan seismic region, Southwest China. Bulletin of Engineering Geology and the Environment, 80(3), 2117-2130. Chang, T.C., Wang, Z.Y., & Chien, Y.H. (2010). Hazard assessment model for debris flow prediction. Environmental Earth Sciences, 60(8), 1619-1630. Chen, N. S., Yang, C. L., Zhou, W., Wei, F. Q., Li, Z. L., Han, D., & Hu, G. S. (2011). A new total volume model of debris flows with intermittent surges: based on the observations at Jiangjia Valley, southwest China. Natural Hazards, 56(1), 37-57. Chen, X. Q., Cui, P., Feng, Z. L., Chen, J., & Li, Y. (2006). Artificial rainfall experimental study on landslide translation to debris flow. Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, 25(1), 106-116. Guo, X., Cui, P., Li, Y., Ma, L., Ge, Y., & Mahoney, W. B. (2016). Intensity–duration threshold of rainfall-triggered debris flows in the Wenchuan earthquake affected area, China. Geomorphology, 253, 208-216. Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2008). The rainfall intensity–duration control of shallow landslides and debris flows: an update. Landslides, 5(1), 3-17. Hassan-Esfahani, L., & Banihabib, M. E. (2016). The impact of slit and detention dams on debris flow control using GSTARS 3.0. Environmental Earth Sciences, 75(4), 328. Hirano, M., Moriyama, T., & Kawahara, K. (1995). Prediction of the occurrence of debris flow and a runoff analysis by the use of neural networks. Journal of Natural Disaster Science, 17(2), 53-63. Hirano, M., & Moriyama, T. (1993). Prediction of occurrence and runoff analysis of debris flow. In Hydraulic Engineering, ASCE, 1780-1785. Huang, J., Hales, T. C., Huang, R., Ju, N., Li, Q., & Huang, Y. (2020). A hybrid machine-learning model to estimate potential debris-flow volumes. Geomorphology, 367, 107333. Lay, U. S., & Pradhan, B. (2017). Identification of debris flow initiation zones using topographic model and airborne laser scanning data. In: Proceeding of Global Civil Engineering Conference. Springer, Singapore, 915-940. Liu, X., Wang, F., Nawnit, K., Lv, X., & Wang, S. (2020). Experimental study on debris flow initiation. Bulletin of Engineering Geology and the Environment, 79(3), 1565-1580. Ni H-Y (2015) Experimental study on initiation of gully-type debris flow based on artificial rainfall and channel runoff. Environmental Earth Science, 73, 6213-6227. Nikolopoulos, E. I., Borga, M., Creutin, J. D., & Marra, F. (2015). Estimation of debris flow triggering rainfall: Influence of rain gauge density and interpolation methods. Geomorphology, 243, 40-50. Nikolopoulos, E. I., Destro, E., Maggioni, V., Marra, F., & Borga, M. (2017). Satellite rainfall estimates for debris flow prediction: an evaluation based on rainfall accumulation–duration thresholds. Journal of Hydrometeorology, 18(8), 2207-2214. Nikolopoulos, E.I., Destro, E., Bhuiyan, M.A.E., Borga, M., & Anagnostou, E.N. (2018). Evaluation of predictive models for post-fire debris flow occurrence in the western United States. Natural Hazards and Earth System Sciences, 18(9), 2331-2343. Pan, H. L., Jiang, Y. J., Wang, J., & Ou, G. Q. (2018). Rainfall threshold calculation for debris flow early warning in areas with scarcity of data. Natural Hazards and Earth System Sciences, 18(5), 1395-1409. Papa, M. N., Medina, V., Ciervo, F., & Bateman, A. (2012). Estimation of debris flow critical rainfall thresholds by a physically-based model. Hydrology & Earth System Sciences Discussions, 9(11), 12797-12824. Rupert, M., Cannon, S. H., Gartner, J. E., Michael, J. A., & Helsel, D. R. (2008). Using logistic regression to predict the probability of debris flows in areas burned by wildfires, southern California, 2003-2006. Washington, DC: US Geological Survey. Staley, D. M., Kean, J. W., Cannon, S. H., Schmidt, K. M., & Laber, J. L. (2013). Objective definition of rainfall intensity–duration thresholds for the initiation of post-fire debris flows in southern California. Landslides, 10(5), 547-562. Tang, C., & Zhang, S. (2008). Study progress and expectation for initiation mechanism and prediction of hydraulic-driven debris flows. Advances in Earth Science, 23(8), 787-793. Tang, W., Ding, H. T., Chen, N. S., Ma, S. C., Liu, L. H., Wu, K. L., & Tian, S. F. (2021). Artificial Neural Network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China. Journal of Mountain Science, 18(1), 51-67. Tang, W., Ding, H. T., Chen, N. S., Ma, S. C., Liu, L. H., Wu, K. L., & Tian, S. F. (2021). Artificial Neural Network-based prediction of glacial debris flows in the ParlungZangbo Basin, southeastern Tibetan Plateau, China. Journal of Mountain Science, 18(1), 51-67. Tanhapour, M., & Banihabib, M. (2019). Determination of the rainfall threshold for debris flow occurrence in a part of Alborz mountainous basins. Watershed Engineering and Management, 11(3), 575-588. (In Persian) Wieczorek, G. F., & Guzzetti, F. 1999. A review of rainfall thresholds for triggering landslides. In: Proceeding of the EGS Plinius Conference, Maratea, Italy, 407-414. Zhang, S. J., Xu, C. X., Wei, F. Q., Hu, K. H., Xu, H., Zhao, L. Q., & Zhang, G. P. (2020). A physics-based model to derive rainfall intensity-duration threshold for debris flow. Geomorphology, 351, 106930. Zhenghong, C., & Bin, M. (1995). Spatial and Temporal Distribution of Rain-caused Slopeslides and Debris Flows in Hubei Province and Correlative Analysis of Rainfall Factors [J]. Rock and Soil Mechanics, 3. Zhuang, J., Cui, P., Wang, G., Chen, X., Iqbal, J., & Guo, X. (2015). Rainfall thresholds for the occurrence of debris flows in the Jiangjia Gully, Yunnan Province, China. Engineering Geology, 195, 335-346. | ||
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