پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,504 |
| تعداد مشاهده مقاله | 124,122,769 |
| تعداد دریافت فایل اصل مقاله | 97,230,979 |
تجزیه و تحلیل خطر سیل‌گیری با استفاده از روش یادگیری ماشین جنگل تصادفی (مطالعۀ موردی: شهر مشهد) | ||
| اکوهیدرولوژی | ||
| مقاله 1، دوره 10، شماره 1، فروردین 1402، صفحه 1-15 اصل مقاله (2.2 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2022.346677.1667 | ||
| نویسندگان | ||
| نرگس عرب1؛ عبدالرسول سلمان ماهینی* 2؛ علیرضا میکائیلی تبریزی3؛ توما ویته4 | ||
| 1دکتری آمایش محیط زیست، دانشکدۀ شیلات و محیط زیست، دانشگاه گرگان | ||
| 2استاد، دانشکدۀ شیلات و محیط زیست، دانشگاه گرگان | ||
| 3دانشیار، دانشکدۀ شیلات و محیط زیست، دانشگاه گرگان | ||
| 4دانشیار، دانشکدۀ جغرافیا، دانشگاه رن 2 | ||
| چکیده | ||
| سیل از رایج‌ترین بلایای طبیعی است و خسارتهای مالی و جانی فراوانی به جای میگذارد. اگرچه میزان بارندگی در بسیاری از مناطق ایران کم است، در بسیاری از مناطق، بیشترین میزان بارندگی سالانه تنها در یک روز یا مدت کوتاهی رخ میدهد که منجر به سیل میشود. آب روان در جریان سیل به دلیل ساختار زمین‌شناسی و همچنین، تخریب اکوسیستم میتواند بسیار آلوده باشد و اغلب گل‌و‌لای زیادی به همراه دارد که بر خسارتهای سیل میافزاید. برای کاهش خسارتهای احتمالی سیل، برنامهریزان و تصمیمگیرندگان باید از زمان و مکان وقوع سیل آگاه باشند. این امر مستلزم استفاده از روشهای جدید پیش‌بینی سیل و جلوگیری از خسارتهای آن است. در این مطالعه، از روش یادگیری ماشین درخت تصادفی یا Random Forest (RF) برای پیش‌بینی مکان وقوع سیل در شهر مشهد استفاده شد و عملکرد آن مورد بررسی قرار گرفت. همچنین تأثیر هر یک از عوامل ارتفاع و شیب متوسط حوضه، جهت شیب، شاخص رطوبت توپوگرافی، شاخص خشکسالی، فاصله از آبراههها، زمین‌شناسی، کاربری اراضی، تراکم آبراههها، آبراههها و میزان بارش حداکثر متوسط سالانه در این پیش‌بینی مورد بررسی قرار گرفت. نتایج ارزیابی خروجی مدل RF نشان داد مقدار AUC95 درصد است. به طور کلی، نتایج نشان داد مدل RF دارای دقت زیادی در تعیین مناطق حساس به وقوع سیل در حوضۀ شهر مشهد است. | ||
| کلیدواژه‌ها | ||
| ارزیابی خطر سیل‌گیری؛ جنگل تصادفی؛ یادگیری ماشین؛ شهر مشهد؛ پهنهبندی سیل | ||
| عنوان مقاله [English] | ||
| Flood risk analysis using random forest machine learning method (Case study: Mashhad city) | ||
| نویسندگان [English] | ||
| Narges Arab1؛ Abdulrassoul Salman Mahiny2؛ Alireza Mikaeili Tabrizi3؛ Thomas Houet4 | ||
| 1Ph.D in Environment assessment and land use planning, Faculty of Fisheries and Environment, Gorgan University, Gorgan ,Iran | ||
| 2Professor, Faculty of Fisheries and Environment, Gorgan University, Gorgan ,Iran | ||
| 3Associate Professor, Faculty of Fisheries and Environment, Gorgan University, Gorgan ,Iran | ||
| 4Associate Professor, Faculty of Geography, University of Rennes 2, France | ||
| چکیده [English] | ||
| Abstract Flood is one of the most common natural disasters that causes significant financial and human losses. Although rainfall is low in many parts of Iran, in some areas, the highest amount of annual rainfall occurs in just one day or a short period, leading to floods. Due to geological structure and ecosystem destruction, the surface water during floods can be highly polluted and often carries a lot of sediment, which increases flood damage. To reduce potential flood damage, planners and decision-makers must be aware of the time and location of floods. This requires the use of new methods for predicting floods and preventing their damage. In this study, the Random Forest (RF) machine learning method was used to predict the location of floods in Mashhad city, and its performance was evaluated. The impact of each factor including average basin elevation and slope, slope direction, topographic moisture index, drought index, distance from waterways, geology, land use, waterway density, waterways, and maximum average annual rainfall was also examined in this prediction. The evaluation results of the RF model output showed an AUC value of 95%. Overall, the results showed that the RF model has high accuracy in identifying flood-prone areas in the Mashhad city basin. | ||
| کلیدواژه‌ها [English] | ||
| Flood risk assessment, Random Forest, machine learning, Mashhad city, flood zoning | ||
| مراجع | ||
|
[1]. Vitalea C, Meijerinka S, Mocciab, F D, Ache P. Urban flood resilience, a discursive-institutional analysis of planning practices in the Metropolitan City of Milan. Land Use Policy. 2020; (95): 104575. [2]. Lin J, He X, Lu S, Liu D, He P. Investigating the influence of three-dimensional building configuration on urban pluvial flooding using random forest algorithm, Environmental Research. 2020. https://doi.org/10.1016/j.envres.2020.110438. [3]. UNDP. The Human Cost of Weathe Related Disasters. 2015. Retrieved from. http:// www.unisdr.org/files/46796_cop21weatherdisastersreport2015.pdf. [4]. Khan A, Xun W, Ahsan H, Vineis P. Climate change, sea-level rise, & health impacts in Bangladesh. Environ. Sci. Policy Sustain. Dev. 2011; 53 (5): 18–33. https://doi. org/10.1080/00139157.2011.604008. Retrieved from. [5]. Adeoye N O, Ayanlade A, Babatimehin O. Climate change and menace of floods in Nigerian cities: socio-economic implications. Adv. Nat. Appl. Sci. 2009; 3 (3): 369–378. [6]. Berghuijs W R, et al. Recent changes in extreme floods across multiple continents. Environmental Research Letters. 2017; 12: 114035. [7]. Zhang Q. et al. Identifying dominant factors of waterlogging events in metropolitan coastal cities: The case study of Guangzhou, China. Journal of Environmental Management. 2020; 271: 110951. [8]. Lin J. Wu W. Investigating the land use characteristics of urban integration based on remote sensing data: experience from Guangzhou and Foshan. Geocarto International. 2019; 34: 1608-1620. [9]. Brody S D, et al. Evaluating the effects of open space configurations in reducing flood damage along the Gulf of Mexico coast. Landscape and Urban Planning. 2017; 167: 225-231. [10]. Mohammad Eini, Hesam Seyed Kaboli , Mohsen Rashidian, Hossien Hedayat. Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction. 2020; 50: 101687. [11]. Mohammad Khalid Hossain, Qingmin Meng. A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood. Land Use Policy. 2020; 99: 104829. [12]. Chengguang Lai, Quanxi shao, Xiaohong chen, Zhaoli Wang, Xiaowen Zhou, Bing Yang, Lilan Zhang. Flood risk zoning using a rule mining based on ant colony algorithm. Journal of Hydrology. 2016; 542: 268-280. [13]. Hossein Mojaddadi, Biswajeet Pradhan, Haleh Nampak, Noordin Ahmad & Abdul Halim bin Ghazali. Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS, Geomatics, Natural Hazards and Risk. 2017. To link to this article: http://dx.doi.org/10.1080/19475705.2017.1294113.
[14]. Youssef A M, Pradhan B, Hassan A M. Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ. Earth Sci. 2011; 62 (3): 611–623. https://doi.org/10.1007/s12665-010-0551-1. [15]. Darabi H, Choubin B, Rahmati O, Torabi Haghighi A, Pradhan B, Kløve B. Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. J. Hydrol. 2019; 142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002 [16]. Fernández D S, Lutz M A. Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng. Geol. 2010; 111 (1–4), 90–98. https://doi.org/10.1016/j.enggeo.2009; 12.006. [17]. Lee MJ, Kang J, Jeon S. Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. 2012 IEEE International Geoscience and Remote Sensing Symposium. 2012. doi:10.1109/ igarss. 2012; 6351414. [18]. Pradhan B. Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spatial Hydrol. 2010; 9:1–18. [19]. Chau KW, Wu CL, Li YS. Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng. 10: 485–491. doi:10.1061/(asce)1084-0699. 2005; 10:6(485). [20]. Zhou J, Deng W, Zou Q, Xiao J, Zhang Y, Hua W. Flood disaster evaluation model based on kernel dual optimization support vector machine. Information Technol J. 2013; 12: 2412–2418. doi:10.3923/itj.2013.2412.2418. [21]. Tehrany MS, Pradhan B, Jebur MN. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol. 2014; 512: 332–343. Doi:10.1016/j.jhydrol.2014.03.008. [22]. Lecca G, Petitdidier M, Hluchy L, Ivanovic M, Kussul N, Ray N, Thieron V. Grid computing technology for hydrological applications. J Hydrol. 2011; 403:186–199. [23]. Gholamali Fard M, Fatehi M, Bidel H, Ghafouri B. Analysis of dependence of land use changes in Mashhad city with topographical parameters in GIS environment. Environmental science and technology. 2016. 19th period Number 5: 299-315. In Persion. [24]. USGS. 2019. The National Map. Retrieved August 17. from. https://viewer. nationalmap.gov/basic/. [25]. Tehrany M S, Pradhan, B, Jebur, M N. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J. Hydrol. 2013. (Amst) 504: 69–79. https://doi.org/10. 1016/j.jhydrol.2013.09.034. [26]. Mukerji A, Chatterjee C, Raghuwanshi, N S. Flood forecasting using ANN, Neuro-Fuzzy, and Neuro-GA models. J. Hydrol. Eng. 2009; 14 (6): 647–652. https://doi. org/10.1061/(ASCE)HE.1943-5584.0000040. [27]. Pradhan B, Lee S. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ. Model. Softw. 2010; 25 (6): 747–759. https://doi.org/10.1016/j.envsoft.2009.10.016. [28]. Khosravi K, Nohani E, Maroufinia E, Pourghasemi H R. A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision- making technique. Nat. Hazards. 2016; 83 (2), 947–987. https://doi.org/10.1007/ s11069-016-2357-2. [29]. Chu H, Wu W, Wang Q J, Nathan R. & Wei J. An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions. Environmental Modelling & Software. 2020; 124: 104587. [30]. Gokceoglu C, Sonmez H, Nefeslioglu H A, Duman TY, Can T. The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol. 2005; 81:65–83. Doi:10.1016/ j.enggeo. 2005.07.011. [31]. Rizeei HM, Saharkhiz M A, Pradhan B, Ahmad N. Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models. Geocarto Int. 2016; 31:1158–1177. Doi: 10.1080/ 10106049.2015.1120354. [32]. Rostami Khalaj M. Hasami D. Salmani H, Teymourian T. Urban flood risk zoning using multi-variable decision-making method (case study: Imam Ali town, Mashhad city). Environmental science and technology. The twenty-second period. 2018; Number (11). February 1998. In Persion. [33]. Netra R. Regmi John R. Giardino John D. Vitek. Assessing susceptibility to landslides: Using models to understand observed changes in slopes. Geomorphology. 2010. Volume 122, Issues 1–2, 1 October 2010, Pages 25-38. [34]. Breiman L. Random forests. Mach learn. 2001; 45:5–32. [35]. Ho TK. Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition. New York (NY): IEEE. 1995. [36]. Kleinberg EM. On the algorithmic implementation of stochastic discrimination. IEEE Trans Pattern Anal Mach Int. 2000; 22:473–490. [37]. Ellis K, Kerr J, Godbole S, Lanckriet G, Wing D, Marshall S. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiol Meas. 2014; 35:2191. [38]. Sunmin Lee, Jeong-Cheol Kim, Hyung-Sup Jung, Moung Jin Lee & Saro Lee. Spatial prediction of flood susceptibility using random-forest and boostedtree models in Seoul metropolitan city, Korea, Geomatics, Natural Hazards and Risk. 2017. DOI: 10.1080/19475705.2017.1308971 [39]. Pradhan B, Hagemann U, Tehrany, MS, Prechtel N. An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. Comput Geosci. 2014; 63:34–43. doi:10.1016/j. cageo.2013.10.011. [40]. Avand M T. Moradi H R. Ramzanzadeh Lesboi m. Preparation of flood sensitivity map using two random forest machine learning models and Bayesian generalized linear model. Environment and water engineering. 2019; Volume 6. Number 1. 83-95.In Persion. | ||
|
آمار تعداد مشاهده مقاله: 581 تعداد دریافت فایل اصل مقاله: 353 |
||