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شناسایی مناطق مستعد سیل‌گیری و تعیین مهم‌ترین عامل‌های مؤثر بر وقوع آن با استفاده از مدل بیشینه‌ی بی‌نظمی در آبخیز تشان خوزستان | ||
| پژوهش های آبخیزداری | ||
| مقاله 8، دوره 33، شماره 4 - شماره پیاپی 129، دی 1399، صفحه 94-109 اصل مقاله (1.75 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2020.341563.1307 | ||
| نویسندگان | ||
| داود داودی مقدم1؛ علی حقی زاده* 2 | ||
| 1دانشجوی دکتری گروه آبخیزداری، دانشکده کشاورزی، دانشگاه لرستان، خرم آباد، ایران | ||
| 2دانشیار گروه آبخیزداری، دانشکده کشاورزی، دانشگاه لرستان، خرم آباد، ایران | ||
| چکیده | ||
| یکی از مهم‌ترین اقدام‌ها در مدیریت سیلاب تهیه‌یِ نقشه‌یِ حساسیت سیل‌گیری است. هدف از این پژوهش شناسایی منطقه‌های مستعد سیل‌گیری با مدل بیشینه‌ی بی‌نظمی در آبخیز تشان خوزستان است. نقشه‌یِ پراکنش سیل‌گیری‌ها تهیه شد و از کل 169 موقعیت سیل‌گیری 70% برای واسنجی مدل و 30% برای اعتبارسنجی به‌کار برده شد. از 10 عامل مؤثر در سیل‌گیری (ارتفاع، جهت شیب، فاصله از آبراه، تراکم زه‌کشی، درجه‌ی شیب، کاربری زمین، فاصله از جاده، شاخص رطوبت پستی‌وبلندی، انحنای تراز و سنگ‌شناسی) بهره‌گرفته شد. روند تأثیر و درصد مشارکت هر یک از ویژگی‌های محیطی در روی‌داد سیل‌گیری با منحنی‌های پاسخ و روش جکنایف بررسی شد. در نهایت، نقشه‌یِ حساسیت سیل‌گیری در چهار طبقه تهیه شد. برای ارزیابی دقت مدل‌سازی انجام‌شده از منحنی تشخیص عمل‌کرد نسبی بهره‌گرفته شد. نتیجه‌ها نشان داد که این مدل دقت بسیار خوبی (0/885AUC=) در شناسایی منطقه‌های مستعد سیل‌گیری دارد، و عامل‌های کاربری زمین و فاصله از جاده با 50/5% و 20/6% مشارکت بیش‌ترین تأثیر را در روی‌داد سیل‌گیری‌ها داشت. حدود 25% از مساحت آبخیز نسبت به روی‌داد سیل‌گیری حساسیت زیاد و خیلی زیاد دارد. با توجه به دقت پیش‌بینی بسیار خوب الگوریتم بیشینه‌ی بی‌نظمی در شناسایی منطقه‌های بحرانی و حساس به سیل‌گرفتگی، توصیه می‌شود از این مدل برای تهیه‌یِ نقشه‌یِ حساسیت سیل‌گیری سایر آبخیزها به‌خصوص منطقه‌هایی که ایستگاه‌های آب‌سنجی ندارد بهره‌گرفته شود. | ||
| کلیدواژه‌ها | ||
| آبخیز تشان؛ اعتبارسنجی؛ بیشینه بی‌نظمی؛ مدیریت سیلاب؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Detection of Susceptible Areas to Flooding and its Most Important Contributing Factors Using the Maximum Entropy Model in the Tashan Watershed, Khuzestan | ||
| نویسندگان [English] | ||
| Davoud Davoudi Moghaddam1؛ Ali Haghizadeh2 | ||
| 1Ph.D. Candidate, Department of Watershed Management, Faculty of Agriculture, Lorestan University, Khorramabad, Iran | ||
| 2Associate Professor, Department of Watershed Management, Faculty of Agriculture, Lorestan University, Khorramabad, Iran | ||
| چکیده [English] | ||
| One of the most important measures for flood management is the preparation of a flood susceptibility map. The purpose of this study was to identify susceptible areas to flooding using the maximum entropy model in the Tashan Watershed, Khuzestan Province. A flood inventory map was prepared for statistical analysis. Of the 169 flooding occurrences, 70% were used for the model calibration and 30% were used for validation. Ten flooding factors, namely: altitude, slope aspect, distance from the river, drainage density, slope angle, land use, distance from the road, topographic wetness index, plan curvature and lithology were used. The effect and contribution of each environmental parameter were calculated using the response curves and the Jackknife method. Finally, a flooding susceptibility map was prepared in four classes. The receiver operating characteristic curve was used to evaluate the accuracy of modeling. The results indicated that the maximum entropy model had very good accuracy (AUC=0.885) in identifying the prone areas to flooding, and the land use and distance from the road with 50.5% and 20.6% contribution, respectively, had the most impact on the occurrence of flood inundation. Also, about 25% of the watershed area was highly sensitive to flooding. Regarding very good prediction accuracy of the maximum entropy model in detecting susceptible areas to flooding, it is recommended to apply this model for the preparation of the flooding map in other watersheds, especially areas lucking hydrometric stations. | ||
| کلیدواژه‌ها [English] | ||
| Flooding management, machine learning, maximum entropy, the Tashan Watershed, validation | ||
| مراجع | ||
|
Arab Ameri A, Pourghasemi HR, Shirani K. 2017. Flood susceptibility zonation using new ensemble Bayesian-AHP methods (Case study: Neka Watershed, Mazandaran Province). Ecohydrology. 4(2): 447– 462. (In Persian). Bui D, Lofman O, Revhaug I, Dick O. 2011. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards. 59(1): 1413–1444. Davoudi Moghaddam D, Rezaei M, Pourghasemi HR, Pourtaaghie ZS. 2013. Groundwater potential spring mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences. 8(2): 913–929. Davoudi Moghaddam D, Rahmati O, Haghizadeh A, Kalantari Z. 2020. A modeling comparison of groundwater potential mapping in a mountain bedrock aquifer: QUEST, GARP, and RF models. Water. 12(3): 1-21. Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL. 2006. Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology. 319(1): 391–409. Dehghani M, Abbasnejad A, Negaresh H. 2016. Assessment of flood hazard and its zoning in Baft plain (south east part of Iran). Geography and Territorial Spatial Arrangement. 6(1): 141–152. (In Persian). Ebrahimi P, Soleymani K, Shahedi K. 2016. Developing strategic environmental planning map based on land use changes and flood zones case study: Neka River. Geography and Territorial Spatial Arrangement. 20(6): 57–74. (In Persian). Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ. 2011. A statistical explanation of the Maxent for ecologists. Diversity and Distributions. 17(3):43–57. Fernandez DS, Lutz MA. 2010. Urban flood hazard zoning in Tucuman Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology. 111(1): 90–98. Fustos I, Abarca‐del‐Rio R, Ávila A, Orrego R. 2017. A simple logistic model to understand the occurrence of flood events into the Biobío River Basin in central Chile. Journal of Flood Risk Management. 10(1): 17–29. Gearman M, Blinnikov MS. 2019. Mapping the potential distribution of Oak Wilt (Bretziella fagacearum) in east central and southeast the Minnesota using the Maxent. Journal of Forestry. 117(6): 579–591. Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu A, Chen W, Kougias I, Kazakis N. 2018. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment. 621(1): 1124–1141. Jebur MN, Pradhan B, Tehrany MS. 2014. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sensing of Environment. 152(1): 150–165. Kariminejad N, Hosseinalizadeh M, Pourghasemi HR, Bernatek-Jakiel A, Campetella G, Ownegh M. 2019. Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran. Science of the Total Environment. 677(1): 281–298. Khalilizadeh M, Mosaedi A, Najafinejad A. 2005. Flood hazard zonation in a part of Ziarat River in Gorgan urban watershed. Journal of Agricultural Sciences and Natural Resources. 12(4): 138–146. (In Persian). Khosravi K, Marufinia E, Nohani E, Chapy K. 2017. Evaluation of logistic regression efficiency in mapping flood susceptibility. Journal of Range and Watershed Management. 69(4): 863–876. (In Persian). Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A. 2012. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences. 67(1): 251–264. Mahmoud SH, Gan TY. 2018. Multi–criteria approach to develop flood susceptibility maps in arid regions of Middle East. Journal of Cleaner Production. 196(1): 216–229. Meinhardt M, Fink M, Tunschel H. 2015. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology. 234(1): 80–97. Mohamadi E, Montaseri M, Sokooti Oskoei R. 2005. Zonation of flood dangers in urban regions, using WMS and HEC-RAS, case study: Oshnavieh, Western Azerbyjan province. Watershed Engineering and Management. 1(1): 61–69. (In Persian). Moradnejadee M, Karimi K, Nakhaee Nejadfar S, Khosravi H, Joorgholami M. 2014. Assessing flood hydrograph by using simulated runoff-rainfall (case study: Evan Watershed). Watershed Management Research Journal. 27(2): 52–60. (In Persian). Mutzner R, Tarolli P, Sofia G, Parlange MB, Rinaldo A. 2016. Field study on drainage densities and rescaled width functions in a high‐altitude alpine catchment. Hydrological Processes. 30(13): 2138–2152. Nampak H, Pradhan B, Manap MA. 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology. 513(1): 283–300. Nazarnezhad H, Moharampour E, Babaei S. 2018. Estimation of the curve number and runoff height using Arc GIS software with the Arc CN-Runoff tool (case study: Nazluchai basin of Urmia). Watershed Management Research Journal. 31(2): 41–49. (In Persian). Nkwunonwo U, Malcolm W, Brian B. 2015. Flooding and flood risk reduction in the Nigeria: Cardinal gaps. Journal of Geography and Natural Disasters. 5(1): 1–12. Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 190(3): 231–259. Phillips SJ, Dudik M. 2008. Modeling of species distributions with the Maxent: new extensions and a comprehensive evaluation. Ecography. 31(2): 161–175. Pourghasemi HR, Moradi HR, Mohammdi M, Mostafazadeh R, Goli Jirandeh A. 2013. Landslide hazard zoning using bayesian theory. JWSS - Isfahan University of Technology. 16(62): 109–120. (In Persian). Pradhan B. 2009. Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Central European Journal of Geosciences. 1(1): 120 –129. Pradhan B, Youssef A. 2011. A 100‐year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia. Journal of Flood Risk Management. 4(3): 189–202. Rahmati O, Zeinivand H, Besharat M. 2015. Flood hazard zoning in the Yasooj Region, Iran, using GIS and multi-criteria decision analysis. Geomatics. Natural Hazards and Risk. 7(3): 1000–1017. Rajabizadeh Y, Ayyoubzadeh SA, Ghomeshi M. 2020. Flood survey of Khuzestan province in 97–98 and providing solutions for its control and management in the future. Ecohydrology. 6(4): 1069–1084. (In Persian). Schultz B. 2006. Flood management under rapid urbanisation and industrialisation in flood‐prone areas: a need for serious consideration. Irrigation and Drainage. 55(1): 3–8. Shrestha S, Kang TS. 2019. Assessment of seismically-induced landslide susceptibility after the 2015 Gorkha earthquake, Nepal. Bulletin of Engineering Geology and the Environment. 78(3): 1829–1842. Tehrany MS, Pradhan B, Jebur MN. 2013. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology. 504(1): 69–79. Tehrany MS, Pradhan B, Mansor S, Ahmad N. 2015. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena. 125(1): 91–101. Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S. 2018. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of the Total Environment. 615(4): 438–451. Wang Y, Hong H, Chen W, Li S, Pamučar D, Gigović L, Drobnjak S, Tien Bui D, Duan H. A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sensing. 11(1): 1–32. Youssef AM, Pradhan B, Sefry SA. 2016. Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences. 75(2):1–24. Zhao G, Pang B, Xu Z, Yue J, Tu T. 2018. Mapping flood susceptibility in mountainous areas on a national scale in China. Science of the Total Environment. 615(1): 1133–1142. Zou Q, Zhou J, Zhou C, Song L, Guo J. 2013. Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stochastic Environmental Research and Risk Assessment. 27(2): 525–546. | ||
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