| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,023 |
| تعداد مقالات | 33,775 |
| تعداد مشاهده مقاله | 45,114,925 |
| تعداد دریافت فایل اصل مقاله | 53,255,802 |
ارزیابی امکان روی‌داد سیل در آبخیز رود تلوار با مدل های وزن شاهد و وایازی پشتیبان بیزین | ||
| پژوهش های آبخیزداری | ||
| مقاله 8، دوره 34، شماره 4 - شماره پیاپی 133، دی 1400، صفحه 104-117 اصل مقاله (1.73 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.354344.1405 | ||
| نویسندگان | ||
| افروز گویلی1؛ جمیل بهرامی* 2؛ کامران چپی3؛ امید رحمتی4 | ||
| 1دانش آموخته کارشناسی ارشد، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران | ||
| 2استادیار گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران | ||
| 3دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی، دانشگاه کردستان، سنندج، ایران | ||
| 4استادیار بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کردستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، سنندج، ایران | ||
| چکیده | ||
| سیل یکی از خطرناک ترین و شایع ترین پیش‌آمد‌های طبیعی در جهان است که هر ساله باعث رسیدن زیان ها و آسیب های بسیاری بر جامعه و محیط زیست می‌شود. ارزیابی و مدیریت جامع سیلاب برای کم کردن تاثیر سیل بر زندگی و گذران مردم ضروری است. افزایش سیل در سال های اخیر نشان می‌دهد که مکان یابی و شناسایی منطقه‌های سیلخیز برای پیش بینی این فاجعه‌ی زیست محیطی اهمیت زیادی دارد زیرا نشناختن منطقه‌های حساس به سیل در آبخیز ممکن است اثر ویرانگری در پی داشته باشد. هدف اصلی این پژوهش ارزیابی عمل‌کرد مدل وزن شاهد و مدل وایازی پشتیبان بیزین برای تهیه‌ی نقشه‌ی حساسیت سیل در آبخیز رود تلوار استان کردستان است. 93 جای سیل‌گیر مشخص‌شده در منطقه به‌شیوه‌ی تصادفی به گروه واسنجی (70 %) و گروه اعتبارسنجی (30 %) تقسیم شد. هر دو مدل بر 10 عامل مؤثر در ایجاد سیل تمرکز دارند، که عبارت است از شیب، جهت شیب، انحنا، مدل رقومی ارتفاع، فاصله از آب‌راه، شاخص رطوبت پستی‌بلندی، شاخص توان آب‌راه، بارندگی، زمین‌شناسی و کاربری زمین. بر پایه‌ی مدل وزن شاهد 35/8% از سطح منطقه در طبقه‌ی خطر متوسط تا خیلی زیاد، و برپایه‌ی مدل وایازی پشتیبان بیزین 45/08% از سطح منطقه در طبقه‌ی خطر متوسط تا خیلی زیاد بود. برای اعتبارسنجی نقشه های توان سیل خیزی، از منحنی تشخیص عمل‌کرد نسبی بهره‌گرفته شد. اگرچه هر دو مدل دقت کافی دارد، در مدل وایازی پشتیبان بیزین درستی بیش‌تری (93/4 %) دیده شد. در زمینه‌ی توانیابی خطر سیل کار‌کرد مدل وایازی پشتیبان بیزین بهتر از مدل وزن شاهد بود. | ||
| کلیدواژه‌ها | ||
| آبخیز رود تلوار؛ سیل؛ مدل وایازی پشتیبان بیزین؛ مدل وزن شاهد | ||
| عنوان مقاله [English] | ||
| Evaluation of the Flooding Susceptibility in the Telvar River Basin Using the Evidence Weight Models and the Bayesian Logistic Regression | ||
| نویسندگان [English] | ||
| Afrooz Gavili1؛ Jamil Bahrami2؛ Kamran Chapi3؛ Omid Rahmati4 | ||
| 1Graduate student in Civil Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 2Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 3Associate Professor, Department of Nature Engineering, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran | ||
| 4Assistant Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran | ||
| چکیده [English] | ||
| Flooding is one of the most dangerous and common natural disasters, which yearly causes many damages and causalities for communities and the environment. A comprehensive flooding assessment and management is essential to reduce the effects of flooding on people's lives and livelihoods. Due to an increase in flooding in recent years, locating and identifying flood-prone areas is very important to predict this environmental disaster as mis-identification of flood-prone areas in a watershed may have devastating effects. The main purpose of this study was to evaluate the performance of the Bayesian Logistic Regression (BLR) and the weight-of-evidence (WOE) models for preparing flooding susceptibility maps in the Talvar Watershed of Province of Kurdistan. The geographical location of 93 flood prone areas identified in the watershed was randomly divided into one group (70%) for calibration and another (30%) for validation. Both models consider ten effective factors in causing flooding, namely: slope, slope direction, curvature, digital elevation model, distance from stream, topographic wetness index (TWI), stream power index (SPI), rainfall amount, geology, and land use. According to the WOE model, about 35.8% of the area is placed in the medium to very high hazard class, and based on the Bayesian logistic regression model, about 45.08% of the area is placed in the medium to very high hazard class. The relative performance detection curve was used to validate the flooding potential maps. Even though both models offered sufficient accuracy, the higher accuracy was assigned to the BLR model (93.4%). Therefore, the BLR model has better performance than the WOE model in terms of the flooding risk potential. | ||
| کلیدواژه‌ها [English] | ||
| Bayesian logistic regression mode, Talvar watershed, flood | ||
| مراجع | ||
Abedini M, Fathi Jokadan R. 2016. Flood risk zoning in the Karganroud’s Catchment basin using ArcGIS. Hydrogeomorphology. 3(7):1–17. (In Persian).Asgari M, Ahmadi M, Hemati M. 2016. Erosion of Chardavol River using HEC-RAS and GIS models. Geographical research. 30(1):71–80. (In Persian). Avali VR, Cooper GF, Gopalakrishnan V. 2014. Application of bayesian logistic regression to mining biomedical data. InAMIA Annual Symposium Proceedings. American Medical Informatics Association. 2014(1):266–274. Avand M, Moradi H, Ramezanzadeh M. 2020. Preparation of flood sensitivity map using two models of random forest machine learning and Bayesian generalized linear model. Journal of Environment and Water Engineering. 6(1):83–95. (In Persian). Azadtalab M, Shahabi H, Shirzadi A, Chapi K. 2020. Flood hazard mapping in Sanandaj using combined models of statistical index and evidential belief function. Journal of Motaleat Shahri. 9(36):27–40. (In Persian).Azadi F, Sadough SH, Ghaharoudui M, Shahabi H. 2020. Zoning of flood risk in Kashkan river basin using two models WOE and EBF. Journal of Geography and Environmental Hazards. 9(1):45–60. (In Persian).Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, Khosravi K, Chen W, Panahi S, Li S, Ahmad BB. 2018. Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific Reports. 8(1):1–14. Chapi K, Talebpour A, Shirzadi A, 2018, Comparison of logistic regression models and Bayesian logistic regression for spatial prediction of mass movements in Kurdistan Province, Journal of Quantitative Geomorphology Research,7(2):60–81. (In Persian). Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, Wang X, Bian H, Zhang S, Pradhan B, Ahmad BB. 2020. Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment..701(1):1–29. Chowdhuri I, Pal SC, Chakrabortty R. 2020. Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Advances in Space Research. 65(5):1466–1489. Das I, Stein A, Kerle N, Dadhwal VK. 2012. Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology. 179(15):116–25. Esmaili F, Rahmani S. 2015. Flood zoning using GIS and mathematical models emphasizing flood management: a case study of Gavi River, Ilam Province, western Iran. International Bulletin of Water Resources and Development. 2(4):60–72. (In Persian) Feng LH, Lu J. 2010. The practical research on flood forecasting based on artificial neural networks. Expert Systems with Applications. 37(4):2974–2977. Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Prakash I, Pham BT, Lee S. 2019. Prediction success of machine learning methods for flash flood susceptibility mapping in the tafresh watershed, Iran. Sustainability. 11(19):5426. Khosravi K, Pourghasemi HR, Chapi K, Bahri M. 2016. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental Monitoring and Assessment. 188(12):1–21. Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR. 2016. 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. Natural Hazards. 83(2):947–987. Lajevardi M, Khaledi S, Satari S. 2014. Zoning of flood potential of Mordagh Chay catchment (East Azerbaijan). Journal of Geography and Planning. 17(44):237-255. (In Persian) Mohammadi A, Kamran KV, Karimzadeh S, Shahabi H, Al-Ansari N. 2020. Flood detection and susceptibility mapping using sentinel-1 time series alternating decision trees and Bag-ADTree models. Complexity .17(1):1–21. Mosavi A, Ozturk P, Chau K.W. 2018. Flood prediction using machine learning models: Literature Review. Water. 10(11):1–40. Opolot E. (2013). Application of remote sensing and geographical information systems in flood management: A review. Research Journal of Applied Sciences Engineering and Technology. 6(10). 1884–1894. Pradhan B. 2010. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing. 38(2):301–320. Rahmati O, Pourghasemi HR, Zeinivand H. 2016. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International. 31(1):42–70. Shafapour Tehrany M, Kumar L, Neamah Jebur M, Shabani F. 2019. Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics Natural Hazards and Risk.10(1):79–101. Song KY, Oh HJ, Choi J, Park I, Lee C, Lee S. 2012. Prediction of landslides using ASTER imagery and data mining models. Advances in Space Research. 49(5):978–93. Tehrany MS, Pradhan B, Jebur MN. 2014. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology. 512(1):332–343. Yariyan P, Avand M, Abbaspour RA, Torabi Haghighi A, Costache R, Ghorbanzadeh O, Janizadeh S, Blaschke T. 2020. Flood susceptibility mapping using an improved analytic network process with statistical models. Geomatics, Natural Hazards and Risk. 11(1):2282–314. | ||
|
آمار تعداد مشاهده مقاله: 896 تعداد دریافت فایل اصل مقاله: 419 |
||