مقایسه روش‌های مختلف تصمیم‌گیری چند معیاره در اولویت‌بندی سیل‌خیزی زیرحوضه‌های آبخیز کشف‌رود

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد علوم و مهندسی آبخیز، دانشگاه فردوسی مشهد، مشهد، ایران

2 دانشیار گروه مرتع و آبخیزداری، دانشگاه فردوسی مشهد، مشهد، ایران

3 گروه آموزش جغرافیا، دانشگاه فرهنگیان، تهران، ایران

10.22067/geoeh.2024.88777.1498

چکیده

سیل یکی از رایج­ترین خطرات طبیعی است که هم زندگی و هم دارایی­های مردم را تحت تأثیر قرار می­دهد. تغییرات آب و هوایی اخیر باعث افزایش فراوانی و شدت سیل شده است. در چنین شرایطی شناسایی مناطق بالقوه خطر سیل برای کاهش خسارات ناشی از سیل بسیار مهم است. ازاین‌رو، این مطالعه با استفاده از شاخص­های مورفومتری و روش­های تصمیم‌گیری چند معیاره به دنبال شناسایی مناطق آسیب‌پذیر سیلاب در زیرحوضه­های حوضه آبخیز کشف رود است. بدین منظور 16 شاخص مورفومتری از DEM منطقه استخراج شد و سیل‌خیزی زیرحوضه­های مختلف با استفاده از 5 روش تصمیم­گیری چندمعیاره شامل AHP، ANP، VIKOR، TOPSIS و ELECTRE تعیین گردید و در پنج طبقه سیل­خیزی خیلی­­زیاد، زیاد، متوسط، کم و خیلی­­کم طبقه­بندی شد. نتایج روش­های مختلف تصمیم­گیری چندمعیاره با استفاده از همبستگی اسپیرمن و بررسی درصد تغییرات مورد ارزیابی قرار گرفت. درنهایت مشخص گردید روش ANP در تهیه نقشه سیل­خیزی حوضه دارای دقت بیشتری است و اختلاف معنی‌داری بین روش ANP،AHP و VIKOR با نقشه سیل­خیزی حوضه بر اساس داده­های مشاهداتی وجود ندارد. در بررسی درصد تغییرات نیز مشخص شد کمترین درصد تغییرات مربوط به روش ANP است. بر اساس نتایج این روش 3/24 درصد حوضه در طبقه سیل­خیزی خیلی زیاد و 7/25 درصد در طبقه زیاد قرار دارند و درمجموع بیش از 50 درصد حوضه در طبقه سیل­خیزی زیاد و خیلی زیاد قرار می­گیرند.

کلیدواژه‌ها

موضوعات


Aher, P. D., Adinarayana, J., & Gorantiwar, S. D. (2014). Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: a remote sensing and GIS approach. Journal of Hydrology511, 850-860. https://doi.org/10.1016/j.jhydrol.2014.02.028
Ahmed, N., Hoque, M. A. A., Howlader, N., & Pradhan, B. (2022). Flood risk assessment: role of mitigation capacity in spatial flood risk mapping. Geocarto International37(25), 8394-8416. https://doi.org/10.1080/10106049.2021.2002422
Akay, H., & Baduna Koçyiğit, M. (2020). Flash flood potential prioritization of sub-basins in an ungauged basin in Turkey using traditional multi-criteria decision-making methods. Soft Computing24, 14251-14263. https://doi.org/10.1007/s00500-020-04792-0
Alam, A., Ahmed, B., & Sammonds, P. (2021). Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh. Quaternary International575, 295-307. https://doi.org/10.1016/j.quaint.2020.04.047
Al-Saady, Y. I., Al-Suhail, Q. A., Al-Tawash, B. S., & Othman, A. A. (2016). Drainage network extraction and morphometric analysis using remote sensing and GIS mapping techniques (Lesser Zab River Basin, Iraq and Iran). Environmental Earth Sciences75, 1243. https://doi.org/10.1007/s12665-016-6038-y
Aouragh, M. H., & Essahlaoui, A. (2018). A TOPSIS approach-based morphometric analysis for sub-watersheds prioritization of high Oum Er-Rbia basin, Morocco. Spatial Information Research26, 187-202. https://doi.org/10.1007/s41324-018-0169-z
Arab Ameri, A., Pour Ghasemi, H. R., Rezaei, K., & Sohrabi, M. (2019). Morphometric prioritization of watersheds for optimal water and soil resources management. Iranian Jouranl of Watershed Management Science, 13(45),87-96. [In Persian] http://dorl.net/dor/20.1001.1.20089554.1398.13.45.11.5
Bhat, M. S., Alam, A., Ahmad, S., Farooq, H., & Ahmad, B. (2019). Flood hazard assessment of upper Jhelum basin using morphometric parameters. Environmental Earth Sciences78, 1-17. https://doi.org/10.1007/s12665-019-8046-1
Bisht, S., Chaudhry, S., Sharma, S., & Soni, S. (2018). Assessment of flash flood vulnerability zonation through Geospatial technique in high altitude Himalayan watershed, Himachal Pradesh India. Remote Sensing Applications: Society and Environment12, 35-47. https://doi.org/10.1016/j.rsase.2018.09.001
Chandrashekar, H., Lokesh, K. V., Sameena, M., & Ranganna, G. (2015). GIS–based morphometric analysis of two reservoir catchments of Arkavati River, Ramanagaram District, Karnataka. Aquatic Procedia4, 1345-1353. https://doi.org/10.1016/j.aqpro.2015.02.175
Chaulagain, D., Rimal, P. R., Ngando, S. N., Nsafon, B. E. K., Suh, D., & Huh, J. S. (2023). Flood susceptibility mapping of Kathmandu metropolitan city using GIS-based multi-criteria decision analysis. Ecological Indicators154, 110653. https://doi.org/10.1016/j.ecolind.2023.110653
Chorley, R. J., Malm, D. E. G., & Pogorzelski, H. A. (1957). A New Standard for Estimating Drainage Basin Shape. American Journal Science, 255, 138-141. https://doi.org/10.2475/ajs.255.2.138
Christopher, O., Idowu, A., & Olugbenga, A. (2010). Hydrological analysis of Onitsha North East drainage Basin using geoinformatic techniques. World Applied Sciences Journal11(10), 1297-1302.
De Brito, M. M., Evers, M., & Almoradie, A. D. S. (2018). Participatory flood vulnerability assessment: a multi-criteria approach. Hydrology and Earth System Sciences22(1), 373-390. https://doi.org/10.5194/hess-22-373-2018
Dehghan, P., Hosseinpour Moghadam, M., Lashkaripour, G., & Ghafouri, M. (2012). Investigating the geomorphological forms of the Kashf River watershed in northeastern Iran. Paper presented of the 8th Conference of the Iranian Association of Engineering Geology and the Environment papers, Ferdowsi University of Mashhad, 1659-1666. [In Persian] https://civilica.com/doc/233175
Dou, J., Yamagishi, H., Pourghasemi, H. R., Yunus, A. P., Song, X., Xu, Y., & Zhu, Z. (2015). An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards78, 1749-1776. https://doi.org/10.1007/s11069-015-1799-2
Ganjirad, M., & Delavar, M. R. (2023). Flood risk mapping using random forest and support vector machine. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences10, 201-208. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-201-2023
Ghorbanzade, M., Azarakhshi, M., Mosaedi, A., & Rostami Khalaj, M. (2017). Evaluation of the efficiency of Analytic Hierarchy Process (AHP) to specify the areas with urban flood risk potential, case study: the central part of Torbat Heydarieh. Physical Geography Research49(4), 645-656. [In Persian]  https://doi.org/10.22059/jphgr.2018.231371.1007037
Hamdan, A. M. (2020). Hydro-morphometric analysis using geospatial technology: A case study of Wadi Gabgaba and Wadi Allaqi watersheds, southern Egypt-northern Sudan. Journal of Asian Scientific Research10(3), 190. https://doi.org/10.18488/journal.2.2020.103.190.212
Hejazi, A., Andariani, S., Almaspour, F., & Mokhtari Asl, A. (2015). Using Multi Criteria Decision Making and Remote Sensing Techniques in GIS Environment for Flood Susceptible Zones Assessment in Lighvan Chai Catchment. Hydrogeomorphology2(3), 61-80. [In Persian]  https://dorl.net/dor/20.1001.1.23833254.1394.2.3.4.5
Horton, R. E. (1945). Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geological Society of America Bulletin56(3), 275-370. https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2
Hosseinzadeh, M. M., Salehi Milani, A. R., & Rezaian Zarandini, F. (2023). Zoning of the sensitivity of the sub-basins of Nekarood basin to flooding, Neka-Mazandaran. Hydrogeomorphology10(34), 100-75. [In Persian] https://doi.org/10.22034/hyd.2023.52132.1646
Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., ... & Prakash, I. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology573, 311-323. https://doi.org/10.1016/j.jhydrol.2019.03.073
Mardani, A., Zavadskas, E. K., Govindan, K., Amat Senin, A., & Jusoh, A. (2016). VIKOR technique: A systematic review of the state of the art literature on methodologies and applications. Sustainability8(1), 37. https://doi.org/10.3390/su8010037
Mir Ghafouri, H., Asadian Ardakani, F., & Azizi, F. (2013). Multi-indicator decision making methods (along with the introduction of application software).Tehran: Academic Jihad Publications. [In Persian]
Moghimi, A., Mousavi Harami, S. R., Moatamed, A. & Ahmadi, H. (2010). The study of the effect of morph metric variables on maximum debi of flood in Chalus drainge basin using statistical methods and mathematical models. Journal of Earth and Resources, 2(1), 65-80. [In Persian]
Msabi, M. M., & Makonyo, M. (2021). Flood susceptibility mapping using GIS and multi-criteria decision analysis: A case of Dodoma region, central Tanzania. Remote Sensing Applications: Society and Environment21, 100445. https://doi.org/10.1016/j.rsase.2020.100445
Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research178(2), 514-529. https://doi.org/10.1016/j.ejor.2006.01.020
Parvaresh, E., Mahdavi, R., Malekian, A., Esmaeilipoor, Y., & Holisaz, A. (2018). Prioritizing of effective factors in flooding potential using Electre IIIMethod and Flood Flow Coefficient (Case study: Sarkhoon Subwatersheds of Bandarabbas). Journal of Arid Biome8(1), 75-87. [In Persian]  https://dorl.net/dor/20.1001.1.2008790.1397.8.1.7.4
Rahaman, S. A., Ajeez, S. A., Aruchamy, S., & Jegankumar, R. (2015). Prioritization of sub watershed based on morphometric characteristics using fuzzy analytical hierarchy process and geographical information system–A study of Kallar Watershed, Tamil Nadu. Aquatic Procedia4, 1322-1330. https://doi.org/10.1016/j.aqpro.2015.02.172
Rahimpour, T., Rezaei Moghaddam, M. H., Hejazi, S., & Valizadeh Kamran, K. (2023). Analysis of hydrogeomorphic characteristics of sub-basins in terms of erosion sensitivity (Case study: Aland Chai basin, northwest of Iran). Researches in Earth Sciences14(3), 112-131. [In Persian]  https://doi.org/10.48308/esrj.2023.103507
Razavizadeh, S., & Shahedi, K. (2015). Combination of AHP and TOPSIS methods to prioritize of flooding in Taleghan sub watersheds. Natural Ecosystems of Iran7(4), 33-46. [In Persian] https://sanad.iau.ir/fa/Journal/nei/Article/983174
Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process (Vol. 282). Berlin, Germany: Springer Science+ Business Media, LLC. https://doi.org/10.1007/0-387-33987-6
Schumm, S. A. (1956). Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geological society of America bulletin67(5), 597-646. https://doi.org/10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2
Sharifi kia, M., Shayan, S., Yamani, M., & Arab Ameri, A. (2018). Classification of Necarod watershed sub-basins using multi-criteria decision-making models, TOPSIS, SAW and VIKOR. Ecohydrology, 5(1), 69-83. [In Persian]  https://doi.org/10.22059/ije.2017.231263.550
Sharifi, F. (2002). Study of flood prevention, containment and control in Golestan Province with watershed management operations and restoration of forests and pastures.Paper presented of the first Seminar on mitigate and prevent flooding. [In Persian] https://civilica.com/doc/115846/
Smith, K. (1950). Standards for Grading Textures of Erosional Topography. American Journal of Science, 248, 655-668. http://dx.doi.org/10.2475/ajs.248.9.655
Tehrany, M. S., Pradhan, B., & Jebur, M. N. (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 Hydrology504, 69-79. https://doi.org/10.1016/j.jhydrol.2013.09.034
Teimouri, M., & Alvandi, E. (2022). Comparison of Models TOPSIS, SAW, ELECTRE and VIKOR in order to the prioritization of sedimentation and flood hazard of watersheds. Journal of Environmental Science and Technology, 24(2), 79- 99. [In Persian]  https://doi.org/10.30495/jest.2022.45404.4731
Zare, H., & Zamzam, F. (2022). Multi-criteria decision making, methods and applications. Yazd: Yazd University Press. [In Persian]
Zzaman, R. U., Nowreen, S., Billah, M., & Islam, A. S. (2021). Flood hazard mapping of Sangu River basin in Bangladesh using multi‐criteria analysis of hydro‐geomorphological factors. Journal of Flood Risk Management14(3), e12715. https://doi.org/10.1111/jfr3.12715
 
CAPTCHA Image