ارزیابی کارایی مدل‏ های ویکور، L-THIA و شبکۀ عصبی مصنوعی در تحلیل منطقه‏ ای سیلاب (مطالعۀ موردی: استان خراسان رضوی)

نوع مقاله : پژوهشی

نویسندگان

1 دانشیار ژئومورفولوژی دانشگاه حکیم سبزواری، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، ایران

2 استاد تمام ژئومورفولوژی، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، ایران

3 دانش‌آموختۀ کارشناسی ارشد ژئومورفولوژی‏، دانشکدۀ جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، ایران

چکیده

با توجه به شرایط طبیعی ایران، بی‌توجهی به موضوع سیلاب‏‏ها می‏تواند خسارت‏های جبران‌ناپذیری به بار آورد که در این میان، برآورد سیلاب و پهنه‏بندی نواحی سیل‌گیر اهمیت بسیار زیادی در کنترل خطرات دارد. بنابراین، پهنه‏بندی بر اثر تغییرات اقلیمی، امری ضروری است. از این‌رو، در پژوهش حاضر به منظور بررسی خطر‏پذیری سیلاب در حوضه‏های منتخب خراسان رضوی با استفاده از مدل ویکور، L-THIA و شبکۀ عصبی مصنوعی انجام شده است. سپس، از متغیرهای چهارده‌گانۀ مؤثر بر وقوع سیلاب شامل اقلیم، کاربری اراضی، ارتفاع، تراکم زهکشی، واحدهای ژئومورفولوژی، لیتولوژی، ارتفاع رواناب، نفوذپذیری، شیب و جهت آن، فاصله از آبراهه، بارش، دما و خاک استفاده شده است. نتایج نشان داد از میان عوامل نامبرده، پارامترهای اقلیم، کاربری اراضی، شیب، تراکم زهکشی، فاصله از آبراهه، بارش، خاک و واحدهای ژئومورفولوژی بر اساس محاسبات آماری تأثیر بیشتری را در وقوع سیلاب دارند. ارزیابی کمی و کیفی نتایج با استفاده از آماره‏های گوناگون نشان داد مدل L-THIA‏، با گامای 8/0 بیشترین مقدار همبستگی را با لایه‏های اولیه دارد ‌و از دقت و کارایی بیشتری نسبت به دو مدل ویکور و شبکۀ عصبی مصنوعی در پیش‏بینی سیلاب برخوردار است.

کلیدواژه‌ها


عنوان مقاله [English]

Efficiency Evaluation of the VIKOR, L-THIA, and Artificial Neural Network (ANT) Models in Flood Zone Analysis (Case Study: Khorasan Razavi Province)

نویسندگان [English]

  • Mohammad Ali Zanganeh Asadi 1
  • Abolghasem Amir Ahmadi 2
  • Mahnaz Naemi Tabar 3
1 Associate Professor of Geomorphology, Hakim Sabzevari University, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Iran
2 Professor of Geomorphology, Hakim Sabzevari University, Faculty of Geography and Environmental Sciences, Iran
3 Graduated from Geomorphology, Hakim Sabzevari University, Faculty of Geography and Environmental Sciences, Iran
چکیده [English]

Considering the natural conditions of Iran, not paying attention to floods can cause irreparable damages, among which flood estimation and zoning of floodplain areas are very significant in controlling hazards, so zoning of climate change is necessary. The present study aims to investigate the risk of floods in selected basins of Khorasan Razavi using the VIKOR, L-THIA, and ANT models. Then, fourteen variables affecting the occurrence of floods including climate, land use, altitude, drainage density, geomorphological units, lithology, run-off height, permeability, slope and direction, distance to rivers/waterways, precipitation, temperature, and soil were used. The results showed that among the mentioned variables, climate parameters, land use, slope, drainage density, distance to rivers/waterways, precipitation, soil, and geomorphological units have greater effects on the occurrence of floods according to statistical calculations. Quantitative and qualitative evaluation of the results using various statistics showed that the L-THIA model, with a γ=0.8, had the highest correlation with the primary layers and was more accurate and efficient than the two VIKOR and ANT models in flood prediction.

کلیدواژه‌ها [English]

  • flood
  • basins
  • Gamma test
  • modeling
  • zoning
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