مقایسه‌‌ عملکرد مدل‌‌های اولیه و بهینه‌شده اتوماسیون سلولی در پیش‌بینی گسترش آتش‌سوزی جنگل

نوع مقاله : علمی- پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد جنگل‌داری، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

2 استاد، گروه جنگل‌داری، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

3 استادیار، گروه علوم و مهندسی جنگل، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران

چکیده

مدل‌‌ اتوماسیون سلولی به‌عنوان یکی از پرکاربردترین مدل‌‌های شبیه‌‌سازی گسترش آتش، نیازمند به‌کارگیری پارامترهای مناسب و ضریب‌های دقیق هستند. هدف اصلی پژوهش پیش‌رو، مقایسه‌‌ مدل‌‌های اولیه اتوماسیون سلولی و مدل بهینه‌شده با استفاده از الگوریتم کلونی زنبورعسل به‌منظور پیش‌بینی نرخ گسترش آتش‌ در یک جنگل دست‌کاشت در شمال شرقی استان گلستان است. با تهیه نقشه واقعیت زمینی نمونه‌ای، قابلیت داده‌های طیفی نوری و راداری ماهواره‌‌های سنتینل 1 و 2 برای تهیه نقشه تیپ و تراکم پوشش گیاهی مورد نیاز در مدل‌سازی گسترش آتش به‌عنوان هدف فرعی بررسی شد. پس از تصحیح تصاویر و استخراج شاخص‌های گیاهی، نقشه‌های نوع و تراکم پوشش گیاهی با استفاده از الگوریتم جنگل تصادفی تهیه شد. نتایج ارزیابی صحت نشان داد که بهترین نتیجه با تلفیق داده‌‌های نوری و راداری به‌دست می‌آید (صحت کلی و ضریب کاپا به‌ترتیب 81/0 و 75/0). مدل‌سازی گسترش آتش با استفاده از مدل‌های اولیه و نیز با ضریب‌های بهینه‌شده در پژوهش‌های پیشین انجام شد. به‌منظور بهبود نتایج و بررسی مقایسه‌ای، ضریب‌های مدل براساس شرایط منطقه با استفاده از الگوریتم کلونی زنبورعسل بهینه‌سازی شد. سپس، مدل‌سازی تکرار شد و با آتش‌سوزی واقعی مقایسه شد. نتایج نشان داد که مدل‌های بهینه‌شده با الگوریتم کلونی زنبورعسل (بهینه‌شده Alexandridis و همکاران (2011) با صحت کلی، ضریب کاپا و ضریب سورنسن به‌ترتیب 92/0، 74/0 و 78/0 و بهینه‌شده Alexandridis و همکاران (2008) به‌ترتیب 93/0، 74/0 و 78/0) مطابقت بیشتری با آتش‌سوزی واقعی در مقایسه با مدل‌های اولیه و بهینه‌شده پیشین داشتند.

کلیدواژه‌ها

عنوان مقاله [English]

Comparative study of basic and Bee Colony-optimized models cellular automation for prediction of wildfire spread

نویسندگان [English]

  • M.W. Alhaj khalaf 1
  • Sh. Shataee 2
  • R. Jahdi 3
1 M.Sc. Student of Forestry, Department of Forestry, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2 Prof., Department of Forestry, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
3 Assistant Prof., Department of Forest Sciences and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
چکیده [English]

As one of the most widely used models for fire spread simulation, cellular automata models commonly require appropriate parameters and the optimized coefficients. The main aim of this study was to compare the basic models of cell automation set by previous studies and optimized models using the Bee Colony Algorithm (ABC) to predict the rate of fire spread in a reforestation area in northeastern Golestan province, Iran. Furthermore, a sub-objective was to test the ability of optical and radar sensors (Sentinel 1 and 2) for mapping the vegetation stand type and density required for fire spread modeling. Following pre-processing and extraction of vegetation indices, vegetation type and density were mapped by the Random Forest algorithm. The accuracy assessment showed that the best result was obtained by combining the optical and radar data (Kappa coefficient (KC) = 0.75 and Overall accuracy (OA) = 0.81). Moreover, the fire spread was modeled using previous and optimized coefficients from previous studies. Model coefficients were optimized based on environmental conditions using the BCA algorithm and were compared with the occurred fire to improve the results and comparative evaluation. The results showed that the optimized models were more consistent (Sorenson coefficient (SC) = 0.78; KC = 0.74 and OA = 0.93 for Alexandridis et al., 2011; and SC = 0.78; KC = 0.74 and OA = 0.92 for Alexandridis et al., 2008) with the observed fire than the other applied cellular automation models.

کلیدواژه‌ها [English]

  • Bee colony algorithm
  • Golestan Province
  • optimization
  • reforestation
  • Sentinel

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