مجله جنگل ایران

مجله جنگل ایران

مدل‌سازی مکانی حساسیت‌پذیری آتش‌سوزی براساس تأثیر جادۀ جنگلی با استفاده از روش‌های یادگیری ماشین در جنگل‌های غرب استان مازندران

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استاد گروه جنگلداری و اقتصاد جنگل دانشکدۀ منابع طبیعی دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران
2 دانش‌آموختۀ دکتری عمران و بهره‌برداری جنگل، گروه جنگلداری و اقتصاد جنگل، دانشکدۀ منابع طبیعی دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران
3 استاد بخش علوم خاک، دانشکدۀ کشاورزی، دانشگاه شیراز
چکیده
مقدمه: جنگل‌های هیرکانی به لحاظ ارزش‌های زیستی و اکوسیستمی، از دارایی‌های بیولوژیکی گرانبها به شمار می‌روند. آتش‌سوزی در عرصه‌های منابع طبیعی از بحران‌هایی است که خسارت‌های جبران‌ناپذیری به بوم‌سازگان وارد می‌کند. هدف این پژوهش، بررسی و مدل‌سازی ارتباط مکانی شبکۀ جاده‌های جنگلی و حساسیت‌پذیری آتش‌سوزی است.
مواد و روش‌ها: عوامل مؤثر بر آتش‌سوزی شامل کاربری اراضی، فاصله از جاده، ارتفاع از سطح دریا، خاک، تیپولوژی، تراکم جاده، اقلیم و جهت شیب جاده‌های موجود بررسی و نقشۀ حساسیت آنها با استفاده از روش یادگیری ماشین مدل‌سازی جنگل تصادفی و روش آماری آنتروپی بیشینه تولید شد. با ارزیابی مدل‌های مورد استفاده توسط منحنی‌های مشخصۀ عملکرد و سطح زیر منحنی و منحنی‌های پاسخ و آزمون جک نایف، درصد اهمیت هر پارامتر در وقوع آتش‌سوزی و میزان اثرگذاری هر پارامتر در مدل‌سازی تعیین شد.
یافته‌ها: درصد اهمیت و مشارکت عوامل مؤثر در پتانسیل آتش‌سوزی برمبنای روش حداکثر آنتروپی نشان‌دهندۀ آن است که تأثیرگذارترین متغیرها بر مدل حساسیت‌پذیری وقوع آتش‌سوزی عبارت‌اند از ارتفاع از سطح دریا با 76 درصد، فاصله از جاده با 1/9 درصد و کاربری اراضی با 4/5 درصد. برمبنای مدل‌سازی تصادفی نیز به‌ترتیب بیشترین اثر را پارامترهای ارتفاع از سطح دریا با میانگین 1/0 درصد، فاصله از جاده با 9/0 درصد و کاربری اراضی و پوشش گیاهی با 4/0 درصد دارند. منحنی پاسخ فاصله از جاده و تراکم جاده بیانگر افزایش رخداد آتش‌سوزی نزدیک به جاده است و جاده‌ها نقش بسزایی در وقوع آتش‌سوزی در منطقه دارند.
نتیجه‌گیری: نقشه‌های حساسیت‌پذیری مکانی آتش‌سوزی نشان می‌دهد که فاصله از جاده، عامل مهمی است که تأثیر زیادی بر پتانسیل آتش‌سوزی دارد، یعنی می‌تواند بر افزایش رخداد و برعکس بر مهار آتش تأثیر بگذارد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Spatial Modeling of Wildfire Susceptibility Based on the Impact of Forest Roads Using Machine Learning Methods in the Western Forests of Mazandaran Province

نویسندگان English

S. A.O. Hosseini 1
N. Shafiee kigasari 2
H.R. Pourghasemi 3
1 Prof., Forest Engineering, Forestry and Forest Economics Dept. Faculty of Natural Resources, University college of Agriculture and Natural Resources, University of Tehran
2 Ph.D. graduated of Forest Engineering, Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University college of Agriculture and Natural Resources, University of Tehran
3 Prof., Dept. of Soil Science, College of Agriculture, Shiraz University
چکیده English

Introduction: The Hyrcanian forests are considered valuable biological assets due to their biodiversity  and ecosystem functions. Wildfires in natural resource areas are among the crises that cause irreparable damage to ecosystems. Therefore, the purpose of this study is to investigate and model the spatial relationship of the forest road network and wildfire susceptibility using modern machine learning methods in the cities of Tankabon and Ramsar.
Materials and methods: The factors affecting wildfire occurrence including land use, distance from the road, height above sea level, soil, typology, road density, climate and slope direction, including existing roads, were investigated and their sensitivity maps were obtained using the Random Forest machine learning method and maximum entropy statistical method. In order to evaluate the models used by receiver operating characteristic curves and the area under the curves and by the response curves and the jackknife test, the percentage of importance of each parameter in the occurrence of wildfire occurrence and its influence in  modeling were determined.
Results: According to Maximum Entropy method, the percentage of importance and participation of the effective factors in the wildfire potential show that the most influencing variables on the fire susceptibility model are height above sea level ( 76%), distance from the road (9.1%), land use (5.4%).  Also, based on the Random Forest model, the parameter of height above sea level with an average of 0.10%, distance from the road 0.9%, land use and vegetation 0.4% have the highest effect. The response curve for  distance from the road and  road density  indicate an increased occurrence of  wildfire  near roads, and roads play a significant role in the occurrence of fire in the region.
Conclusion: The wildfire spatial susceptibility maps show that road distance is an important factor that greatly influences wildfire potential; that is, it can both increase the likelihood of fire occurrence and, conversely, affect fire suppression.

کلیدواژه‌ها English

Random Forest modeling
Road
Western Mazandaran
Wildfire
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  • تاریخ دریافت 15 اسفند 1402
  • تاریخ بازنگری 13 آذر 1403
  • تاریخ پذیرش 18 دی 1403