Using autologistic regression to simulate the forest fire development based on a cellular automata
عنوان مقاله: Using autologistic regression to simulate the forest fire development based on a cellular automata
شناسه ملی مقاله: ICSAU05_0628
منتشر شده در پنجمین کنگره بین المللی عمران ، معماری و توسعه شهری در سال 1396
شناسه ملی مقاله: ICSAU05_0628
منتشر شده در پنجمین کنگره بین المللی عمران ، معماری و توسعه شهری در سال 1396
مشخصات نویسندگان مقاله:
h Sahraiian - GIS MSc student, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran,
p Pahlavani - Assistant professor, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran,
b Bigdeli - Assistant professor, School of Civil Engineering, College of Engineering, Shahrood University of Teechnology, Shahrood, Iran,
خلاصه مقاله:
h Sahraiian - GIS MSc student, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran,
p Pahlavani - Assistant professor, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran,
b Bigdeli - Assistant professor, School of Civil Engineering, College of Engineering, Shahrood University of Teechnology, Shahrood, Iran,
The forest fire is one of the natural disasters that menaces the large parts of the forests. Forest fires proliferate increasingly due to oxygen resource depletion, greenhouse gases, and rising global temperatures. Due to the devastating effects of forest fires, research on modern methods for simulation forest fire is essential. In this research, Golestan forest fire development has been simulated based on a cellular automata and an autologistic regression was used for calibration. In the proposed method, 5 m spatial resolution has higher accuracy than the other spatial resolutions. Also, using Moore neighborhood filter is better than the Extended Moore neighborhood filter. At the best case in this research, the Kappa index, overall accuracy, and relative operating characteristic (ROC) obtained 79.7%, 87.1% and 84.4%, respectively
کلمات کلیدی: Simulation, Forest Fire, Cellular Automata, Autologistic Regression
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/735042/