| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,019 |
| تعداد مقالات | 33,734 |
| تعداد مشاهده مقاله | 44,975,189 |
| تعداد دریافت فایل اصل مقاله | 46,518,724 |
پهنه بندی و بررسی عوامل مؤثر بر آتشسوزی جنگل در شهرستان بویراحمد با استفاده از مدل های تابع شواهد قطعی و ماشین بردار پشتیبان | ||
| تحقیقات حمایت و حفاظت جنگلها و مراتع ایران | ||
| مقاله 7، دوره 17، شماره 2 - شماره پیاپی 34، اسفند 1398، صفحه 197-222 اصل مقاله (1.93 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfrpr.2020.128649.1406 | ||
| نویسندگان | ||
| مژگان بازیار* 1؛ جعفر اولادی قاد‌یکلایی2؛ حمیدرضا پورقاسمی3؛ محمدرضا سراجیان مارالان4 | ||
| 1جنگلداری- دانشکده منابع طبیعی- دانشگاه کشاورز ی و منابع طبیعی ساری | ||
| 2دانشیار، گروه جنگلداری، دانشکده منابع طبیعی، دانشگاه کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| 3استادیار، بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز،شیراز، ایران | ||
| 4استاد، گروه نقشه برداری، دانشکده نقشه‌برداری، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| جهت تعیین الگوی مکانی احتمال آتشسوزی در جنگل‌های شهرستان بویراحمد، از مدلهای تابع شواهد قطعی و ماشین بردار پشتیبان استفاده شد. به این منظور در ابتدا 145 موقعیت آتشسوزی گذشته براساس گزارشها، دادههایMODIS و با بررسیهای میدانی با استفاده ازGPS ثبت شد که از این تعداد، 70% برای مدلسازی و 30% به‌منظور اعتبارسنجی مدل استفاده گردید. در مرحله بعد 15 عامل (طبقات ارتفاعی، درجه شیب، جهت شیب، شاخص موقعیت توپوگرافی، شاخص رطوبت توپوگرافی، انحناء سطح، فاصله از روستا، فاصله از رودخانه، فاصله از جاده، سازندهای زمین شناسی، NDVI، کاربری اراضی، تبخیر و تعرق سالانه، بارندگی سالانه و درجه حرارت سالانه) برای بررسی خطر آتشسوزی انتخاب و نقشه‌های آن تهیه شد. بعد از انجام تست هم‌خطی بین متغیرهای مستقل، از مدلهای تابع شواهد قطعی و ماشین بردار پشتیبان برای ایجاد نقشه پهنه‌بندی آتشسوزی استفاده شد. برای مدلسازی، مکان‌های آتشسوزی رخ داده در گذشته مشخص شد و 70 درصد دادههای جمع‌آوری شده به‌عنوان دادههای آموزشی برای مدلسازی و 30 درصد داده‌ها جهت اعتبارسنجی مدل استفاده شد. نتایج مطالعه از طریق نقشه پهنه‌های آتشسوزی نشان داد که مناطق با حساسیت‌های خیلی‌زیاد و زیاد، 40 درصد منطقه را پوشش داده اند. نتایج اعتبارسنجی کارایی مدل های تابع شواهد قطعی بیان‌گر سطح زیر منحنی برابر با 72.2 درصد و ماشین بردار پشتیبان با سطح زیر منحنی 83 درصد بوده و الگوریتم ماشین بردار پشتیبان در منطقه مورد مطالعه توانست احتمال وقوع آتشسوزی را بهتر پیش‌بینی کند. از نتایج تحقیق پیش‌رو برای برنامه ریزی و مدیریت خطر آتشسوزی‌های آینده در منطقه مورد مطالعه بهره برد. | ||
| کلیدواژه‌ها | ||
| مدلسازی مکانی آتشسوزی؛ تابع شواهد قطعی؛ مدل ماشین بردار پشتیبان؛ هم‌خطی متغیرها؛ شهرستان بویراحمد | ||
| عنوان مقاله [English] | ||
| Zoning and Investigation of Factors Affecting Forest Fire Using Evidential Belief Function Algorithm and Support Vector Machine in Boyer Ahmad City | ||
| نویسندگان [English] | ||
| Mozhgan Bazyar1؛ J. Oladi Ghadikolaii2؛ H.R. Pourghasemi3؛ M.R. Serajyan maralan4 | ||
| 1forestry- natrural resorce colege- sari university- sari-iran | ||
| چکیده [English] | ||
| In order to determine the spatial pattern of the probability of fire in the forests of Boyerahmad city, Belife evidence function models and support vector machines were used. For this purpose, at first 145 past fire positions were reported, MODIS data and field surveys were recorded using GPS, of which 70% were used for modeling and 30% for model validation. Next, 15 factors (altitude, slope gradient, slope direction, topographic position index, topographic moisture index, surface curvature, distance from village, distance from river, distance from road, geological formations, NDVI, land use, evapotranspiration annual, annual rainfall and annual temperature) were selected to assess the fire risk and maps were prepared. After performing a linear test between the independent variables, the Belife evidence function and the support vector machine models were used to create the fire zoning map. For modeling, past fire locations were identified and 70% of the data collected were used as training data for modeling and 30% for model validation. The results of the fire map study showed that areas with very high sensitivity cover 40% of the area. The results of the validation of the performance of the Belife evidence function models showed that the area under the curve was equal to 72.2% and the support vector machine with the area below the curve was 83%.Predication. Results of the current research can be used to plan and manage future fire hazards in the study area. | ||
| کلیدواژه‌ها [English] | ||
| : Spatial modeling of fire fighting, Evidential Belief Function, variables, Support Vector Machine, Boyer Ahmad city | ||
| مراجع | ||
|
-Adab, H., Kanniah, K.D. and Solaimani, K. 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques, Natural hazards, 65(3):1723-1743. -Agee, J.K., Bahro, B., Finney, M.A., Omi, P.N., Sapsis, D.B., Skinner, C.N., Van Wagtendonk, J.W. and Weatherspoon, C.P. 2000. The use of shaded fuelbreaks in landscape fire management. Forest ecology and management, 127(1-3): 55-66. -Bahery, H., Ghods Khah, M. and Pour Babaii , H. 2018. Long-term effects of wildfire on wood species composition and natural regeneration in Hyrcanian forests(Case study, Lesakoti forest of Tonekabon, Mazandaran State). Ecology of Iranian Forests, 9(5): 37-46. -Banjeshafiee, A. and Beigi, H. 2014. Evaluation of Fuzzy Linear Combination Method for Forest Fire Risk Mapping (Case Study: Sardasht Forest, West Azarbaijan) (Case study: Sardasht Forest, West Azarbaijan). Science and Technology Researches for Wood and Forest, 23(3). -Bowman, D.M., Balch, J., Artaxo, P., Bond, W.J., Cochrane, M.A., D’antonio, C.M., DeFries, R., Johnston, F.H., Keeley, J.E., Krawchuk, M.A. and Kull, C.A. 2011. The human dimension of fire regimes on Earth. Journal of biogeography, 38(12): 2223-2236. -Carranza, E.J.M. and de Palomera, R.A. 2005. Evidential belief mapping of epithermal gold potential in the Deseado massif, Santa Cruz Province, Argentina. In Actas del XVI congreso geologico Argentino, 19-23 September 2005, La Plata. La Plata: Instituto de Recursos Minerales (INREMI) 2005, 451-458. -Carranza, E.J.M., Van Ruitenbeek, F.J.A., Hecker, C., van der Meijde, M. and van der Meer, F.D. 2008. Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. International Journal of Applied Earth Observation and Geoinformation, 10(3): 374-387. -Chen, F., Du, Y., Niu, S. and Zhao, J. 2015. Modeling forest lightning fire occurrence in the Daxinganling Mountains of Northeastern China with MAXENT. Forests, 6(5): 1422-1438. -Cortez, P. and Morais, A.d.J. R. 2007. A data mining approach to predict forest fires using meteorological data. Paper presented at the Associação Portuguesa para a Inteligência Artificial (APPIA), Portugal. de Vasconcelos, M.P., Silva, S., Tome, M. and Alvim, M. 2001. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric engineering and remote sensing, 67(1): 73-81. -Dong, X., Li-min, D., Guo-fan, S., Lei, T. and Hui, W. 2005. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of forestry research, 16(3):169-174. -Ebrahimi, H. 2016. Modeling the areas prone to fire using the maximum disruption model under the WebGIS system Case Study: Forests and Rangelands of East Azarbaijan Province. State - Ministry of Science, Research, Technology - Tabriz University - Faculty of Geography. -Ercanoglu, M. and Gokceoglu, C. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental geology, 41(6). -Eskandari, S., Oladi, J., Jalilvand, H. and MH, S. 2012. Modeling and forecasting the risk of fire in forests the third section is used by the Geographic Information System (GIS). Thghighat Jangal va Senobar Iran, 21(2). -Flannigan, M.D. and Haar, T.V. 1986. Forest fire monitoring using NOAA satellite AVHRR. Canadian Journal of Forest Research, 16(5): 975-982. -Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M. and Haussler, D.J.B. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. 16(10): 906-914. -Geravand, S., YarAli, N.B. and Sadeghi, H.A. 2012. Spatial pattern and risk map of fire in natural lands of Lorestan province. Forest and Poplar Research of Iran, 21(2). -Guo, F., Wang, G., Su, Z., Liang, H., Wang, W. and Lin F,E.A. 2016. What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests. International Journal of Wildland Fire, 25(5): 505-519. -Hernandez-Leal, P., Arbelo, M. and Gonzalez-Calvo, A. 2006. Fire risk assessment using satellite data. Advances in Space research, 37(4):741-746. -Hosmer Jr, D.W., Lemeshow, S. and Sturdivant, R.X. 2013. Applied logistic regression (Vol. 398). John Wiley & Sons. -Izmailov, R., Vapnik, V. and Vashist, A. 2013. Multidimensional splines with infinite number of knots as SVM kernels. Paper presented at the The 2013 International Joint Conference on Neural Networks (IJCNN). -Jaafari, A. and Pourghasemi, H. R. 2019. Factors Influencing Regional-Scale Wildfire Probability in Iran: An Application of Random Forest and Support Vector Machine. In Spatial Modeling in GIS and R for Earth and Environmental Sciences, 607-619. -Jafarri, A., Rezaeian, J. and Omrani, M. S.O. 2017. Spatial prediction of slope failures in support of forestry operations safety. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 38(1). -Jaiswal, R.K., Mukherjee, S., Raju, K.D. and Saxena, R. 2002. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation Geoinformation, 4(1): 1-10. -Jazirehi, M. and Ebrahimi Rastaghi, M. 2004. Silviculture of Zagros forests, Tehran University Press, 560p (In Persian). -Jenness. 2002. Surface areas and ratios from elevation grid (surgrids. avx) extension for ArcView 3. x, v. 1.2. Jenness Enterprises. -Johnson, E., Miyanishi, K. and Weir, J. 1998. Wildfires in the western Canadian boreal forest: landscape patterns and ecosystem management. Journal of Vegetation Science, 9(4): 603-610. -Kushla, J.D. and W.J,R. 1997. The role of terrain in a fire mosaic of a temperate coniferous forest. Forest Ecology and Management,95(2). -Leuenberger, M., Parente, J., Tonini, M., Pereira, M.G. and Kanevski, M. 2018. Wildfire susceptibility mapping: Deterministic vs. stochastic approaches. Environmental Modelling & Software, 101. -Li, R. and Wang, N.J.S. 2019. Landslide susceptibility mapping for the Muchuan county (China): A comparison between bivariate statistical models (woe, ebf, and ioe) and their ensembles with logistic regression.11(6), 762. -Maghsoudi, M. and Rahmati, M. 2018. Gemorphsites A ssessment of Lorestan province in Iran by comparing of zouros and comanescu’s methods ( case study:Poldokhtar area, Iran). GeoJournal of Tourism and Geosites, 21(1): 226p (In Persian). -Nami, M., Jaafari, A., Fallah, M. and Nabiuni, S. 2018. Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. International journal of Environmental Science Technology, 15(2): 373-384. -Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A. and Pereira, J.M. 2012. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Journal of Forest Ecology Management, 275: 117-129. -Parisien, M.A., Snetsinger, S., Greenberg, J.A., Nelson, C.R., Schoennagel, T., Dobrowski, S.Z. and Moritz, M.A. 2012. Spatial variability in wildfire probability across the western United States.International Journal of Wildland Fire, 21(4): 313-327. -Pourghasemi, H.R., Beheshtirad, M. and Pradhan. 2016. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards, 7(2): 861-885. -Pourghasemi, H.R., Jirandeh, A.G., Pradhan, B., Xu, C. and Gokceoglu, C.J.J.o.E.S.S. 2013. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. 122(2): 349-369. -Pourtaghi, Z.S., Pourghasemi, H.R., Aretano, R. and Semeraro, T. 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological indicators, 64: 72-84. -Pourtaghi, Z.S., Pourghasemi, H.R., and Rossi, M. 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran.Environmental earth sciences., 73(4):1515-1533. Pradhan, B., Abokharima, M.H., Jebur, M.N. and Tehrany, M.S. 2014. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Natural hazards, 73(2). -Rahimi, I., Esmaeili, A., Tafiqi, A. and Mahmoudi, F. 2010. Modeling the effects of vegetation on the potential of forest fires Forests using remote sensing technology and satellite images of the MODIS sensor Case study of Marivan forests. Paper presented at the First International Conference on Plant, Water, Soil and Air Modeling. -Sadeghi Far, M., Beheshti Al Agha, A. and Pour Reza, M. 2017. Variability of Soil Nutrients and Aggregate Stability at Different Post-Fire Times in Zagros Forests (Case Study, Paveh forest). Ecology of Iranian Forests, 8(4): 19-27 (In Persian). -Sakr, G.E., Elhajj, I.H., Mitri, G. and Wejinya, U.C. 2010. Artificial intelligence for forest fire prediction. Paper presented at the International Conference on Advanced Intelligent Mechatronics. -Samantarai, S., Nag, A., Singh, N., Dash, D., Basak, A., Nando, G.B. and Das, N.C. 2019. Chemical modification of nitrile rubber in the latex stage by functionalizing phosphorylated cardanol prepolymer: A bio-based plasticizer and a renewable resource.Journal of Elastomers Plastics, 51(2): 99-129. -Somashekar, R., Ravikumar, P., Kumar, C.M., Prakash, K. and Nagaraja, B. 2009. Burnt area mapping of Bandipur National Park, India using IRS 1C/1D LISS III data. Journal of the Indian Society of Remote Sensing, 37(1): 37-50. -Syphard, A.D., Radeloff, V.C., Keuler, N.S., Taylor, R.S., Hawbaker, T.J., Stewart, S.I. and Clayton, M.K. 2008. Predicting spatial patterns of fire on a southern California landscape. International Journal of Wildland Fire, 17(5): 602-613. Thach, N.N., Ngo, D.B.T., Xuan-Canh, P., Hong-Thi, N., Thi, B.H., Nhat-Duc, H. and Dieu, T.B. 2018. Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study. Ecological informatics, 46: 74-85. -Vasilakos, C., Kalabokidis, K., Hatzopoulos, J. and Matsinos, I. 2009. Identifying wildland fire ignition factors through sensitivity analysis of a neural network. J Natural hazards, 50(1): 125-143. -Weise, D.R. and Biging, G.S. 1997. A qualitative comparison of fire spread models incorporating wind and slope effects. Forest Science, 43(2). -Wijayanto, A.K., Sani, O., Kartika, N.D. and Herdiyeni, Y. 2017. Classification model for forest fire hotspot occurrences prediction using ANFIS algorithm. Paper presented at the In IOP Conference Series: Earth and Environmental Science. -Yosefi, M. and Rahimian, J. 2013. Forests of Kohgiluyeh and Boyerahmad province. Shiraz.,Rayehe honar pars.(In Persian): Rayehe honar pars. -Zeng, T., Hudson, J., Kay, S., Laginestra, E. and Authority, S.O.P. 2003. A fuzzy GIS approach to fire risk assessment: a case study of Sydney Olympic Park, Australia. Paper presented at the Spatial Sciences Conferences.
| ||
|
آمار تعداد مشاهده مقاله: 645 تعداد دریافت فایل اصل مقاله: 487 |
||