Hybrid intelligent modeling of wild fires risk

被引：0
作者：
Vardis-Dimitris Anezakis
Konstantinos Demertzis
Lazaros Iliadis
Stefanos Spartalis
机构：
[1] Democritus University of Thrace,Laboratory of Forest Informatics, Department of Forestry and Management of the Environment and Natural Resources
[2] Democritus University of Thrace,Department of Civil Engineering, School of Engineering
[3] Democritus University of Thrace,Laboratory of Computational Mathematics, School of Engineering, Department of Production and Management Engineering
来源：
Evolving Systems | 2018年 / 9卷
关键词：
Fuzzy Chi Square test; Fuzzy cognitive maps; Correlation analysis; Forest fires; Climate change models;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Forest fires are one of the most serious natural disasters for the countries of the Mediterranean basin and especially for Greece. Studying the climate change effect on the maximization of the problem is a constant objective of the scientific community. This research initially proposes an innovative hybrid version of the statistical Chi-Square test that employs Soft Computing methods. More specifically it introduces the Fuzzy Chi Square Independence test that fuzzifies p values using proper Risk Linguistics, based on Fuzzy Membership functions. In the second stage, it proposes a new Hybrid approach that models the evolution of burned areas in Greece. First it analyzes the parameters and determines the way they affect the problem, by constructing Fuzzy cognitive maps. The system projects into the future and forecasts the evolution of the problem through the years till 2100, based on the variance of average monthly temperature and average rain height (due to climate change) for the months May–October based on various climate models. Historical data for the period 1984–2004 were used to test the system for the areas of Chania and Ilia.
引用
收藏
页码：267 / 283
页数：16
相关论文
共 121 条
[1]  
Anezakis VD(2016)A hybrid soft computing approach producing robust forest fire risk indices. IFIP Advances in Information and Communication Technology, AIAI September 2016 Thessaloniki Greece 475 191-203
[2]  
Demertzis K(2016)Interactions between climate, land use and vegetation fire occurrences in El Salvador Atmosphere 7 art. no. 26-14
[3]  
Iliadis L(2014)Fuzzy inference ANN ensembles for air pollutants modeling in a major urban area: the case of Athens Eng Appl Neural Netw Commun Comput Inf Sci 459 1-127
[4]  
Spartalis S(2015)Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning J Integr Comput Aided Eng 23 115-1206
[5]  
Armenteras D(2016)HISYCOL Α hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens J Neural Comput Appl 27 1191-63
[6]  
Gibbes C(2016)Semi-supervised hybrid modeling of atmospheric pollution in urban centers Commun Comput Inf Sci 629 51-17
[7]  
Vivacqua CA(2017)Interacting effects of topography, vegetation, human activities and wildland-urban interfaces on wildfire ignition risk For Ecol Manag 397 10-186
[8]  
Espinosa JS(2017)The normal fire environment-modeling environmental suitability for large forest wildfires using past, present, and future climate normals For Ecol Manag 390 173-710
[9]  
Duleba W(2015)Sequentially contingent fires, droughts and pluvials structured a historical dry forest landscape and suggest future contingencies J Veg Sci 26 697-12
[10]  
Goncalves F(2015)Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape For Ecol Manag 356 2-510
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