# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 2 Background  
### Forecasting the Risk of Forest Fires  
An analysis of recent works in the field of fire forecasting [8; 14; 18; 19] has shown a fairly large number of different methods and techniques for forest fire susceptibility and risk mapping. To solve this problem, probabilistic-based models such as Maxent models, fuel moisture content (FMC), and fire area simulator (FARSITE) [25; 26; 27] are proposed. The following methods are also used: machine learning [8; 18; 28] (including artificial neural networks (ANNs) [29; 30; 31; 32; 33]), fuzzy logic [14; 19; 34; 35; 36], clustering analysis [5], GIS-based multi-criteria decision analysis (MCDA) techniques, analytical hierarchy process (AHP) [15; 37; 38; 39], fuzzy AHP [40], frequency ratio [41], logistic regression [19; 29; 31; 42; 43], Bayesian modeling [3], and CBR [44; 45].  
At the same time, CBR [20] allows one to combine parameters with quantitative (numerical) and qualitative (verbal) values. The main advantage of its application is the possibility of obtaining results without the model training stage. CBR is usually used to obtain preliminary results in order to further justify the use of other methods that require deeper research of the domain, but it also provides more accurate assessments [45].  
The result of applying these methods and models is a risk assessment, for which various scales are used, in particular, the Canadian System of Weather Indices for Forest Fires (CFFWIS) [46]. However, the direct application of the described methods and scales requires consideration of the individual properties of initial data and the studied areas [47]. These properties have the constant and dynamic character and influence the ability of ignition, spread rate, control difficulty, and fire impact [8]. The main requirements for wildfire occurrence and spread [48] are favorable climatic circumstances, the presence of fuel, its spatial continuity, and a source of ignition. Weather factors, human behavior, vegetation traits, fuel availability, and topography are among the most essential [49]. Some sources also propose division of the factors into groups [8]: (i) topography (or multi-class landscape indicators [6]), (ii) climatic, and (iii) fuel. Based on these works, it is possible to form a general list of factors that can be used to predict the development of a wildfire: height above sea level; terrain slope; topographic humidity index; distance from urban areas; average annual temperature; land use; distance from roads; average annual precipitation; distance to the river; air temperature (average daily and maximum); dates of average daily temperatures passing through threshold limits; dates of onset and descent of stable snow cover; relative humidity (average daily and minimum); lack of air humidity; the number of days with a relative humidity of \(\leq\)30% in one of the observation periods for a certain period; the annual precipitation period; the number of days with rain; the dryness index; the weather regime; the number of days with thunderstorms, etc.  
In the context of this research, we can single out studies focused on taking into account the peculiarities of the climatic zones of the Russian Federation and Irkutsk Oblast. We aim to provide an improvement of the scale for assessing fire hazard classes of forests depending on weather conditions [50] in order to introduce new factors, in particular, humidity indicators [51]. We also take into account regional characteristics [52; 53] as well as existing methods for assessing fire safety [54]. The works considered above were used to analyze the domain and identify factors affecting the assessment of wildfire hazard.