# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 1 Introduction  
Wild and man-made fires remain a serious problem all over the world [1; 2; 3; 4; 5; 6]. They have a detrimental effect on the composition and structure of fauna and flora and on the quality of air, soil, and water [7; 8], which generally leads to degradation of ecosystems [9]. In addition, they are a serious threat to people and infrastructure [10; 11]. Forest fires are caused by natural and climatic conditions (in particular, dry thunderstorms), careless handling of fire by the local population when picking berries and mushrooms, burning of grass, etc., while the main causes very often depend on the climatic or socio-cultural characteristics of the regions [12; 13; 14]. Effective monitoring and forecasting of their development is the basis for competent resource planning and informed decision making [15; 16], including through the use of remote sensing methods [14; 17]. This task is especially relevant for Irkutsk Oblast, the territory with the highest forest cover (78%) among the subjects of the Russian Federation, where fire-hazardous confierous plantations predominate (more than 90% of the entire area covered by forest) [13]. To solve the forecasting task, different methods and tools based on machine learning [8; 18], decision-making methods [14], fuzzy logic [19], clustering analysis [5], etc., are used. All of them require certain conditions and data preprocessing. In this paper, we apply a case-based approach [20] from our experience for solving forecasting tasks in the fields of petrochemistry and technogenic emergencies [21; 22]. The use of case-based reasoning (CBR) is considered in the context of solving the task of forecasting the risk of wildfires for Irkutsk Oblast, including data preprocessing, formationof a case model, creation of a prototype of a case-based expert system and its debugging, integration of a prototype into a forest fire monitoring web service, and its evaluation. CBR provides the effective use of accumulated experience in the absence of in-depth knowledge, using only superficial knowledge, i.e., knowledge about the visible relationships between events and facts in the domain. In this case, there is a direct mapping of the domain model, presented, for example, in a conceptual or set-theoretic form, into a case model, excluding complex training procedures, analysis, and evaluation of the parameters required for the employment of machine learning methods. The main contributions of this paper that determine its novelty are the following:  
* A domain model and a case model that provide a compact representation of information about weather conditions, vegetation type, and infrastructure of the region in relation to the possible risk of a wildfire. Information from various sources was used to build these models: GIS data, satellite images, weather stations data, literature, statistics, forest regulations, and expert assessments;
* A case base containing information about 2240 fires in Irkutsk Oblast for the period from 2017 to 2020. This case base was formed on the basis of the database that includes more than 45,000 records describing information about thermal points identified as a result of analysis of satellite images;
* A methodology for creating prototypes of case bases, providing the transformation of decision tables of a special type. The methodology includes the use of an original tool, namely personal knowledge base designer (PKBD) [23], and contains the following steps: preparing CSV files with decision tables for PKBD import, importing CSV files containing information for cases with the creation of a knowledge base, and testing imported data by queries with subsequent correction of the data. This methodology is a specialization of the approach from [24] for case bases;
* The results of evaluating the effectiveness of solving the task of forecasting the risk of wildfires based on CBR.