# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 3 Forecasting the Risk of Forest Wildfires Using a Case-Based Approach
### Results (Employed Works)
#### 3.3.4 Prototyping a Case Base  
The proposed approach was tested by creating and debugging a prototype of the case base for wildfire risk forecasting. The personal knowledge base designer (PKBD) [23] is our software that was used as a mean of prototyping. Creating a prototype of a case base includes the following steps:  
1. Preparing CSV files with decision tables for PKBD import;
2. Importing CSV files containing information for cases with the creation of a knowledge base;
3. Testing imported data by queries with subsequent correction of the data. Below, we describe these steps in detail. Step 1 involves preparing initial data in the form of CSV files for importing to PKBD. The form of decision tables supported by PKBD has the following features [67]: the ability to include a column with the names of rules to the table structure; the ability to indicate dependent columns with the symbol \"#\"; the ability to specify compound column names,  
Figure 5: A fragment of the conceptual model of the domain. including the name of the entity (or class name) and the name of its property, separated by the string \"::\"; the absence of restrictions on the values in the cells; i.e., they can consist not only of a set of values {yes, no} representing a degenerate form of truth tables, but they allow one to use specific arbitrary values as cell values instead of only values indicating the presence or absence of a certain property (component) in the case structure. In our case, the following table structure (headers) is used to determine the hazard class, which is formed based on the case model: id; fire id; new fire id; dt; lat; lon; fire territory; fire area; municipalities; population density; forestry; weather station id; weather station name; RRR (the amount of precipitation); Ff (wind strength); U (relative humidity of the air); T (air temperature); Td (dew point temperature); DD (wind direction); WW (current weather reported from the weather station); Po (atmospheric pressure); area; distance to the nearest motorway; distance to the nearest railway; distance to the nearest railway; distance to the nearest reservoir; forest zone; forest-seed zoning areas; weather hazard class; snowiness; snowiness uncertainty; name locality; name MO locality; municipalities locality; distance to locality; dry thunderstorm; #kv; #forest hazard classes. An example of a decision table is shown in Figure 6, with each row corresponding to a description of a separate case. Step 2 allows one to create the structure of a knowledge base during import or just to add data to an existing one. In the first case, the titles of the table columns are used as the structure of cases. Figure 7 shows an example of the PKBD GUI after importing a list of cases and a structure (a template) of a case. In this step, the user can correct the existing cases or add new ones. During the import process, scales (ranges) of possible values for certain properties of cases are also formed. Step 3 involves making queries to the case base obtained. When making queries, the importance of separate properties can be defined. The importance of properties or information weight is a subjective value that affects the assessment of similarity. The scale (1; 100) is used to determine it. As can be seen in Figure 7, case properties describing weather data, features of surface and vegetation, and proximity of infrastructure are involved in the retrieval (queries). The remaining parameters (lat-long, fire territory and district names, etc.) are used only as additional information when interpreting the results. As a result of retrieval, the cases are ranked by the value of similarity. Figure 8 shows the results of a query in the form of a list of similar cases with the ability to drill down into its detailed comparative preview (Figure 9). The resulting prototype of the case base contains information on 2240 wildfires in Irkutsk Oblast for 2017 and 2020. Next, we used the obtained case base and case-retrieval modules of PKBD for wildfire risk forecasting.