# 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)
#### 3.3.5 Forest Fire Risk Forecasting Based on CBR  
To test the approach proposed, we selected two of the forest districts of Irkutsk Oblast, namely Bodaibinsk and Kazachinsk-Lena. First one includes 2818 separate quarters and the second 3695. The forecasting risk (a hazard class) of forest fires for each quarter based on CBR contains the following stages:  
(1) Defining the weather condition and other properties of certain forestry for a certain date: In particular, the following properties are used as the main ones: forest_hazard_classes, RRR (the amount of precipitation), Ff (wind strength), U (relative humidity of the air), T (air temperature), DD (wind direction), WW (current weather reported from the weather station), W1 (past weather between observation dates 1), W2 (past weather between observation dates 2), Po (atmospheric pressure), weather_hazard_class, snowiness, forest_zone, forest_seed_zoning zones, and thunderstorm; then, we used these data as a problem description (query) for case retrieval. (2) Retrieving cases using our prototype of case base and software: The case retrieval procedure uses the similarity metrics presented in Section 2.3 and is divided into two sub-steps: (2.a) calculating the similarity for each case and (2.b) summing the retrieved cases by similarity intervals, which are then used to interpret the results. As a result of (2.a), a detailed description of the calculated similarities is formed, indicating a certain variable (property) and its contribution to the total similarity (Figure 10). In this case, the final score is calculated as a weighted linear convolution (Equation (1)). The result of (2.b) is a list of forest quarters characterized by information of a number of cases with different similarities. We used the following intervals for summing cases with accordance of a similarity: 0-0.5, 0.5-0.8, and 0.8-1 (Figure 11). Figure 9: An example of a PKBD GUI form: detailed preview of the CBR results. (RRR, the amount of precipitation; Ff, wind strength; U, relative humidity of the air; T, air temperature; Td, dew point temperature; DD, wind direction (rumba); WW, current weather reported from the weather station; W1, past weather between observation dates 1; W2, past weather between observation dates 2; Po, atmospheric pressure). To highlight the importance of values, a color indication is used: when comparing properties, close values are displayed in green, non-close values are displayed in red; when showing similarity, a high similarity is displayed on a green background, a pale pink background is used for a low one, and yellow and blue colors are also possible for intermediate values of similarity. The intervals are defined based on the opinion of experts. Since data on real fires are compared, it is assumed that if the similarity of cases tends to 1, then this fact significantly increases the probability of a wildfire. The score value of 0.8 indicates that 20% of the signs of cases are allowed to mismatch when the current situation is assessed as dangerous. To determine statistical reliability, it is proposed to assess a high risk if there are more than five cases describing the current situation in which the similarity is in the range from 0.8 to 1. An estimate value of 0.5 indicates that only 50% of the signs of cases coincide with the current situation. In this case, we assume that the risk of wildfire under these conditions is moderate. Lower risk indicators are not identified at this stage of research. In the future, when receiving new data and conducting experiments, the values of the intervals will be refined. (3) Interpreting the case retrieval results using the following heuristic decision-making rules and defining hazard class according to the scale, where I is very high, II is high, III is moderate, IV is low, and V is very low:  
1. IF no cases, THEN forest fire risk is improbable (hazard class V);
2. IF there are cases with a similarity from 0 to 0.5, THEN forest fire risk is least probable (hazard class IV);
3.