# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 4 Discussion and Conclusions  
The analysis of the CBR results showed a different contribution of the variables (properties) to the formation of similarity of the retrieved cases (Table 3). For this purpose, the quarters that received the highest scores and ensured the formation of I and II hazard classes were analyzed. According to these data (Table 3), it follows that the factors describing weather conditions made the greatest contribution to the formation of the total similarity. Their cumulative contribution was 63.8%. Next are the factors characterizing the state of the forest. Their total contribution was 36.2%. Thus, it can be argued that one of the reasons for the predominance of high-risk quarters in the southern part of the Bodaibinsk forestry and in the central and northern parts of the Kazachinsk-Lena forestry when vegetation differs is a small number of weather stations. This fact led to the use of the same weather data for a certain group of quarters. We evaluated the obtained CBR results by their comparison with the results of real monitoring for a certain time period. For our evaluation, we used the following accuracy score based on the calculation of a delta (difference) between CBR and monitoring values for forest quarters:  
\[ \begin{array}{l}\text{Accuracy}=1,\text{n}^{\text{CBR}}\geq\text{n}^{ \text{F}};\\ \text{Accuracy}=\text{n}^{\text{CBR}}/\text{n}^{\text{F}},\text{n}^{\text{CBR }}<\text{n}^{\text{F}}\end{array}  \tag{8}\]  
Figure 13: The forecasting results in the Kazachinsk-Lena forestry. where nF is a number of forest fires for the quarter with accordance of monitoring data; nCBR is a number of retrieved cases with a similarity more than 0.8 for the quarter; Accuracy is the accuracy score calculated in different ways with accordance to a nCBR value. We calculated the accuracy score for quarters of the Bodaibinsk and Kazachinsk-Lena forest districts. The average score value reached 0.51. Table 4 shows a comparison of the results obtained with the closest works [44; 45] and others [6; 8; 14; 47]. In particular, in [44; 45], authors used different datasets (case bases) and case models. In [44], they achieved 68.8% forecast effectiveness for a dataset of 560 cases. In [45], the use of CBR provided 58% of good predictions (which can be interpreted as an accuracy score) for a dataset with 2000 cases (which is commensurate with the case base in our work). At the same time, the combination of CBR with other technologies in [45] achieved good predictions of 66%. Other techniques with higher scores are used by researchers in [6; 8; 14; 47]. A more detailed consideration of approaches and assessments is presented in [18]. In this connection, our results look modest, but our work can be considered as a first stage of a larger investigation within the project \"Fundamentals, methods and technologies of digital monitoring and forecasting of the ecological situation of the Baikal natural territory\". In the future, using the experience of other researchers [8; 18; 45], we plan to increase the effectiveness of forecasting through the development of the case model as well as through the joint use of various technologies such as case-based, rules-based reasoning and machine learning methods. The solution considered in this paper has its limitations, disadvantages, and advantages. In particular, an important limitation is the availability of information on forest quarters and real fires. Therefore, in this work, the Bodaibinsk and Kazachinsk-Lena districts were selected since it was possible to form a complete dataset for them. The study also revealed a weakness in the coverage of Irkutsk Oblast by weather stations, which led to the use of the same weather data for groups of quarters, which generally lowered the accuracy of forecasting. A correlation analysis of variables (properties) was also not carried out in order to reduce the set and exclude dependent variables from processing, while the expediency of using aggregated indices was determined, as in, for example, [44].