Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning

Abstract

Forest fire is one of the serious threats to the population and infrastructure of Irkutsk Oblast because its territory is heavily forested. This paper discusses the main stages of solving the problem of forecasting the risk of forest fires via a case-based approach, including data preprocessing, formation of a case model, and creation of a prototype of a case-based expert system. The main contributions of the paper are the following: a case model that provides a compact representation of information about weather conditions, vegetation type, and infrastructure of the region in relation to the possible risk of a wildfire; a case-base containing information about wildfires in Irkutsk Oblast for the period from 2017 to 2020; and a methodology for creating prototypes of case bases providing the transformation of decision tables of a special type. The approbation of the approach was carried out for separate forest districts, namely Bodaibinsk and Kazachinsk-Lena. The accuracy score was used for the evaluation of the results of forecasting the risk of wildfires. The average score value reached 0.51. The evaluation results revealed that application of the case-based approach can be considered as the initial stage for deeper investigations with the use of different methods (data mining, neural networks) for more accurate forecasting.

Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest

“…As part of the study, we carried out a wildfire risk analysis using case-based reasoning (CBR) [22,81] and RF methods.…”

“…In particular, it is common to use neural networks [26][27][28][29][30][31][32][33] to build fire hazard maps for the territories of Portugal [26], Spain [27], Iran [29,30], Vietnam [31], and China [32]. The Naive Bayes method [3,51] is used for the territory of Iran, neuro-fuzzy systems are employed for the forests of Chile [17], Brazil [18], Vietnam [20] and Iran [21], the Random Forest method is applied to Mediterranean Europe [34], Ethiopia [36] and China [32,35,39], and GIS-based multicriteria decision analysis methods (GIS-based multi-criteria decision analysis (MCDA)) [52], analytical hierarchical process (AHP) [19,48,53,54] and case-based reasoning [22][23][24] are used for this purpose, too.…”

“…In [22], we forecast the risk of wildfires in certain areas of Irkutsk Oblast (Bodaibinsky and Kazachinsko-Lensky Districts) based on case-based reasoning. This method was chosen as the most effective for preliminary analysis of the obtained data set.…”

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

“…Case-based reasoning (CBR) is a machine learning algorithm proposed by Aamodt and Plaza et al in 1994 that mimics the analogical reasoning in the human brain [46]. CBR consists of four basic processes: case representation, case retrieval, case reuse and case retain [30,47]. The schematic diagram of CBR is depicted in Figure 3, and the specific steps are as follows:…”

“…As a mature branch of artificial intelligence, case-based reasoning (CBR) has been widely applied in other fields [23]. CBR has greater classification performance compared with traditional data mining methods [24] and it has also shown excellent performance in fields like fault diagnosis [25][26][27], risk assessment [28,29], and forest fire prediction [30][31][32]. It should be noted that the weights of case characteristic attributes in CBR have a significant impact on the prediction performance of the model.…”