Forecasting the Occurrence of Forest Fires

At the moment, forest fire forecasting is a large-scale field of research, the progress of which can give significant practical results. Natural disasters often cause widespread harm, and people try to cope with them year after year. Modern technologies may well reduce their impact on human life and minimize the costs of dealing with the consequences. To solve this problem, a model based on artificial intelligence methods for predicting forest fires will be created. Since the resources required to eliminate fires are huge, presented model should be the most effective. For this purpose, a comparison and analysis of classification algorithms such as XGBoost and the random forest method will be carried out. Presented paper will also refer to the Poisson algorithm, which is often used in this subject area, and compare its work with the mathematics of the methods that have been chosen. The final solution will not only be described in detail in Jupyter, but will also be similarly assembled on the Razum AI platform. Thus, the second goal of this paper is to investigate the possibility of using visual design platforms to solve problems in similar subject areas and compare the effectiveness and labor costs of methods.