Forest fire prediction
and analytics app
Development and maintenance of applications based on distributed services. Migration of parts of the platform to the AWS cloud. Preparing to migrate payments to Stripe.
A non-profit data collection and analytics application that has the aim of predicting the effects of forest fires in the US and Canada. The application generates advanced reports using artificial intelligence to analyze extensive state documents and a computing platform with a postGIS plug-in for interpretation and saving geographic data directly to the database. Based on the generated reports, the scale of risk resulting from the occurrence of a natural disaster is determined and preventive measures are then taken.
To be able to determine whether a function that counts the sources of possible disasters produces correct results, the testing method had to be changed. Previously, it was checked whether the function counts maps, but with possible errors, the process of functional and integration tests did not stop CI and it was always required to manually check the calculations. Thanks to the introduction of unit, functional and integration tests with assertions, it was checked whether the result is also correct with the expected value in the perspective of individual functional elements and the entire system.
Reducing the working time of state officials from half a year to half an hour when preparing reports from available statistical data thanks to the development and application of algorithms.
A 40% reduction in costs and acceleration of application performance thanks to the creation of a serverless application that performs calculations on maps in the Google CloudFunctions environment.
Increasing the quality, detail and speed of tests carried out by changing the testing method – unit, functional and integration tests with assertions. Also, by reducing costs on GitHub Actions, because after the change, it was not necessary to run four virtual machine units – just one was sufficient.