https://fire-resilient-landscapes.github.io/tags/fire-prediction/Recent content in fire prediction on Fire Resilient LandscapesHugo -- gohugo.ioen&copy; 2021 <a href="authors/carojeda/"> Carolina G. Ojeda </a>Sun, 12 Dec 2021 00:00:00 +0000https://fire-resilient-landscapes.github.io/docs/01-is-possible-predict-fire/Sun, 12 Dec 2021 00:00:00 +0000https://fire-resilient-landscapes.github.io/docs/01-is-possible-predict-fire/Wildfires are naturally caused (e.g. by lightning bolts), and human-caused, e.g. with lit cigarette butts carelessly thrown away in areas where cities meet rural and industrial zones. The study about wildfires, as broad as it is, considers risk/hazard models, fire spread, control and post-disaster recovery. Wildfires are impossible to predict at the moment. However, these studies can identify fire-prone areas. To do so, they may use: historical data, satellite images, machine learning and drone monitoring.https://fire-resilient-landscapes.github.io/docs/05-machine-learning/Sun, 12 Dec 2021 00:00:00 +0000https://fire-resilient-landscapes.github.io/docs/05-machine-learning/Machine Learning (ML) is how computers learn based on a large amount of data. However, they will be able to predict fire-prone areas as long as they obtain more accurate data and there is a strong investment in this technology. Human-caused wildfires may be assessed by ML given the large amount of satellite and statistical information from the past. One of the most accurate ML is Random Forest. Wildfires caused by power transmission failures may also be assessed using the Hybrid Step XGBoost model which teaches the computers to create warnings in real time and avoid false alarms with a 98.