Developing novel machine-learning-based fire weather indices

Abstract

Accurate wildfire risk estimation is an essential yet challenging task. As the frequency of extreme fire weather and wildfires is on the rise, forest managers and firefighters require accurate wildfire risk estimations to successfully implement forest management and firefighting strategies. Wildfire risk depends on non-linear interactions between multiple factors; therefore, the performance of linear models in its estimation is limited. To date, several traditional fire weather indices (FWIs) have been commonly used by weather services, such as the Canadian FWI. Traditional FWIs are primarily based on empirical and statistical analyses. In this paper, we propose a novel FWI that was developed using machine learning (ML) – the MLFWI. We present the performance of the MLFWI and compare it withvarious traditional FWIs. We find that the MLFWI significantly outperforms traditional indices in predicting wildfire occurrence, achieving an AUC score of 0.99 compared to 0.62-0.80. We recommend applying the MLFWI in wildfire warning systems.

Forest fire vulnerability in Nepal's Chure region: Investigating the influencing factors using generalized linear model

“…In our study, the area under flooded vegetation, representing forests area or swamp lands seasonally submerged under water [ 98 ], was considered an insignificant factor. This shows that humidity along with vegetation type plays a large role in influencing forest fire incidents in line to the findings of Shmuel & Heifetz [ 99 ] which reported areas with low relative humidity along with high NDVI values poses high risk of forest fire.…”

“…Our research depicts that population density has a significant negative relationship with forest fire as shown in the result. Higher population density reduces the probability of forest fire to a large extent, in accord with several studies [ 99 , 100 ]. Generally, there is a non-chromatic relationship between the population density and the forest fire.…”

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

“…In recent years, regression machine learning algorithms have experienced rapid growth due to their demonstrated high accuracy across a range of applications. For instance, regression machine learning has been used to predict stock prices [ 12 ], diagnose diseases [ 13 ], forecast weather patterns [ 14 , 15 ], and assess molecular similarity [ 16 ]. In addition, regression machine learning has been used to optimise processes and improve decision making in industries such as manufacturing and transportation [ 17 , 18 , 19 , 20 ].…”

A Machine-Learning Approach to Predicting Daily Wildfire Expansion Rate

“…One of the central advantages of ML models is their relatively high performance when predicting non-linear phenomena. As wildfire behavior is affected by non-linear interactions between various factors, scholars have recognized the potential contribution of ML models to predictions in the field [8] and have demonstrated the high performance of ML models in wildfire prediction both regionally (e.g., [9,10]) and globally (e.g., [11,12]). Fortunately, in recent years, data required for wildfire prediction have become available not only in specific regions but also globally.…”