1. [PDF] Machine Learning Regression Techniques to Predict Burned Area ...
Abstract: The study presents the implementation of machine learning regression techniques to predict burned areas of forest fires.
2. [PDF] AN APPLICATION OF REGRESSION MODELS FOR ANALYSIS AND ...
By creating a model that uses meteorological measurements such as wind and relative humidity, it should possible to prepare better in order to fight wildfires ...
3. [PDF] Predicting Forest Fire with Linear Regression and Random Forest
The first one is Mean Absolute. Error(MAE), it's the mean value of the difference between every single observed value and the arithmetic mean. It can be used ...
4. Regression, Fire, and Dangerous Things (3/3) - Elevanth.org
29 jun 2021 · The story of a monk who is transformed into a fox, because he denies that an enlightened person is subject to cause and effect.
Hyakujō's Fox is a classic Zen kōan attested from the year 1036 CE. It is surely much older. It gives the story of a monk who is transformed into a fox, because he denies that an enlightened person is subject to cause and effect.
5. [2308.08936] Estimating fire Duration using regression methods - arXiv
Bevat niet: Absolute | Resultaten tonen met:Absolute
Wildfire forecasting problems usually rely on complex grid-based mathematical models, mostly involving Computational fluid dynamics(CFD) and Celluar Automata, but these methods have always been computationally expensive and difficult to deliver a fast decision pattern. In this paper, we provide machine learning based approaches that solve the problem of high computational effort and time consumption. This paper predicts the burning duration of a known wildfire by RF(random forest), KNN, and XGBoost regression models and also image-based, like CNN and Encoder. Model inputs are based on the map of landscape features provided by satellites and the corresponding historical fire data in this area. This model is trained by happened fire data and landform feature maps and tested with the most recent real value in the same area. By processing the input differently to obtain the optimal outcome, the system is able to make fast and relatively accurate future predictions based on landscape images of known fires.
6. Analysis of Wildfire Danger Level Using Logistic Regression Model in ...
29 nov 2023 · When its absolute value is greater, it suggests a stronger correlation between two factors. RF is an integrated learning method with a ...
Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values hi...
7. Forest Fire Prediction with the help of multiple regression models
13 okt 2019 · I will be predicting the scale of a woodland fireplace primarily based on capabilities which include geospatial information, wind, temperature, and humidity.
regression will predict a continuous quantity and provide a value
8. [PDF] Model Comparisons for Predicting Grassland Fire Occurrence ...
12 apr 2022 · Here we selected 16 environmental variables that may have impacts on fire occurrence, then built regression models of grassland fire probability ...
9. [PDF] Multiple regression analysis of fire deaths from burns
and would allow the regression less Ireedom to accommodate to the data. There are no such absolute effects of age. Old people evidently survive a shorter ...
10. Modeling Fire Occurrence at the City Scale: A Comparison between ...
8 apr 2017 · ... absolute correlation value is. Using global linear regression as the training model, estimates of the prediction error and variable ...
An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically ...
11. [PDF] Spatial Prediction of Fire Ignition Probabilities: Comparing Logistic ...
A complete fire danger estimation system needs to address the likelihood of ... multivariate model is obtained using stepwise regression on. 1. 74.
12. [PDF] A Weighty Issue: Estimation of Fire Size with Geographically Weighted ...
Logistic regression is done with binomial data: fire/no fire. Geographically ... AIC is not an absolute quality measure, i.e. a null hypothesis cannot be tested.
13. Identifying Influential Spatial Drivers of Forest Fires through ... - MDPI
This model was used because the values of mean bias error, mean absolute error, and global standard deviation for these datasets were, respectively, 0.35, 8.36, ...
Identifying the underlying factors derived from geospatial and remote sensing data that contribute to forest fires is of paramount importance. It aids experts in pinpointing areas and periods most susceptible to these incidents. In this study, we employ the geographically and temporally weighted regression (GTWR) method in conjunction with a refined continuous invasive weed optimization (CIWO) algorithm to assess certain spatially relevant drivers of forest fires, encompassing both biophysical and anthropogenic influences. Our proposed approach demonstrates theoretical utility in addressing the spatial regression problem by meticulously accounting for the autocorrelation and non-stationarity inherent in spatial data. We leverage tricube and Gaussian kernels to weight the GTWR for two distinct temporal datasets, yielding coefficients of determination (R2) amounting to 0.99 and 0.97, respectively. In contrast, traditional geographically weighted regression (GWR) using the tricube kernel achieved R2 values of 0.87 and 0.88, while the Gaussian kernel yielded R2 values of 0.8138 and 0.82 for the same datasets. This investigation underscores the substantial impact of both biophysical and anthropogenic factors on forest fires within the study areas.
14. [PDF] spatial logistic regression models for predicting peatland fire in
Keywords: Fire-prediction model, fire management, spatial logistic-regression method, Sumatra ... their absolute values were displayed in descending sequence, ...
15. absolute fire - Downlink - SoundCloud
14 feb 2018 · Listen to ABSOLUTE FIRE by Downlink playlist on desktop and mobile.
I've been opening my sets with this track for a while now. It starts with some huge bass stabs and drops into a simplistic "bash your head" bassline that gets those live crowds moving. I added another
16. Abstract - CSIRO PUBLISHING | International Journal of Wildland Fire
Evaluating regression model estimates of canopy fuel stratum characteristics in four crown fire-prone fuel types in western North America. Miguel G. Cruz A C ...
Two evaluations were undertaken of the regression equations developed by M. Cruz, M. Alexander and R. Wakimoto (2003, International Journal of Wildland Fire 12, 39–50) for estimating canopy fuel stratum characteristics from stand structure variables for four broad coniferous forest fuel types found in western North America. The first evaluation involved a random selection of 10 stands each from the four datasets used in the original study. These were in turn subjected to two simulated thinning regimes (i.e. 25 and 50% basal area removal). The second evaluation involved a completely independent dataset for ponderosa pine consisting of 16 stands sampled by T. Keyser and F. Smith (2010, Forest Science 56, 156–165). Evaluation statistics were comparable for the thinning scenarios and independent evaluations. Mean absolute percentage errors varied between 13.8 and 41.3% for canopy base height, 5.3 and 67.9% for canopy fuel load, and 20.7 and 71% for canopy bulk density. Bias errors were negligible. The results of both evaluations clearly show that the stand-level models of Cruz et al. (2003) used for estimating canopy base height, canopy fuel load and canopy bulk density in the assessment of crown fire potential are, considering their simplicity, quite robust.
17. Modeling spatial patterns of fire occurrence in Mediterranean ...
Source. Forest Ecology and Management > 2012 > 275 > Complete > 117-129 ... Keywords. Fire occurrence Random Forest Multiple Linear Regression Mediterranean ...
► We model spatial patterns of fire occurrence in Mediterranean Europe with two methods. ► Random Forest method showed a better performance than Multiple Linear Regression. ► NW Iberian Peninsula and South Italy have higher likelihood of fire occurrence. ► Precipitation is the most important variable with both methods. ► Local roads density, livestock density and unemployment rate are also significant.
18. Evaluating regression model estimates of canopy fuel stratum ...
29 aug 2024 · Fire and Fire Surrogates Study · Fire Operations Maps · FIREMON · FIRESEV ... Mean absolute percentage errors varied between 13.8 and 41.3 ...
Two evaluations were undertaken of the regression equations developed by M. Cruz, M. Alexander and R. Wakimoto (2003, International Journal of Wildland Fire 12, 39-50) for estimating canopy fuel stratum characteristics from stand structure variables for four broad coniferous forest fuel types found in western North America. The first evaluation involved a random selection of 10 stands each from the four datasets used in the original study. These were in turn subjected to two simulated thinning regimes (i.e. 25 and 50% basal area removal).
19. Near-complete loss of fire-resistant primary tropical forest cover in ...
18 dec 2020 · We find that fires did not penetrate undisturbed primary forest areas deeper than two kilometres from the forest edge irrespective of drought conditions.
Deforestation in Indonesia in recent decades has made increasingly large parts of the region vulnerable to fires. Burning is particularly widespread in deforested peatlands, and it leads to globally significant carbon emissions. Here we use satellite-based observations to assess loss and fragmentation of primary forests and associated changes in fire regimes in Sumatra and Kalimantan between 2001 and 2019. We find that fires did not penetrate undisturbed primary forest areas deeper than two kilometres from the forest edge irrespective of drought conditions. However, fire-resistant forest now covers only 3% of peatlands and 4.5% of non-peatlands; the majority of the remaining primary forests are severely fragmented or degraded due to proximity to the forest edge. We conclude that protection and regeneration of the remaining blocks of contiguous primary forest, as well as peatland restoration, are urgently needed to mitigate the impacts of potentially more frequent fire events under future global warming. Loss and fragmentation of contiguous tracts of primary forest enhances the susceptibility of tropical peatland and forest to fires triggered by frequent droughts, according to satellite-based remote sensing of Sumatra and Kalimantan from the past two decades
