Predicting the occurrence of wildfires with binary structured additive regression models-Reference-Cited by-同舟云学术

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Predicting the occurrence of wildfires with binary structured additive regression models

Published:2017-02 Issue: Volume:187 Page:154-165
ISSN:0301-4797
Container-title:Journal of Environmental Management
language:en
Short-container-title:Journal of Environmental Management

Author:

Ríos-Pena Laura,Kneib Thomas,Cadarso-Suárez Carmen,Marey-Pérez Manuel

Funder

Spanish Ministry of Innovation & Competitiveness

Galician Regional Authority

Competitive Research Reference Group

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Waste Management and Disposal,General Medicine,Environmental Engineering

Reference117 articles.

1. A methodology for investigating trends in changes in the timing of the fire season with applications to lightning-caused forest fires in alberta and Ontario, Canada;Albert-Green;Can. J. For. Res.,2013

2. Landscape change and ecosystem classification in a municipal district of a small city (Isernia, Central Italy);Acosta;Environ. Monit. Assess.,2005

3. Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment;Aguado;Int. J. Wildland Fire,2007

4. Mapping lightning/human-caused wildfire occurrence under ignition point location uncertainty;Amatulli;Ecol. Model.,2007

5. Modelling of landscape changes derived from the dynamics of socio-ecological systems: a case of study in a semiarid Mediterranean landscape;Aranzabal;Ecol. Indic.,2008

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1. Natural or man-made? Finding the drivers of forest fires within the protected area habitats: a case study from the lower Shivalik Landscape, Western Himalaya;Natural Hazards;2024-03-20

2. Effects of Adding Native Annual Seeds to South Korea Native Perennial Seed Mixture on Early Stage Vegetation Recovery, Soil Enzymes, and Nutrient Dynamics in Post-Fire Soils;Forests;2023-11-21

3. PLS-PM analysis of forest fires using remote sensing tools. The case of Xurés in the Transboundary Biosphere Reserve;Ecological Informatics;2023-07

4. Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility;Geomatics, Natural Hazards and Risk;2023-05-09

5. Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain;Journal of Environmental Management;2023-02

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