Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning

Citations Over TimeTop 10% of 2015 papers

Abstract

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Related Papers

• → Dependence of remote sensing accuracy of global horizontal irradiance at different scales on satellite sampling frequency(2019)24 cited
• → Analysis of Geostationary Satellite Imagery Using a Temporal-Differencing Technique(2003)20 cited
• → Application of geostationary satellite data for determining solar radiations over Pakistan(1991)10 cited
• → GOES-R ABI on-orbit performance(2017)1 cited
• → Assessment of Ocean Color Products From the New Generation Himawari-8 AHI Geostationary Satellite and Its Application in the Calculation of the Photosynthetically Active Radiation(2024)