scholarly journals Modelling chamise fuel moisture content across California: a machine learning approach

2021 ◽  
Author(s):  
Scott B. Capps ◽  
Wei Zhuang ◽  
Rui Liu ◽  
Tom Rolinski ◽  
Xin Qu
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Learning Approach ◽  
Fuel Moisture ◽  
Machine Learning Approach ◽  
Fuel Moisture Content

Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data

2021 ◽  
Author(s):  
Chunquan Fan ◽  
Binbin He ◽  
Peng Kong ◽  
Hao Xu ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Fuel Moisture ◽  
Fuel Moisture Content

scholarly journals Estimation of 10-Hour Fuel Moisture Content Using Meteorological Data: A Model Inter-Comparison Study

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 982
Author(s):  
HoonTaek Lee ◽  
Myoungsoo Won ◽  
Sukhee Yoon ◽  
Keunchang Jang
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Fire Modeling ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Correction Coefficient ◽  
Fuel Moisture ◽  
Modeling Studies ◽  
Study Sites ◽  
Fuel Moisture Content

Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-h FMC models based on three different approaches, including regression (MREG), machine learning algorithms (MML) with random forest and support vector machine, and a process-based model (MFSMM), were compared. In addition, whole-year models of each type were compared with their respective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-h FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-h FMC models. In the assessments, MML showed the best performance (R2 = 0.77–0.82 and root mean squared error [RMSE] = 2.05–2.84%). The introduction of the correction coefficient into MREG improved its estimates (R2 improved from 0.56–0.58 to 0.68–0.70 and RMSE improved from 3.13–3.85% to 2.64–3.27%) by reducing the errors associated with high 10-h FMC values. MFSMM showed the worst performance (R2 = 0.41–0.43 and RMSE = 3.70–4.39%), which could possibly be attributed to the lack of radiation input from the study sites as well as the particular fuel moisture stick sensor that was used. Whole-year models and seasonal models showed almost equal performance because 10-h FMC varied in response to atmospheric moisture conditions rather than specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine-learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-h FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those involving fire danger ratings.

Live fuel moisture content estimation from MODIS: A deep learning approach

2021 ◽  
Vol 179 ◽  
pp. 81-91
Author(s):  
Liujun Zhu ◽  
Geoffrey I. Webb ◽  
Marta Yebra ◽  
Gianluca Scortechini ◽  
Lynn Miller ◽  
...  
Keyword(s):  
Deep Learning ◽  
Moisture Content ◽  
Learning Approach ◽  
Fuel Moisture ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content
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