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Article

A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation

School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
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Academic Editors: Alfonso Fernández-Manso and Carmen Quintano
Received: 24 May 2021 / Revised: 12 July 2021 / Accepted: 14 July 2021 / Published: 16 July 2021
Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to build relationships between the input drivers (weather, fuel and site characteristics) and observed DFMC. The latter attempts to simulate the processes that occur in the fuel with energy and water balance conservation equations. However, empirical models lack explanations for physical processes, and process-based models may provide an incomplete representation of DFMC. To combine the benefits of empirical and process-based models, here we introduced the Long Short-Term Memory (LSTM) network and its combination with an effective physics process-based model fuel stick moisture model (FSMM) to estimate DFMC. The LSTM network showed its powerful ability in describing the temporal dynamic changes of DFMC with high R2 (0.91), low RMSE (3.24%) and MAE (1.97%). When combined with a FSMM model, the physics-guided model FSMM-LSTM showed betterperformance (R2 = 0.96, RMSE = 2.21% and MAE = 1.41%) compared with the other models. Therefore, the combination of the physics process and deep learning estimated 10-h DFMC more accurately, allowing the improvement of wildfire risk assessments and fire simulating. View Full-Text
Keywords: dead fuel moisture content (DFMC); deep learning; FSMM-LSTM; LSTM; wildfires dead fuel moisture content (DFMC); deep learning; FSMM-LSTM; LSTM; wildfires
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MDPI and ACS Style

Fan, C.; He, B. A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation. Forests 2021, 12, 933. https://0-doi-org.brum.beds.ac.uk/10.3390/f12070933

AMA Style

Fan C, He B. A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation. Forests. 2021; 12(7):933. https://0-doi-org.brum.beds.ac.uk/10.3390/f12070933

Chicago/Turabian Style

Fan, Chunquan, and Binbin He. 2021. "A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation" Forests 12, no. 7: 933. https://0-doi-org.brum.beds.ac.uk/10.3390/f12070933

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