Paper Info
Title
Urban Fire Situation Forecasting: Deep Sequence Learning With Spatio-Temporal Dynamics
Abstract
Understanding the evolving discipline of urban fire situations is a basic but challenging task for urban security and fire-fighting decisions. Traditional methods forecast the urban fire situation through mathematical modeling and statistical learning, which could be interpretable but generally lack of efficiency and practicality. Recently, some deep neural network methodologies, especially convolutional neural network (CNN) and recurrent neural network (RNN), are presented as paradigms to capture dynamics in spatial-temporal complex phenomenon, which tally with the characteristics of fire situation forecasting. In this paper, we propose a novel deep sequence learning model as the fire situation forecasting network (FSFN) to better process the information and spatio-temporal correlations in regional urban fire alarm dataset. FSFN model integrates structures of Variational auto-encoders and context-based sequence generative model Seq2seq to obtain the latent representation of the fire situation and learn the spatio-temporal dynamics. Furthermore, we augment the network structure of FSFN from a simple deep sequence generative model to adversarial fire situation forecasting network with auxiliary information(Adversarial FSFN-A). The experimental studies demonstrate the effectiveness of Adversarial FSFN-A has superior spatio-temporal distribution prediction of multi-type urban fire situation. (C) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.asoc.2020.106730
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Fire Situation Forecasting, Information fusion, Variational Auto-Encoder, Sequence generation, Spatio-temporal analysis
Journal
97
Issue
ISSN
Citations 
Part
1568-4946
1
PageRank 
References 
Authors
0.36
25
6
Name
Order
Citations
PageRank
Guangyin Jin163.19
Qi Wang2111.67
Cunchao Zhu311.04
Yang-He Feng4239.91
Jincai Huang55416.88
Xingchen Hu694.52