Research Article
Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing
@INPROCEEDINGS{10.1007/978-3-030-67537-0_17, author={Lan Liu and Cheng-fan Li and Xian-kun Sun and Jiangang Shi}, title={Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Collaborative computing Remote sensing Volcanic ash cloud Identification Long Short-Term Memory (LSTM)}, doi={10.1007/978-3-030-67537-0_17} }- Lan Liu
Cheng-fan Li
Xian-kun Sun
Jiangang Shi
Year: 2021
Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM Collaborative Computing
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_17
Abstract
Collaborative computing performs quickly and accurately the task via combining the multimedia, multi-methods, and multi-clients. Analyzing of traditional feedforward neural network (FNN), long short-term memory (LSTM) neural networks and remote sensing data, this paper proposes a new identification method of sequential feature based on FNN-LSTM collaborative calculation in the volcanic ash cloud monitoring. In this method, combining remote sensing data, the FNN network is used firstly to construct the identification model of volcanic ash cloud. Next, the LSTM network is used to identify the sequential feature of dynamic changes in volcanic ash cloud based on the text data of the volcanic ash report. And then the simulation and true volcanic ash cloud case is performed and analyzed. The experimental results show that: 1) the proposed method is high in training accuracy with 76.54% and testing accuracy with 77%, respectively, and has obvious advantages for small-scale data volumes; 2) the total accuracy and RMS of the simulation analysis reached 79.05% and 0.0149, respectively, it verified the feasibility and effectiveness in the prediction of spatiotemporal evolution; 3) the anti-noise property and the image segmentation effect is good, the obtained sequential feature of the volcanic ash cloud are closer to the actual diffusion. It can provide a reference for sequential feature extraction and dynamic monitoring of volcanic ash cloud in complex environments.
