Abstract
Monitoring of volcanic ash cloud is conducive to the disaster prevention and mitigation and public safety. To tackle of large amount and various types of data and continuous changes of volcanic ash cloud monitoring, in this paper, a new long short term memory (LSTM) and cellular automaton (CA) (i.e., LSTM-CA) collaborative computing method for volcanic ash cloud diffusion is proposed via neural networks. Based on diffusion characteristics of volcanic ash cloud, a CA model of volcanic ash cloud in the three-dimensional spaces was first constructed. And then the constantly changing sequential characteristics of volcanic ash cloud was learned by LSTM neural network and further treated as the evolution rule of the CA diffusion model of volcanic ash cloud in three-dimensional space. Next, simulation experiments and analysis were conducted in terms of wind direction, wind speed, step size and the number of cell. Finally, the proposed LSTM-CA collaborative computing method was tested and verified in the actual Etna ash cloud diffusion case. The experimental results show that: (1) in the two-dimensional space, the proposed LSTM-CA method can obtain a good initial simulation effect of volcanic ash cloud diffusion, and the total accuracy of volcanic ash cloud identification reached 96.1%; (2) in the three-dimensional space, the proposed LSTM-CA method can exact simulate the horizontal and vertical diffusion trends of volcanic ash cloud; (3) the proposed LSTM-CA method can significantly reduce the modeling complexity of volcanic ash cloud and improve the calculation efficiency of spatiotemporal data. It seems to provide a new idea to identify and simulate the volcanic ash cloud in complex environments.
Highlights
Global volcanic eruption occurred almost synchronously with seismic activity, and concentrated on the major volcanic belts formed on the edge of the plate [1]
To further understand the sequential characteristics and the diffusion simulation in dynamic volcanic ash cloud monitoring, the current study investigates the simulation of volcanic ash cloud diffusion in three-dimensional space using the long short term memory (LSTM)-cellular automaton (CA) collaborative calculation method
DIFFUSION SIMULATION IN THREE-DIMENSIONAL SPACE In this part, the wind speed, wind direction and atmospheric turbulence were inputted into the proposed LSTM-CA collaborative computing method of volcanic ash cloud, and the simulation of volcanic ash cloud in three-dimensional space was performed in the Matlab software platform
Summary
Global volcanic eruption occurred almost synchronously with seismic activity, and concentrated on the major volcanic belts formed on the edge of the plate [1]. Collaborative computing refers to a way of computing that is relatively independent in terms of space and time series, and jointly completes a certain computing task in accordance with pre-established interconnection modes, interaction methods and technologies, and computing strategy [11]–[13] It has obvious advantages in monitoring of volcanic ash cloud using the dynamic changes of spatiotemporal data [14]–[16]. As a discrete system in time and space, cellular automaton (CA) can simulate complex systems that are constantly updated via evolution rule [17], [18] It has great potential in the dynamic monitoring contains identification and diffusion of discrete volcanic ash cloud in time and space. To further understand the sequential characteristics and the diffusion simulation in dynamic volcanic ash cloud monitoring, the current study investigates the simulation of volcanic ash cloud diffusion in three-dimensional space using the LSTM-CA collaborative calculation method. (4) Via the new cell status information, the output value of classifier is calculated in the output gate, and the final output value is gotten
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