Volcanic Ash Cloud Diffusion from Remote Sensing Image Using LSTM-CA Method

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.