Prediction of Volcanic Fallout Spread Using Deep Learning Techniques

• Published in: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) pp. 987 - 991
• Main Authors: Mannem, Siva Naga Prasad, S, Janardhana Rao, Atluri, Yaswitha Sai, Prasad, Chitturi, Chindu, Hema
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02-09-2021
• Subjects: Ash; Autonomous investigation; Autonomous specialist; Computational modeling; DCNN; Deep learning; Deep support learning; operate cell robot; Predictive models; Robots; Training; Volcanoes
• DOI: 10.1109/ICIRCA51532.2021.9545078

The volcanic fallout ash is one of the main reasons which make the volcanic eruption a deadly natural disaster. The prediction of the unfold of volcanic fallout can be made. However, the calculation is complex and requires massive computing sources to make an accurate prediction. Previously, researchers figured out several ways to predict it faster, but it nevertheless required running the simulation. Therefore, this research work endorse the use of deep getting to know to make a quickly prediction of the ash spread. Specifically, the cGAN-based structure is used to predict ash unfold patterns from a range of input information inclusive of weather maps and eruption scale parameters. At the cease of the project, it is highly intended to to present a paper at a conference as well as open-source the skilled model so that the whole society can be well-prepared for the deadly catastrophe with the aid of getting accurate predictions faster.