Autoencoder-Driven Weather Clustering for Source Estimation during Nuclear Events

TL;DR

This paper introduces a novel deep learning-based method for rapid source estimation during nuclear events by combining weather clustering with atmospheric dispersion modeling, improving emergency response accuracy.

Contribution

The paper presents a new approach integrating deep feature extraction and weather clustering for faster, more accurate source estimation in radiological emergencies.

Findings

High accuracy in source estimation over European weather data

Effective comparison with deep classification networks

Advantages in speed and interpretability of the proposed method

Abstract

Emergency response applications for nuclear or radiological events can be significantly improved via deep feature learning due to the hidden complexity of the data and models involved. In this paper we present a novel methodology for rapid source estimation during radiological releases based on deep feature extraction and weather clustering. Atmospheric dispersions are then calculated based on identified predominant weather patterns and are matched against simulated incidents indicated by radiation readings on the ground. We evaluate the accuracy of our methods over multiple years of weather reanalysis data in the European region. We juxtapose these results with deep classification convolution networks and discuss advantages and disadvantages.