1. SISN
  2. All Volumes and Issues
  3. Volume 15, 2024
  4. Decoding Cesium-137: a Deep Learning Approach to Environmental Prediction

Decoding Cesium-137: a Deep Learning Approach to Environmental Prediction

SISN.
2024;
Volume 15
: pp. 200 - 209
https://doi.org/10.23939/sisn2024.15.200
Authors:
  1. Vasyl Lytvyn
  2. Oleksii Khobor
1
Lviv Polytechnic National University, Lviv, Ukraine
2
Lviv Polytechnic National University, Information Systems and Networks Department

The study delves into the significant environmental threat posed by cesium-137, a byproduct of nuclear mishaps, industrial activities, and past weapons tests. The persistence of cesium-137 disrupts ecosystems by contaminating soil and water, which subsequently affects human health through the food chain. Traditional monitoring techniques like gamma spectroscopy and soil sampling face challenges such as variability and the intensive use of resources.

The paper introduces deep learning, a branch of artificial intelligence, as a revolutionary method for environmental monitoring. By utilizing extensive datasets, deep learning predicts the spread of cesium-137, thus enhancing our understanding and management of its impact. The application of predictive models based on deep learning in various environmental domains demonstrates their potential for analyzing cesium-137 pollution.

cesium-137 pollution
deep learning
environmental monitoring
predictive modeling
ecosystem impact
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