Research on the Construction of Desertification Risk Warning System Based on Deep Learning

DOI

10.2991/978-94-6463-708-3_39How to use a DOI?

Keywords

Deep learning; Desertification risk; Data fusion

Abstract

To improve the accuracy and timeliness of desertification risk early warning, a multi-source data fusion method based on deep learning is used to analyze the impact of meteorological data, soil moisture, and remote sensing imagery on desertification prediction. The experimental results show that the deep learning model achieves a prediction accuracy of 95.2% with the fusion of meteorological, soil moisture, and remote sensing data, significantly higher than the 84.7% accuracy of traditional linear regression models. This approach not only enhances the prediction accuracy but also improves the system’s real-time responsiveness, demonstrating a broad potential for application in desertification monitoring.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wei Feng
PY  - 2025
DA  - 2025/05/09
TI  - Research on the Construction of Desertification Risk Warning System Based on Deep Learning
BT  - Proceedings of the 2024 10th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE 2024)
PB  - Atlantis Press
SP  - 353
EP  - 364
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-708-3_39
DO  - 10.2991/978-94-6463-708-3_39
ID  - Feng2025
ER  -