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Open Access

MPformer: A Transformer-Based Model for Earthen Ruins Climate Prediction

School of Information Technology, Northwest University, Xi’an 710127, China
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Abstract

Earthen ruins contain rich historical value. Affected by wind speed, temperature, and other factors, their survival conditions are not optimistic. Time series prediction provides more information for ruins protection. This work includes two challenges: (1) The ruin is located in an open environment, causing complex nonlinear temporal patterns. Furthermore, the usual wind speed monitoring requires the 10 meters observation height to reduce the influence of terrain. However, in order to monitor wind speed around the ruin, we have to set 4.5 meters observation height according to the ruin, resulting in a non-periodic and oscillating temporal pattern of wind speed; (2) The ruin is located in the arid and uninhabited region of northwest China, which results in accelerating aging of equipment and difficulty in maintenance. It significantly amplifies the device error rate, leading to duplication, missing, and outliers in datasets. To address these challenges, we designed a complete preprocessing and a Transformer-based multi-channel patch model. Experimental results on four datasets that we collected show that our model outperforms the others. Ruins climate prediction model can timely and effectively predict the abnormal state of the environment of the ruins. This provides effective data support and decision-making for ruins conservation, and exploring the relationship between the environmental conditions and the living state of the earthen ruins.

Keywords

earthen ruins protectiontime series predictionTransformer

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Tsinghua Science and Technology
Volume 29 Issue 6,
December 2024
Pages 1829-1838
DOI: 10.26599/TST.2024.9010035
Cite this article:
Xu G, Wang H, Ji S, et al. MPformer: A Transformer-Based Model for Earthen Ruins Climate Prediction. Tsinghua Science and Technology, 2024, 29(6): 1829-1838. https://doi.org/10.26599/TST.2024.9010035

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Received: 13 September 2023
Revised: 07 December 2023
Accepted: 06 February 2024
Published: 03 May 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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