Deep Learning and Transformer Models for Groundwater Level Prediction in the Marvdasht Plain: Protecting UNESCO Heritage Sites—Persepolis and Naqsh-e Rustam

Deep Learning and Transformer Models for Groundwater Level Prediction in the Marvdasht Plain: Protecting UNESCO Heritage Sites—Persepolis and Naqsh-e Rustam

Groundwater level monitoring is crucial for assessing hydrological responses to climate change and human activities, which pose significant threats to the sustainability of semi-arid aquifers and the cultural heritage they sustain. This study presents an integrated remote sensing and transformer-bas...

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Bibliographic Details
Main Authors: Peyman Heidarian, Franz Pablo Antezana Lopez, Yumin Tan, Somayeh Fathtabar Firozjaee, Tahmouras Yousefi, Habib Salehi, Ava Osman Pour, Maria Elena Oscori Marca, Guanhua Zhou, Ali Azhdari, Reza Shahbazi
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
transformer models
neural networks
InSAR
deep learning
land subsidence
water resource management
Online Access:https://www.mdpi.com/2072-4292/17/14/2532
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