Predictive Archaeological Risk Assessment at Reservoirs with Multitemporal LiDAR and Machine Learning (XGBoost): The Case of Valdecañas Reservoir (Spain)

Abstract

The conservation and monitoring of archaeological sites submerged in water reservoirs have become increasingly necessary in a climatic context where water management policies are possibly accelerating erosion and sedimentation processes. This study assesses the potential of using multitemporal LiDAR data and Machine Learning (ML)—specifically the XGBoost algorithm—to predict erosional and sedimentary processes affecting archaeological sites in the Valdecañas Reservoir (Spain). Using data from 2010 to 2023, topographic variations were calculated through a robust workflow that included the co-registration of LiDAR point clouds and the generation of high-resolution DEMs. Hydrological variables, topographic descriptors, and water dynamics-related factors were extracted and used to train models based on the detected measurement errors and the temporal ranges of the DEMs. The model trained with 2018–2023 data exhibited the highest predictive performance (R2 = 0.685), suggesting that sedimentary and erosional patterns are partially predictable. Finally, a multicriteria approach was applied using a DEM generated from 1957 aerial photographs to estimate past variations based on historical terrain conditions. The results indicate that areas exposed to fluctuating water levels and different topographic orientations suffer greater damage. This study highlights the value of LiDAR and ML in assessing the vulnerability of archaeological sites in highly dynamic environments.

Keywords

• LiDAR
• archaeological heritage
• climate change
• machine learning
• predictive modelling
• risk assessment