367 2. MATERIALS AND METHODS 2.1. Study area and data overview The study was conducted at an operational TSF in South America, located in a humid, mild, highrainfall climate. The facility is impounded by a downstream embankment with a maximum height of 40 m and a crest length of 545 m. Further site identifiers and operational details are not reported due to confidentiality constraints under a non-disclosure agreement. 2.1.2 Rainfall and temperature Meteorological forcing was derived from a meteorological station, located approximately 1 km from the TSF, and aggregated to the 12-day Sentinel-1 acquisition intervals used in the deformation analysis. Table 1 shows that over February 2020 to July 2025, 12-day accumulated rainfall averaged 104.73 mm (IQR: 70.91-130.53 mm), with a maximum of 267.13 mm. Mean air temperature averaged 18.27 °C while mean maximum and minimum temperatures were 23.28 °C and 15.56 °C, respectively. The meteorological series indicates marked rainfall variability through time, with wetter intervals interspersed with comparatively dry periods, whereas temperature varied within a narrower range. Table 1 – Summary of meteorological conditions used in the analysis Variable Mean Median IQR Minimum Maximum Rainfall accumulation (mm) (12-day) 104.73 100.03 70.91-130.53 10.24 267.13 Mean air temperature (°C) 18.27 18.25 17.75-18.75 16.24 20.47 Maximum air temperature (°C) 23.28 23.17 22.41-24.11 20.74 27.68 Minimum air temperature (°C) 15.56 15.66 14.98-16.23 12.9 17.48 2.2 MT-DInSAR time-series Deformation monitoring relied on Sentinel-1A C-band SAR Interferometric Wide (IW) swath single look complex (SLC) data, enabling frequent, all-weather observations. A time series from January 2020 to July 2025 were processed with the SBAS approach implemented in the open-source OpenSARLab framework, producing pixel-wise LOS displacement time series and temporal coherence. For the vertical deformation analysis, pixels with temporal coherence ≥ 0.6 were retained to ensure measurement reliability. Vertical deformation (dU) was estimated by decomposing ascending and descending observations using pixel-wise acquisition geometry. Time-series uncertainty was quantified via coherence-based uncertainty propagation, yielding 8,J. 2.3 Prediction framework and model structure To forecast vertical deformation, we implemented a data-driven prediction framework based on gradient boosting. LightGBM was selected as the regressor because it is computationally efficient and performs well on structured data, while capturing nonlinear relationships and feature interactions relevant to the vertical deformation increment Δ4á>?Δ>. Separate LightGBM models were trained for each zone (crest, toe, west, and east) to reflect distinct deformation regimes across the TSF. This zone-specific strategy avoids averaging behaviour across areas with different kinematics and is consistent with common practice in deformation forecasting, where nearby locations can respond differently to the same forcing. Each model performs one-step-ahead prediction aligned with the next Sentinel-1 acquisition. Formally, the model learns à(#>)→Δ4á>?Δ>, where #> is the feature vector at time M and Δ4á>?Δ> is the predicted deformation increment at the subsequent observation. One-step forecasts can be iterated to extend predictions over longer horizons when required.
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