ArticleScientific reports2026
Simulation of long-term spatio-temporal environmental dynamics using a unified benchmark of neighbor augmenting, LSTM and graph attention models.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Rapid environmental change has increased the need for predicting the long-term geospatial reliably. However, accurately modeling spatio-temporal geospatial dynamics remains challenging Because of the nonlinearities, complex spatial dependency, and external driving factors, it is difficult to predict. In this paper, a comprehensive benchmarking framework is proposed for the comparison of neighborhood-based, graph-based and attention-based spatiotemporal deep learning models, with the same preprocessing, training and testing procedure.. Long Short-Term Memory (LSTM) models with and without auxiliary variables are compared with hybrid Graph Attention Network-LSTM (GAT-LSTM) models and fully attention-based GAT-Temporal Attention models, with and without a feed-forward (MLP/FFN) block. All models are trained using a unified preprocessing and evaluation pipeline on annual satellite data in Network Common Data Form (NetCDF) from 2000 to 2023, with 2024 reserved as a fully unseen test dataset. Global pixel-wise measures such as R
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