Spatio-Temporal Graph Neural Networks for Multi-Horizon River-Stage Forecasting in the Amazon: A Case Study on the Manaus–Santarém Corridor - Aguiar, Isabela A.; Rodrigues, Gabrielly F.; Vilhena, Raphael I. Winovski; Fontenelle, Antonio; Vital, Wington L.; Caliari, Italo P.; Bragança, Hendrio (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
Inland waterway transport in the Brazilian Amazon depends critically on river navigability, which is strongly governed by hydroclimatic variability and increasingly severe hydrological extremes. This paper proposes a multi-model framework for daily multi-horizon river-stage forecasting in the Manaus–Santarém corridor, combining official hydrometric observations from ANA with a spatio-temporal graph neural network (STGNN) that encodes upstream–downstream connectivity and travel-time-informed edge weights. The STGNN is evaluated against temporal benchmarks, recurrent baselines, a statistical model, and operational baselines across six forecast horizons (1–90 days) under a unified protocol with a chronological hold-out test set covering 2024. The STGNN achieves the best MAE, RMSE, and NSE at every horizon, maintaining NSE > 0.99 through 30 days, a threshold no competing model reaches, with RMSE gains of 29–35% over the strongest baseline at 14–30 days, the window governed by upstream signal propagation according to the physical travel times encoded in the graph. Results demonstrate that explicitly representing the river network as a directed graph provides consistent and physically interpretable predictive advantages over purely temporal architectures for multi-horizon Amazonian hydrological forecasting.