Computational Modelling Framework for Riverine Landscape Dynamics Advanced Machine Learning Approaches to Predict Floodplain Transformations in Multi-Scale Hydromorphological Systems

Computational prediction of riverine landscape dynamics requires integration of multi-scale hydromorphological data with advanced machine learning optimization frameworks. This study presents an enhanced computational architecture combining hybrid Cellular Automata-Long Short-Term Memory networks (CA-LSTM) with gradient-boosted ensemble methods for predicting floodplain Land Use/Land Cover (LULC) transformations across the Amazon River system's lower floodplain. We applied our framework to a 450-kilometer riparian corridor spanning the Solimões-Amazon confluence region across three decades (1992-2022), utilizing Landsat and Sentinel-2 multispectral time series. The computational model integrates: (1) attention-weighted temporal feature extraction from satellite imagery sequences; (2) spatially-distributed hydromorphological drivers encoded through graph neural networks; and (3) Bayesian optimization for automated hyperparameter tuning across heterogeneous landscape classes. Our results demonstrate significant performance improvements over conventional cellular automata approaches: out-of-sample prediction accuracy reaches 78.4% (vs. 67% for traditional CA models), with computational efficiency gains of 340% through algorithmic parallelization on GPU architectures. The model successfully predicts transitions in floodplain forest (declining 8.3% per decade), várzea agricultural expansion (increasing 6.1% per decade), and erosion-prone unvegetated bars (accelerating 3.2% per decade) through 2042. Sensitivity analysis reveals that hydrological regime modification (dam construction effects) explains 41% of landscape variance, while deforestation pressures account for 38%, with remaining variance attributable to settlement expansion and climate-driven phenological shifts. Computational validation through spatiotemporal cross-validation and Monte Carlo uncertainty quantification demonstrates robust predictive capability across landscape heterogeneity. This work establishes a generalizable computational framework applicable to diverse riverine systems globally, with implications for adaptive floodplain management under climate-hydrological uncertainty.