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 withadvanced machine learning optimization frameworks. This study presents an enhanced computational architecture combininghybrid Cellular Automata-Long Short-Term Memory networks (CA-LSTM) with gradient-boosted ensemble methods for predictingfloodplain Land Use/Land Cover (LULC) transformations across the Amazon River system's lower floodplain. We applied ourframework 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-weightedtemporal feature extraction from satellite imagery sequences; (2) spatially-distributed hydromorphological drivers encodedthrough graph neural networks; and (3) Bayesian optimization for automated hyperparameter tuning across heterogeneouslandscape classes. Our results demonstrate significant performance improvements over conventional cellular automataapproaches: out-of-sample prediction accuracy reaches 78.4% (vs. 67% for traditional CA models), with computational efficiencygains of 340% through algorithmic parallelization on GPU architectures. The model successfully predicts transitions in floodplainforest (declining 8.3% per decade), várzea agricultural expansion (increasing 6.1% per decade), and erosion-prone unvegetatedbars (accelerating 3.2% per decade) through 2042. Sensitivity analysis reveals that hydrological regime modification (damconstruction effects) explains 41% of landscape variance, while deforestation pressures account for 38%, with remaining varianceattributable to settlement expansion and climate-driven phenological shifts. Computational validation through spatiotemporalcross-validation and Monte Carlo uncertainty quantification demonstrates robust predictive capability across landscapeheterogeneity. This work establishes a generalizable computational framework applicable to diverse riverine systems globally,with implications for adaptive floodplain management under climate-hydrological uncertainty.