Deep Learning for Monthly Precipitation Prediction in Mountainous Terrain: From Individual Architectures to EnsembleStrategies

We evaluate hybrid deep learning architectures and ensemble strategies for monthly precipitation prediction over Boyacá, Colombia (3,965 CHIRPS grid cells, 145–5,490 m elevation, horizons 1–12 months). Three spatial encoding paradigms are compared: convolutional (ConvLSTM), spectral (Fourier Neural Operator hybrids), and graph-based (Graph Neural Network with Temporal Attention, GNN-TAT). GNN-TAT matches ConvLSTM accuracy (R²: 0.628 vs 0.642) with 95% fewer parameters and lower variance, leveraging elevation-weighted edges for interpretable spatial reasoning. Beyond individual models, late fusion via Ridge regression (R²=0.668) improves over all single architectures by exploiting complementary grid-based and graph-based error structures. Conversely, early fusion stacking collapses to R²=0.212, showing that combining predictions preserves inductive biases while merging intermediate representations destroys them. We also report the first evaluation of State Space Models (Mamba) for regional precipitation, which fail to transfer from sequence modeling (R²=0.200). Three operational guidelines emerge: graph-based encoders are efficient alternatives in complex terrain, ensemble gains depend on late-stage combination, and documenting architectural failures narrows the search space for future practitioners. All experiments use standardized CHIRPS/SRTM inputs and fixed random seeds for reproducibility.

Keywords: monthly precipitation prediction, deep learning, hybrid architectures, Graph Neural Networks, ConvLSTM, ensemble learning, mountainous terrain, Colombian Andes, State Space Models

Related Publications:
1. Pérez Reyes, M.R.; Suárez Barón, M.J.; García Cabrejo, Ó.J. Spatiotemporal Prediction of Monthly Precipitation: A Systematic Review of Hybrid Models. Hydrology Research (IWA Publishing), under review — Revision 3.

2. Pérez Reyes, M.R.; Suárez Barón, M.J.; García Cabrejo, Ó.J. Hybrid Deep Learning Architectures for Multi-Horizon Precipitation Forecasting in Mountainous Regions: Systematic Comparison of Component-Combination Models in the Colombian Andes. Hydrology (MDPI), accepted.

3. Pérez Reyes, M.R.; Suárez Barón, M.J.; García Cabrejo, Ó.J. A Data-Driven Deep Learning Framework for Monthly Precipitation Prediction in Complex Mountainous Terrain: Systematic Evaluation of Hybrid Architectures, Ensemble Strategies, and Emerging Paradigms. Hydrology (MDPI), ready for submission.