Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

Suitable forecasting of streamflow plays a fundamental role in the sustainable management of water resources. This is especially important in regions with high hydroclimatic variability, such as Colombia. Reliable predictions of water availability are essential to support decision-making on water allocation, risk mitigation, and the long-term resilience of supply systems, including urban aqueduct services. However, traditional deterministic models often require extensive parameterization. They may lack flexibility. Data-driven approaches such as Long Short-Term Memory networks typically fail to provide meaningful uncertainty quantification. In this work, we introduce the Chained Correlated Gaussian Process. This is a scalable probabilistic framework for jointly forecasting multiple hydrological time series. The proposed model represents likelihood parameters through latent Gaussian Processes. This enables the use of non-Gaussian likelihoods to enforce physical constraints on the outputs. In particular, a Gamma likelihood is integrated into a Linear Model of Coregionalization. This approach captures interdependencies among multiple time series while ensuring non-negativity. To achieve computational scalability, we employ a variational inference framework with shared inducing points. This reduces computational complexity from cubic to linear with respect to the number of observations. Additionally, a hybrid optimization strategy is adopted. It combines Adam and Natural Gradient methods to improve numerical stability and avoid poor local optima. The proposed methodology is validated using twelve years of daily observations from 23 reservoirs in Colombia. These reservoirs serve as proxies for water availability dynamics within interconnected resource systems. Experimental results demonstrate that the ChdGP framework consistently outperforms baseline models across multiple forecasting horizons. The Gamma-likelihood configuration shows superior performance. It provides physically consistent predictions, improves uncertainty quantification, and effectively captures asymmetric behaviors and extreme hydrological events. Performance evaluation based on the Negative Log Predictive Density metric confirms the robustness of the proposed approach under non-Gaussian conditions. The results highlight the potential of the proposed framework as a reliable tool for probabilistic forecasting and risk-aware decision-making in sustainable water resource management. It offers a flexible, scalable methodology for modeling complex hydrological systems under uncertainty.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition commonly assessed through clinical interviews, behavioral observation, and rating scales. Although electroencephalography (EEG) has emerged as a promising complementary tool for ADHD assessment, robust subject-independent classification remains challenging due to inter-subject variability, limited datasets, and the need for interpretable computational models. This work introduces EEG-TACT, a compact end-to-end deep learning architecture for identifying ADHD subjects from EEG epochs. The proposed model integrates an EEGNet-inspired convolutional embedding, a Transformer encoder operator, and an attention-based pooling mechanism to jointly capture local spatiotemporal EEG patterns, contextual temporal dependencies, and task-relevant latent representations. EEG-TACT was evaluated on a publicly available EEG dataset using strict, subject-independent stratified group partitions, ensuring that data from the same subject were never shared across the training, validation, and test subsets. Model interpretability is examined through learned temporal filter responses, class-conditioned self-attention maps, and latent-space projections, while an ablation study quantifies the contribution of each architectural component. Performance was assessed at the fold, subject, and epoch levels, together with statistical significance comparisons against representative state-of-the-art architectures. EEG-TACT outperformed the contrasted models, achieving subject-level accuracy of 87.5%, recall of 96.0%, and precision of 82.8%, while requiring only a few thousand trainable parameters. By exhaustively repeating the initialization, the proposed model demonstrated improved labeling reliability and achieved the best average ranking among the evaluated architectures. The reported results therefore prove that EEG-TACT provides a compact, robust, and interpretable model for EEG-based ADHD identification under subject-independent evaluation settings.

Sustainable water allocation, drought mitigation, and operational planning require reliable forecasting models that account for hydroclimatic variability while respecting physical constraints. This study proposes Chd-Gamma, a chained correlated Gaussian Process (GP) framework for multi-output hydrological forecasting. The proposed model extends chained GPs beyond independent or single-output settings by embedding their latent likelihood-parameter functions in a Linear Model of Coregionalization. Chd-Gamma also enhances conventional multi-output GP hydrological forecasting by replacing Gaussian likelihood assumptions with a Gamma likelihood, thereby enforcing non-negativity and representing skewed and heteroscedastic storage distributions. The proposed model was contrasted with the well-known Long Short-Term Memory (LSTM) network, the multi-output Linear Model of Coregionalization GP (LMC), and the chained correlated GP with Gaussian likelihood (Chd-Normal) for forecasting the daily useful storage volumes from 23 Colombian reservoirs recorded from 2010 to 2022 across multiple prediction horizons. The results over a two-year testing period show that Chd-Gamma provides the strongest overall performance across the four metrics considered. Chd-Gamma reduced the mean squared error by 80% with respect to LSTM and 20% relative to Chd-Normal. In terms of probabilistic performance, the average Negative Log Predictive Density (NLPD) improved by up to 21%. Compared to LMC, with narrow prediction intervals but low coverage, and Chd-Normal, also narrow but overcovering, Chd-Gamma achieves near-nominal coverage of 0.992 with a moderate increase in interval width, pointed towards the best calibration–sharpness trade-off. These findings demonstrate that Chd-Gamma improves accuracy and uncertainty representation while maintaining physically consistent forecasts, making it suitable for risk-aware reservoir-operation support.