This study presents a leakage-proof hybrid machine learning framework for early flood risk forecasting using multimodal spatiotemporal tabular data. An event-based dataset covering Kazakhstan from 2001 to 2021 was created by integrating topographic characteristics, hydrometeorological variables, long-term surface water dynamics, and remotely sensed spectral indices. To ensure realistic assessment, only pre-event observations were used, and event-based temporal data separation was employed to prevent leakage between the training and test subsets. The proposed Remote Sensing Adaptive Linear Opinion Pool Machine Learning (RS-ALOP-ML) framework combines multiple logistic regression experts with different regularization strengths through an Adaptive Linear Opinion Pool (ALOP) probabilistic fusion strategy, thereby preserving interpretability while improving forecasting robustness. The proposed framework was evaluated for four independent forecast horizons (T + 1, T + 7, T + 14, and T + 30 days) and compared with traditional machine learning algorithms and state-of-the-art tabular deep learning models, including Random Forest, ExtraTrees, XGBoost, LightGBM, CatBoost, MLP, FT-Transformer, TabNet, and Process Wide&Deep. Experimental results demonstrate that the proposed hybrid approach consistently achieves competitive or superior forecasting performance while maintaining computational efficiency and transparent probabilistic outputs. The study highlights that carefully designed hybrid machine learning architectures, combined with leakage-safe evaluation protocols, provide a robust foundation for multimodal environmental forecasting and decision support applications.

This study proposes a hybrid computational model for diagnosing such biases using groundwater observation data across Kazakhstan. The analytical dataset included 2402 georeferenced observations, including 492 natural springs from OpenStreetMap (OSM), 109 boreholes from OSM, and 1801 spatially filtered pseudo-absence observations. Springs and boreholes together formed 601 positive groundwater observations, while pseudo-absence samples represented a spatially filtered background level rather than confirmed groundwater absence. Each observation was characterized by 89 environmental predictors extracted from Google Earth Engine. The proposed hybrid observation source bias index (HOSBI) combines a normalized robust effect size based on the median absolute value of the Cliff delta, multivariate distribution divergence quantified using RBF-MMD, and spatially confirmed source distinctiveness. These components were assigned fixed weights of 0.40, 0.35, and 0.25 to emphasize statistical and distributional data while maintaining spatial validation. Spatial cross-validation achieved a balanced accuracy of 0.855 for distinguishing OSM sources from OSM wells and 0.846 for separating positive observations from background pseudo-absences. Climate showed the strongest source-related bias (HOSBI = 0.923), while Sentinel-1 SAR contributed the most to the contrast between positive and background data (HOSBI = 0.923). The proposed framework provides an interpretable and replicable preliminary assessment of source bias in heterogeneous geospatial datasets.

This study presents a hybrid artificial intelligence system for latent cardiovascular risk stratification based on publicly available clinical and laboratory data. The proposed system integrates data preprocessing, auxiliary target modeling, latent phenotyping using UMAP and Gaussian mixture models, fuzzy logic-based risk integration, and multilevel predictive modeling. The key contribution of the system is the construction of a proxy target reflecting latent risk progression by combining phenotypic structure, probabilistic indicators, and mortality-related anchor points. Experimental evaluation was conducted on the NHANES dataset. The final analytical cohort included 78,822 adult participants, and the modeling set was divided into training, validation, and test subgroups using a stratified 70/15/15 design. The proposed PhaseFuzzy Hybrid model achieved an accuracy of 0.8390, a balanced accuracy of 0.7302, an F1-score of 0.5225, an MCC of 0.4203, an ROC-AUC of 0.8489, a PR-AUC of 0.5014, and a best LogLoss value of 0.4290 on the test set. The latent phenotyping step also demonstrated acceptable internal validity with a silhouette coefficient of 0.4138 and a confidence of 0.8800. The results demonstrate that the proposed framework identifies hidden cardiometabolic risk factors and provides an interpretable, scalable, and calibration-aware framework for latent cardiometabolic risk stratification and population-level screening.

This study presents a spatially aware machine learning model for probabilistic groundwater potential assessment using multimodal geospatial data derived from topography, hydrotopography, climate, soil, land use, Sentinel-1 SAR, and water balance variables. The model incorporates spatially consistent data partitioning, leakage-controlled model development, and probabilistic forecasting to improve the robustness and transferability of groundwater potential assessment. A total of 2402 spatial observations, including 601 groundwater-related locations and 1801 spatially filtered pseudo-absence samples, were used to evaluate eleven machine learning and deep learning models. The proposed hybrid framework achieved the best overall validation results with ROC-AUC of 0.9319, PR-AUC of 0.8363, F1-measure of 0.8226, balanced accuracy of 0.8847, and MCC of 0.7613. Independent spatial block testing further demonstrated strong generalization ability, showing ROC-AUC of 0.9535, PR-AUC of 0.9002, F1-measure of 0.7983, balanced accuracy of 0.8594, and MCC of 0.7336. Comparative experiments demonstrated that the proposed framework remains competitive with state-of-the-art machine learning and deep learning approaches while providing robust probabilistic estimates in spatially separated validation. The resulting groundwater potential maps identify areas with environmental conditions similar to known groundwater observations and provide a reliable basis for prioritizing hydrogeological studies, groundwater exploration, and regional water resources planning in data-poor settings.