Incomplete environmental datasets pose significant challenges in developing accurate predictive models, particularly in hydrological research. This study addresses data missingness by investigating gap imputation methodologies for datasets with 5–20% data absence, focusing on the Mureș River in Romania. Utilizing a novel approach, we applied various imputation techniques, including the ratio method, Kalman filtering, and machine learning algorithms (XGBoost, Gradient Boosting, Random Forest and CatBoost), while developing an innovative self-assessment metric for evaluating imputation performance without relying on external reference data. Through systematic analysis of hydrological station data from four monitoring points, we artificially introduced data gaps to rigorously test method applicability. The research demonstrates the feasibility of constructing a robust self-evaluation framework for selecting optimal imputation techniques, potentially enhancing data reliability and analytical precision in environmental and geospatial research. Our findings contribute a structured methodology for addressing data incompleteness, offering researchers a quantitative approach to improving dataset integrity and predictive modeling in complex environmental systems.

This study aims to reconstruct NDI45 missing values due to cloud cover while outlining the importance of vegetation health for the climate–carbon cycle and the benefits of the NDI45 index for high canopy area indices. The methods include a novel hybrid framework that combines a deterministic Kalman filter (KF) and a clustering-based LSTM network to generate gap-free NDI45 series with 20 m spatial and 5-day temporal resolution. The innovation of the applied method relies on achieving a single-sensor workflow, provides a pixel-level uncertainty map, and minimizes LSTM overfitting through clustering based on a correlation threshold. In the northern Pampas (South America), this hybrid approach reduces the MAE by 22–35% on average and narrows the 95% confidence interval by 25–40% compared to the Kalman filter or LSTM alone. The three-dimensional spatio-temporal analysis demonstrates that the KF–LSTM hybrid provides better spatial homogeneity and reliability across the entire study area. The proposed framework can generate gap-free, high-resolution NDI45 time series with quantified uncertainties, enabling more reliable detection of vegetation stress, yield fluctuations, and long-term resilience trends. These capabilities make the method directly applicable to operational drought monitoring, crop insurance modeling, and climate risk assessment in agricultural systems, particularly in regions prone to frequent cloud cover. The framework can be further extended by including radar backscatter and multi-model ensembles, thus providing a promising basis for the reconstruction of global, high-resolution vegetation time series.

Cookbooks serve as structured records of both ingredient repertoires and the underlying processing logics that define a culture’s culinary identity. By modeling five Transylvanian ethnic traditions—Hungarian, Romanian, Transylvanian Saxon, Jewish, and Armenian—as weighted, undirected co-occurrence networks, we found that interethnic connectivity is driven primarily by technological processes rather than simple ingredient presence. Using purposive sampling, we compiled a harmonized corpus of 1409 recipes and applied explicit ingredient normalization (retention, aggregation, and deconstruction) and a 14-class functional taxonomy. We computed density, clustering, modularity, and centrality measures and compared cuisines with a binary Jaccard index, both at the category level and within four course types. Category networks reveal an exceptionally tight Hungarian–Romanian–Armenian triangle (J > 0.95), whereas course-level results show that main dishes exhibit the strongest divergence (J < 0.28). These results support a layered identity model of Transylvanian gastronomy: while shared confectionery frameworks in desserts dissolve ethnic boundaries (M < 0.17), main dishes actively guard cultural boundaries through distinct technological signatures.