The rapid growth of organic waste volumes in urban areas and increasing environmental pressures necessitate the transition toward sustainable and risk-informed municipal waste management systems. This study aims to develop a data-driven decision support framework for the risk-informed management of municipal organic waste within the context of sustainable urban development. The proposed approach integrates multi-source municipal data, advanced preprocessing techniques, entropy-based feature weighting, and an ensemble of machine learning models, including Random Forest, Gradient Boosting, and XGBoost. An integrated environmental risk index is formulated to quantify the state of the waste management system and to support predictive analytics. The results demonstrate high predictive performance and reveal that key risk drivers include demographic pressure, transport accessibility, infrastructure characteristics, and seasonal variability of waste generation. The developed framework enables the integration of predictive risk analytics into municipal decision support systems, facilitating optimized waste collection logistics, infrastructure planning, and early identification of critical conditions. The findings confirm that data-driven approaches can significantly enhance the efficiency and adaptability of urban waste management systems. The proposed framework contributes to sustainable urban development by supporting circular economy principles and enabling proactive, risk-aware governance of municipal organic waste systems.

The rapid growth of organic waste volumes in urban areas and increasing environmental pressures necessitate the transition toward sustainable and risk-informed municipal waste management systems. This study aims to develop a data-driven decision support framework for the risk-informed management of municipal organic waste within the context of sustainable urban development. The proposed approach integrates multi-source municipal data, advanced preprocessing techniques, entropy-based feature weighting, and an ensemble of machine learning models, including Random Forest, Gradient Boosting, and XGBoost. An integrated environmental risk index is formulated to quantify the state of the waste management system and to support predictive analytics. The results demonstrate high predictive performance and reveal that key risk drivers include demographic pressure, transport accessibility, infrastructure characteristics, and seasonal variability of waste generation. The developed framework enables the integration of predictive risk analytics into municipal decision support systems, facilitating optimized waste collection logistics, infrastructure planning, and early identification of critical conditions. The findings confirm that data-driven approaches can significantly enhance the efficiency and adaptability of urban waste management systems. The proposed framework contributes to sustainable urban development by supporting circular economy principles and enabling proactive, risk-aware governance of municipal organic waste systems.

The rapid development of precision agriculture technologies requires intelligent systems capable of automatically analyzing unmanned aerial vehicle (UAV) imagery and transforming image-processing results into structured information for crop-monitoring decision support. This study develops and computationally validates an integrated framework combining deep learning-based semantic segmentation, RGB-based spatial interpretation, image-space vectorization of candidate RGB heterogeneity zones, and monitoring-priority assessment. Experimental validation was performed using the open-access dataset “High-Resolution RGB Images and Corresponding Masks of Agricultural Fields”, comprising mixed agricultural vegetation scenes. Following data-integrity verification, 326 valid RGB image–reference mask pairs were retained and divided into 21,324 non-overlapping 512 × 512-pixel patches. U-Net-ResNet50, DeepLabV3-ResNet50, and FCN-ResNet50 were evaluated in three repeated source-image-grouped holdout experiments using the full dataset. U-Net-ResNet50 achieved the highest held-out performance, with mean Precision = 0.9946 ± 0.0008, Recall = 0.9931 ± 0.0021, vegetation IoU = 0.9877 ± 0.0020, and Dice = 0.9938 ± 0.0010, and was therefore selected for subsequent spatial analysis. Its segmentation outputs were combined with VARI, ExG, and GLI indices to identify candidate RGB heterogeneity zones, which were converted into image-space vector objects in source-image pixel coordinates. The revised full-data analysis generated 58,206 candidate RGB heterogeneity zones. These regions represent visible canopy heterogeneity rather than physiologically confirmed crop stress and are intended to support targeted field verification and monitoring prioritization. The results demonstrate the feasibility of an end-to-end UAV RGB processing workflow that transforms pixel-level segmentation outputs into structured spatial information for precision-agriculture monitoring and decision support.

The increase in the volume of organic household waste and the transition to a circular economy require the development of reliable forecasting tools capable of ensuring effective planning of waste recycling and bioenergy production systems. Despite significant advances in machine learning methods, most current research focuses on improving forecasting accuracy, while paying insufficient attention to model interpretation and the use of the results to support management decisions. The aim of this study is to develop and validate, using empirical municipal data, an explainable machine learning framework for forecasting the generation of organic household waste to support decision-making regarding the development of biogas and biomethane systems. The proposed framework combines data preprocessing, feature engineering, ensemble machine learning algorithms, prediction quality assessment, Explainable Artificial Intelligence (XAI) methods, and bioenergy potential assessment into a unified decision support system. The study was conducted using an empirical dataset comprising 10,800 observations for 20 local communities, obtained from the monitoring and accounting records of LKP “Green City” (Lviv, Ukraine) and covering the period from 1 January 2024 to 23 June 2025. The dataset includes demographic, socioeconomic, territorial, tourism-related, natural and climatic, infrastructural, logistical, organizational and economic, and temporal characteristics. To forecast the normalized index of organic household waste generation, we developed and compared Random Forest, Extra Trees, Gradient Boosting, XGBoost, LightGBM, and CatBoost models. A Linear Regression baseline fitted to the overlapping index-related predictors achieved MAE = 0.02339, RMSE = 0.02886, and R² = 0.96412 on the chronological holdout set, indicating that a substantial part of the constructed target is linearly reconstructible. Among the six ensemble models, CatBoost yielded the most favorable point estimates, with MAE = 0.02933, RMSE = 0.03720, and R² = 0.84958. Strict leave-one-municipality-out validation showed lower zero-shot spatial transferability, with Extra Trees achieving the highest pooled performance (MAE = 0.05198, RMSE = 0.06464, R² = 0.58159). Extra Trees-based local adaptation substantially improved transfer to previously unseen communities, reaching pooled MAE = 0.03234, RMSE = 0.04121, and R² = 0.83424 after 120 days of community-specific observations, although performance remained heterogeneous across municipalities. SHAP analysis identified tourism activity, air temperature, household income, the proportion of the urban population, weekends, and distance to water bodies as important contributors to the model predictions and revealed nonlinear model-specific associations for tourism activity, temperature, and income. Based on the predicted organic waste generation, a scenario-based assessment of the annual bioenergy potential of the investigated communities was performed, demonstrating substantial differences in potential electricity generation among communities. The scientific contribution of this study lies in the integration of ensemble forecasting, explainable artificial intelligence methods, and scenario-based bioenergy potential assessment within a single decision-support framework. The practical value of the proposed approach lies in its potential use for preliminary forecasting of organic waste resource availability and scenario-based assessment of bioenergy potential. Further operational, infrastructure, or investment applications require external validation using independent municipal datasets and verification under real operating conditions.