Efficient coordination within the supply chain of prefabricated construction remains a significant challenge due to the high level of interdependence among supply chain participants, the complexity of information flows, and the sensitivity of construction processes to communication delays. This study proposes an integrated methodological framework that combines fuzzy logic and social network analysis (SNA) to evaluate the structural stability and interaction dynamics of supply chain participants. First, a synthetic indicator—link stability—is introduced to quantify the robustness of relationships between supply chain actors. Link stability is defined as a function of five determinants: collaboration level, trust level, communication quality, adoption of digital tools, and effectiveness of dispute resolution. Fuzzy logic is applied to calculate this indicator for each pair of participants, reducing subjectivity in expert assessments. Second, the link stability matrix is used to compute a wide set of centrality measures, including degree, betweenness, closeness, eigenvector, PageRank, information, harmonic, and second-order centralities. These metrics reveal the structural influence of each actor within the network and allow for the identification of core, semi-peripheral, and peripheral roles. A heatmap demonstrates a highly centralized network structure dominated by managerial and design roles. The results contribute to improving supply chain resilience, enhancing communication pathways, and supporting decision-making in prefabricated construction projects.

Post-war reconstruction requires effective decision-support tools capable of integrating technical, economic, and organizational criteria under conditions of high uncertainty. The evaluation and prioritization of damaged buildings for recovery interventions are critical challenges for reconstruction project management. This study proposes a hybrid decision-support framework for assessing the strategic feasibility of building recovery using a novel Strategic Revitalization Index (SRI). The proposed methodology integrates a hierarchical fuzzy inference system, simulation techniques, and an artificial neural network surrogate model. The fuzzy model aggregates four key evaluation dimensions: technical condition of the building, economic feasibility of recovery actions, project implementation capability, and environmental and social impact. To analyze the model’s behavior and generate training data, a synthetic dataset was created using Latin Hypercube Sampling, covering a wide range of possible reconstruction conditions. The generated dataset was subsequently used to train an artificial neural network capable of approximating the nonlinear mapping implemented by the fuzzy decision model. The obtained results demonstrate high predictive performance of the surrogate model, with R² = 0.976, RMSE = 0.0266, MAE = 0.0133, and MAPE = 4.95%. Scenario analysis further illustrates how different recovery strategies influence SRI values and enables comparison of alternative reconstruction approaches. The proposed framework provides a flexible analytical tool for supporting strategic decision-making in post-war reconstruction projects. By combining fuzzy logic, simulation techniques, and machine learning, the model enables systematic prioritization of recovery strategies and may support large-scale reconstruction planning in post-conflict environments.

High-frequency Doppler monitoring can support operational water-resource assessment in semi-arid and irrigation-dependent rivers, but existing sensor-to-forecast workflows often address data quality control, temporal diagnostics, and forecasting as separate tasks. This limits their ability to determine whether irregular and potentially erroneous sensor records contain a temporal structure that can support reliable short-term prediction. This study develops a forecastability-oriented sensor-to-forecast workflow that integrates quality control of Supervisory Control and Data Acquisition (SCADA)-based Doppler records, hydraulic consistency assessment, temporal and multiscale diagnostics, and comparative one-hour-ahead forecasting within a single reproducible framework. The approach was evaluated using multichannel water-level, discharge, and velocity observations from a fixed monitoring cross-section of the Talas River near Zhasorken, Kazakhstan. Autocorrelation analysis, stationarity tests, Detrended Fluctuation Analysis, STL decomposition, Morlet wavelet analysis, and the Zivot–Andrews structural-break test were used to characterize the selected water-level series before model evaluation. The results revealed strong short-lag dependence, persistent nonstationary scaling, localized multiscale variability, and no stable seasonal pattern. A structural change identified in mid-July 2025 partly explained the apparent nonstationarity. Sensitivity analysis based on uninterrupted segments confirmed that the main persistence and multiscale features remained present without interpolation across multi-day gaps, although interpolation increased dependence estimates at 24 and 48 h lags. In the independent January–March 2026 test period, VAR achieved the lowest MAE of 0.84 mm, whereas ARIMA + STL achieved the lowest RMSE of 1.51 mm, outperforming the persistence, recurrent neural-network, and tree-based benchmarks under the selected configuration. The novelty of the proposed workflow lies in using sensor-data quality and temporal structure jointly to guide target selection, model comparison, and forecastability interpretation. The findings are site-specific and require multi-year, multi-site validation and integration of meteorological, upstream, and irrigation-management predictors before operational deployment.