Integrated Forecasting of Infrastructure Resilience Using Corrosion, Water Systems, and Agricultural Time-Series Models

This study presents an integrated forecasting framework for infrastructure resilience through the application of corrosion modeling, water systems forecasting, and agricultural time-series analysis. By employing machine learning techniques, artificial neural networks, and statistical models, the framework addresses infrastructure degradation, water resource variability, and agricultural productivity under environmental stressors. Corrosion models estimate material degradation rates based on environmental and operational parameters. Water systems models utilize forecasting algorithms and resilience structures to support climate-adaptive resource management. Agricultural time-series models apply historical data to predict yield fluctuations and market trends, particularly within smallholder systems. The integration of these models supports cross-sectoral risk analysis, informs infrastructure planning, and enables decision-making under uncertainty. Key limitations include data heterogeneity, model generalization, and governance fragmentation. Future research targets enhanced data integration, methodological refinement, and collaborative modeling approaches to strengthen adaptive infrastructure systems.