Over the past decade, CENIT has strengthened its risk-informed approach to managing pipeline geohazards, focusing on early warnings for hydrometeorological threats. With a network of 90 monitoring sensors along pipeline rights-of-way, the system provides near-real-time data on rainfall and river levels, enabling proactive responses.

By integrating Artificial Intelligence (AI) with geotechnical susceptibility models, adaptive zoning identifies locations prone to rainfall-induced landslides, guiding timely inspections and operational decisions. Despite advancements, rainfall events can deviate from historical patterns, posing challenges for predictive accuracy.

This study explores key insights, experiences, and opportunities to improve geohazard assessments along pipeline corridors by blending mathematical modeling with heuristic approaches. Case studies highlight extreme precipitation events that surpass recorded data, illustrating the complexities of forecasting rainfall-related geohazards in pipeline infrastructure.