Two-phase strategy framework for spatial prediction of landslide hazards in wide-area power linear engineering projects: the case of the China's Renewable Energy Transmission Corridors

A critical knowledge gap persists in the development of high-precision spatial prediction frameworks for landslide susceptibility assessment along wide-area linear power infrastructure. Therefore, this study develops a novel two-phase optimization framework to address this gap, focusing on China's Renewable Energy Transmission Corridors (RETCs). Phase I employs natural breaks (optimal at 26-level grading) to address spatial heterogeneity in conditioning factors, while in Phase II the selection of non-landslide sample is optimized based on different geological environment zones and areas with lower susceptibility levels. Six base machine learning models were evaluated, with two ensemble models (Stacking and Blending) achieving superior performance, achieving an Area Under the Curve (AUC) value exceeding 0.88. The Blending model demonstrated peak accuracy (AUC = 0.927), identifying 35% of transmission towers in high and very high susceptibility zones across nine provinces. The framework enables tower-specific susceptibility assessment, crucial for protecting China's 80,000 km transmission network. These findings advance RETCs resilience by: (1) establishing continuous conditioning factor optimal grading strategy for linear infrastructure, (2) introducing a replicable non-landslide sample optimization protocol, and (3) demonstrating ensemble models superiority in energy corridor landslide susceptibility mapping. This framework provides robust support for securing stable clean energy delivery, with potential applications in global renewable energy grid landslide hazards management.