As a crucial component of transportation networks, major railroad projects inevitably lead to ecological damage during the construction phase. Therefore, early warning systems for ecological health are essential for promoting effective ecological governance. This study proposes an ecological health evaluation index based on the PSR (Pressure-State-Response) model. Furthermore, the study establishes a status warning model for projects using system dynamics theory with a focus on ecology, environment, and resources; and conducts simulation modeling to assess ecological health along the railway lines. The study identified that the water supply increased from 0 to 269.376 million cubic meters, and new soil and water runoff increased from 0 to 992.912 tons from 2015 to 2018. In contrast, construction wastewater and domestic wastewater remained stable at 0.63 tons and 0.32 tons, respectively, throughout the construction period. Ecological restoration improved accordingly and gradually stabilized in the following years. The empirical findings from the railway project indicate that: (1) Expansion of construction land imposes a burden on the region's natural ecology and with the implementation of ecological protection helping to reduce the extent of ecological damage; (2) Major railroad projects have led to pollution of the regional water environment, necessitating alignment with local policies for comprehensive environmental management throughout the construction process; (3) Economic development drives increased energy consumption and water resource pressures in the construction region, highlighting the importance of resource efficiency for sustainable development. The early warning evaluation model proposed in this study enhances prediction and evaluation tools for the ecological governance of major railways, thereby offering specific preventive and governance measures for ecological and sustainable construction of major transportation projects.

Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas.