To clearly realize the dynamic process as well as the dynamic fragmentation behavior of a long run-out rockslide, a novel numerical method for landslide simulation of the coupled finite-discrete element method (FDEM) was applied and the Jichang rockslide was used as a case. The calibrated simulation result of the FDEM in a rockslide deposit corresponds well with the real rockslide deposit. The main run-out process of the rockslide lasts for 75 s and can be divided into acceleration and deceleration stages, which last for 33 s and 42 s, respectively. The maximum overall rockslide movement speed is 35 m/s while the partial sliding mass reaches 45 m/s. The fracturing, fragmentation, and disintegration processes of the sliding mass can be clearly observed from the dynamic scenarios. Fracture energy generated by rock fracturing constantly increases with time in a non-linear form. Of the total fracture energy, 54% is released in the initial 5 s because of fracturing, and 39% of the total fracture energy is released because of fragmentation and disintegration in the last 35 s. The accumulated friction energy increases in the whole run-out process, and its magnitude is much greater than the kinetic energy and fracture energy of the sliding mass.

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions.