<p>Landslide hazard assessment (LHA) evaluates both the spatial and temporal probability of landslide occurrences, playing a crucial role in risk mitigation and prevention. Recently, LHA is widely applied owing to the accumulated availability of high-quality landslide samples over decades. However, two critical issues remain to be tackled: (1) In a well-established landslide inventory, the time span of their occurrence is recorded to be up to several decades. The critical analysis of the timeframe scale effects of landslide samples (year vs. decade vs. half-century scales) on LHA remains absent in current methodologies. (2) The relationship between landslide occurrence and environmental factors is characterized by complex nonlinearities, making it challenging for a single model to undertake sophisticated modeling tasks. To address these issues, we conducted a study in Hubei Province, China. We collected landslide samples spanning 47&#xa0;years from 1975 to 2021. Twelve scenarios were designed based on different timeframes of landslide occurrences. Subsequently, we utilized two metaheuristic algorithms, namely the White Shark Optimizer (WSO) and the Chameleon Swarm Algorithm (CSA), to optimize the convolutional neural network (CNN) model for landslide susceptibility assessment. The rainfall thresholds for Hubei Province were derived based on the <i>R</i>_d-<i>R</i>_p model. We calculated the landslide hazard by coupling these thresholds with landslide susceptibility levels through a landslide hazard matrix. Our findings indicate that a 15-year timeframe for landslide samples is most suitable for LHA in Hubei Province, yielding the highest AUC value of 0.873 in this scenario. Furthermore, the CSA algorithm outperforms the WSO in finding the optimal parameters. The LHA based on the proposed method in this study effectively predicted rainfall-induced landslides on July 9, 2024. This demonstrates the practical application and predictive power of our proposed method in landslide hazard assessment.</p>