Abstract: Concept model of regional ecological risk is built for the characteristics of ecosystems alongside Qinghai-Xizang highway and railway based on MLP (Multilayer percetron) model. Seven indices such as snow hazard, drought hazard and landslide are selected to evaluate the integrated ecological risk of the ecosystems alongside Qinghai-Xizang highway and railway. Results show that Qaidam mountain desert region has the highest average ecological risk value (4.2585), followed by Guoluonaqu alpine scrub meadow region (2.7640) and Qinghai and Qilian mountain steppe region (2.7335) among these ecosystems alongside Qinghai-Xizang highway and railway through six natural regions. As far as land cover types are concerned, the top three ecological risk values appear in the needle-leaved forest (4.3096), desert ecosystem (4.1174) and land without vegetation (3.6182) respectively, which are higher than those in the other seven land cover types in the study site. Although the risk values are influenced by natural factors and human activities, they are more strongly controlled by natural factors. According to the ecological risk characteristics, these ecosystems alongside Qinghai-Xizang highway and railway are subdivided to four subregions, including Chaidam basin high risk region, Xidatan to Dangxiong moderate risk region, and east Qinghai, Qilian and south Qinghai slight risk regions.

Key words: ecological risk assessment, artificial neural network, MLP(Multilayer percetron) model, natural factors, artificial factors

摘要： 根据青藏公路铁路沿线（50 km缓冲区）生态系统特征，选取雪灾、旱灾、崩塌滑坡等7项指标，依托人工神经网络MLP（Multilayer Percetron）模型，构建青藏公路铁路沿线生态风险评价模型。评价结果显示：青藏公路铁路沿线生态系统所跨越的6个自然区的平均生态风险值居前3位的是：柴达木山地荒漠区（4.2585），果洛那曲高寒灌丛草甸区（2.7640）、青东祁连山地草原区（2.7335）；沿线10种植被生态系统平均生态风险值居前3位的是：针叶林生态系统（4.3096）、荒漠生态系统（4.1174）和无植被地段（3.6182）。在影响各区、各植被生态系统风险值大小的因素中，自然因素为主要控制因素，人为因素影响相对较弱。依据评价结果，将青藏公路铁路沿线生态系统划分为4个区：柴达木盆地高风险区、西大滩至当雄中度风险区、青东祁连和青南2个轻度风险区。

关键词: 生态风险评价, 人工神经网络, MLP模型, 自然因素, 人为因素