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考虑未来土地利用动态情景的滑坡易发性制图

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山区城镇化的建设会造成土地利用的变化,从而改变滑坡的易发性.然而,目前在滑坡灾害的管理中缺乏未来土地利用动态变化下的易发性研究.基于此,本文以重庆市万州区长江沿岸乡镇为例,首先利用斑块生成土地利用变化模拟(Patch-gen-erating Land Use Simulation,PLUS)模型对该地区2025年与2030年的土地利用动态因子进行预测;随后,结合研究区所收集的静态因子与滑坡灾害数据构建了易发性评价的数据库.并利用粒子群算法(Particle Swarm Optimization,PSO)对XGBoost、LightGBM与CatBoost共3种基于GBDT(Gradient Boosting Decision Tree)框架的集成学习模型进行超参数的调整并 开展动态易发性建模;最后,基于最优评价模型开展未来土地利用动态变化情景下的滑坡易发性进行制图.研究结果表明:①基于PLUS模型的土地利用变化结果能够为未来滑坡易发区域的预测提供准确的基础数据;②通过对比PSO算法参与建模前后的AUC结果,可以发现PSO-CatBoost模型能够更好的拟合研究区滑坡清单和触发因子的非线性关系,并获得了最优的建模精度;③综合考虑未来土地利用与滑坡易发性的研究框架表明未来长江沿岸乡镇的建筑用地发展会提高潜在的滑坡易发性.本文所提出的研究思路对于构建坚韧有效的山区城镇防灾减灾体系具有重要的实际意义,并可为未来山区城镇的规划提供科学指导.
Mapping the Landslide Susceptibility Considering Future Land Use Dynamics Scenario
The urbanization in mountainous areas will cause land use changes,thereby potentially altering the landslide susceptibility.However,there is a lack of research on landslide susceptibility under future land use dynamics in landslide hazard management at present.In this study,taking the towns along the Yangtze River in Wanzhou District,Chongqing City as an example,we first used the Patch-generating Land Use Simulation(PLUS)model to predict the dynamic factors of land use in 2025 and 2030.Then,combined with the static factors collected and landslide data in the study area,we constructed a database for susceptibility assessment.The Particle Swarm Optimization(PSO)algorithm was used to adjust the hyperparameters of the ensemble learning models based on the GBDT(Gradient Boosting Decision Tree)framework,including XGBoost,LightGBM,and CatBoost,and conduct dynamic susceptibility modeling.Finally,we mapped the landslide susceptibility under scenarios of future land use dynamics based on the optimal evaluation model.The results show that:(1)the land use change results based on the PLUS model can provide accurate basic data for predicting future landslide-prone areas;(2)by comparing the AUC results before and after the involvement of the PSO algorithm in the modeling,we found that the PSO-CatBoost model was able to better fit the nonlinear relationship between the landslide inventory and the triggering factor in the study area,and achieved an optimal modeling accuracy;(3)our research framework that integrates future land use dynamics and landslide susceptibility suggested that future development of building lands in townships along the Yangtze River will increase landslide susceptibility.The research approach proposed in this paper is of great practical significance for building a resilient and effective disaster prevention and reduction system in mountainous urban areas and can provide scientific guidance for the future planning of mountainous urban areas.

landslide susceptibilityPLUS modelfuture land use changeParticle Swarm Optimization(PSO)XGBoostLightGBMCatBoostWanzhou District

金必晶、曾韬睿、桂蕾、殷坤龙、朱宇航、刘洋

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中国地质大学(武汉)工程学院,武汉 430074

中国地质大学(武汉)地质调查研究院,武汉 430074

滑坡易发性 PLUS模型 未来土地利用变化 粒子群算法(PSO) XGBoost LightGBM CatBoost 万州区

国家自然科学基金国家自然科学基金国家重点研发计划

4187752416015632023YFC3007201

2024

10.12082/dqxxkx.2024.240009

地球信息科学学报
中国科学院地理科学与资源研究所

地球信息科学学报

CSTPCD北大核心
影响因子：1.004
ISSN：1560-8999
年,卷(期)：2024.26(6)
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