Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study propos...Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.展开更多
The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical ...The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.展开更多
[目的]揭示黄土高原农牧交错带草地净初级生产力(NPP)时空演变规律,明确自然因素与人类活动作用及其交互驱动机制,为该区域或类似草地生态系统的科学管理与可持续利用提供理论依据。[方法]应用CASA模型模拟1990—2020年草地NPP,结合趋...[目的]揭示黄土高原农牧交错带草地净初级生产力(NPP)时空演变规律,明确自然因素与人类活动作用及其交互驱动机制,为该区域或类似草地生态系统的科学管理与可持续利用提供理论依据。[方法]应用CASA模型模拟1990—2020年草地NPP,结合趋势分析及最优参数地理探测器识别草地NPP时空变化特征的驱动因素。[结果](1) 1990—2020年黄土高原农牧交错带草地NPP整体呈增加趋势,年平均增长率4.93 g C/(m^(2)·a)。NPP的阶段性特征比较明显,1990—1999年草地NPP呈不显著下降趋势;2000年后,草地NPP开始增加,2011—2020年这一阶段增加显著。(2) 1990—2020年草地NPP呈显著增加的面积达82.15%;减少的地区仅占3.14%,零星分布于内蒙古自治区。(3)草地NPP呈东南高、西北低的分异格局,降水和坡度是主要驱动因素,并且总体呈正向作用;人类活动的影响在因子间非线性交互作用下显著增强,其中放牧、耕地及建设用地扩张整体呈负向效应。但随着人均GDP的增加,生态保护与技术投入降低了人类活动的负面影响。[结论]黄土高原农牧交错带草地整体恢复较好,未来草地管理中考虑自然作用的影响与差异化的适度放牧政策,增加农牧民收入,同时平衡草地生态系统的直接受益者(草地生产力使用)和间接受益者(草地调节服务)之间的权衡。展开更多
合理选择滑坡易发性评价因子是揭示滑坡易发区空间分布特征的重要前提。通过考虑因子最优离散分类,结合地理探测器(Geodetector,GD)与空间主成分分析法(Spatial Principal Component,SPCA)构建滑坡易发性指数。发现评价因子普遍呈现离...合理选择滑坡易发性评价因子是揭示滑坡易发区空间分布特征的重要前提。通过考虑因子最优离散分类,结合地理探测器(Geodetector,GD)与空间主成分分析法(Spatial Principal Component,SPCA)构建滑坡易发性指数。发现评价因子普遍呈现离散分类数越大,因子解释度越高的趋势,且自然断点法与分位数分类法的离散分类结果较好;因子间的交互作用对因子的解释度有一定影响,归一化植被指数(NDVI)的单因子解释度为0.453,但在交互作用下其耦合解释度范围仅为[0.03,0.226];研究区滑坡易发性指数为0.589,属于中度易发区,高度易发区与极易发区主要分布在研究区北部高植被覆盖的山地丘陵处;研究区的滑坡易发性指数具有显著空间聚集特征,高-高聚集区主要分布在该区北部中低山丘陵处,低-低聚集区主要分布在该区南部低缓河谷处。展开更多
基金the National Key Research and Development Program of China(Grant No.2022YFF1302405)the Yunnan Province Key Research and Development Program(Grant No.202203AC100005)+1 种基金the National Natural Science Foundation of China(Grant No.42061005,42067033)Applied Basic Research Programs of Yunnan Province(Grant No.202101AT070110,202001BB050073).
文摘Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.
文摘The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.
文摘[目的]揭示黄土高原农牧交错带草地净初级生产力(NPP)时空演变规律,明确自然因素与人类活动作用及其交互驱动机制,为该区域或类似草地生态系统的科学管理与可持续利用提供理论依据。[方法]应用CASA模型模拟1990—2020年草地NPP,结合趋势分析及最优参数地理探测器识别草地NPP时空变化特征的驱动因素。[结果](1) 1990—2020年黄土高原农牧交错带草地NPP整体呈增加趋势,年平均增长率4.93 g C/(m^(2)·a)。NPP的阶段性特征比较明显,1990—1999年草地NPP呈不显著下降趋势;2000年后,草地NPP开始增加,2011—2020年这一阶段增加显著。(2) 1990—2020年草地NPP呈显著增加的面积达82.15%;减少的地区仅占3.14%,零星分布于内蒙古自治区。(3)草地NPP呈东南高、西北低的分异格局,降水和坡度是主要驱动因素,并且总体呈正向作用;人类活动的影响在因子间非线性交互作用下显著增强,其中放牧、耕地及建设用地扩张整体呈负向效应。但随着人均GDP的增加,生态保护与技术投入降低了人类活动的负面影响。[结论]黄土高原农牧交错带草地整体恢复较好,未来草地管理中考虑自然作用的影响与差异化的适度放牧政策,增加农牧民收入,同时平衡草地生态系统的直接受益者(草地生产力使用)和间接受益者(草地调节服务)之间的权衡。
文摘合理选择滑坡易发性评价因子是揭示滑坡易发区空间分布特征的重要前提。通过考虑因子最优离散分类,结合地理探测器(Geodetector,GD)与空间主成分分析法(Spatial Principal Component,SPCA)构建滑坡易发性指数。发现评价因子普遍呈现离散分类数越大,因子解释度越高的趋势,且自然断点法与分位数分类法的离散分类结果较好;因子间的交互作用对因子的解释度有一定影响,归一化植被指数(NDVI)的单因子解释度为0.453,但在交互作用下其耦合解释度范围仅为[0.03,0.226];研究区滑坡易发性指数为0.589,属于中度易发区,高度易发区与极易发区主要分布在研究区北部高植被覆盖的山地丘陵处;研究区的滑坡易发性指数具有显著空间聚集特征,高-高聚集区主要分布在该区北部中低山丘陵处,低-低聚集区主要分布在该区南部低缓河谷处。
