High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution.This study proposed a vertical route planning meth...High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution.This study proposed a vertical route planning method for unmanned aerial vehicles(UAVs),which could achieve rapid image collection based on strictly calculated route parameters.The effectiveness of this method was verified using a DJI Mavic 2 Pro,obtaining high-resolution landslide images within the Dongchuan debris flow gully,in the Xiaojiang River Basin,Dongchuan District,Yunnan,China.A three-dimensional(3D)model was constructed by the structure-from-motion and multi-view stereo(SfM-MVS).Micro-geomorphic features were analyzed through visual interpretation,geographic information system(GIS),spatial analysis,and mathematical statistics methods.The results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement accuracy.The 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy(horizontal accuracy better than 6 cm,elevation accuracy better than 3 cm,and relative accuracy better than 3.5 cm).The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides,facilitating intuitive acquisition of surface details.The slope of landslide clusters ranged from 36°to 72°,with the majority of the slope facing east and southeast.Upper elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation levels.During the rainy season,surface runoff was abundant,and steep topography exacerbated changes in surface features.This route method is suitable for unmanned aerial vehicle(UAV)landslide surveys in complex mountainous environments.The geomorphological analysis methods used will provide references for identifying and describing topographic features.展开更多
A class of Sturm-Liouville problems with discontinuity is studied in this paper.The oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions are obtained.The main method used in ...A class of Sturm-Liouville problems with discontinuity is studied in this paper.The oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions are obtained.The main method used in this paper is based on Prufer transformation,which is different from the classical ones.Moreover,we give two examples to verify our main results.展开更多
为提高城市轨道交通网络关键节点的识别精度,基于复杂网络理论、交通网络性能特征,考虑轨道交通站点的城市活力信息,建立关键节点评价指标,采用轻量级梯度提升决策树(light gradient boosting machine,LightGBM)机器学习算法计算逼近理...为提高城市轨道交通网络关键节点的识别精度,基于复杂网络理论、交通网络性能特征,考虑轨道交通站点的城市活力信息,建立关键节点评价指标,采用轻量级梯度提升决策树(light gradient boosting machine,LightGBM)机器学习算法计算逼近理想解排序(technique for order preference by similarity to ideal solution,TOPSIS)法中各项评价指标的权重,提出融合LightGBM算法和TOPSIS法的城市轨道交通关键节点识别模型。以杭州城市轨道交通网络为例,针对识别出的前15个关键节点进行动态攻击,通过计算删除关键节点后的网络效率与最大连通子图比例验证模型的识别精度。结果表明:删除前5个关键节点后,网络效率与最大连通子图比例分别为46.89%、56.47%,在一定程度上破坏了轨道交通的网络结构;删除前15个关键节点时,网络效率与最大连通子图比例分别下降至25.61%与16.6%,轨道交通网络基本完全破坏。基于LightGBM和TOPSIS法的城市轨道交通关键节点模型可有效识别交通网络中的关键节点,识别精度较高。展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 62266026)
文摘High-resolution landslide images are required for detailed geomorphological analysis in complex topographic environment with steep and vertical landslide distribution.This study proposed a vertical route planning method for unmanned aerial vehicles(UAVs),which could achieve rapid image collection based on strictly calculated route parameters.The effectiveness of this method was verified using a DJI Mavic 2 Pro,obtaining high-resolution landslide images within the Dongchuan debris flow gully,in the Xiaojiang River Basin,Dongchuan District,Yunnan,China.A three-dimensional(3D)model was constructed by the structure-from-motion and multi-view stereo(SfM-MVS).Micro-geomorphic features were analyzed through visual interpretation,geographic information system(GIS),spatial analysis,and mathematical statistics methods.The results demonstrated that the proposed method could obtain comprehensive vertical information on landslides while improving measurement accuracy.The 3D model was constructed using the vertically oriented flight route to achieve centimeter-level accuracy(horizontal accuracy better than 6 cm,elevation accuracy better than 3 cm,and relative accuracy better than 3.5 cm).The UAV technology could further help understand the micro internal spatial and structural characteristics of landslides,facilitating intuitive acquisition of surface details.The slope of landslide clusters ranged from 36°to 72°,with the majority of the slope facing east and southeast.Upper elevation levels were relatively consistent while middle to lower elevation levels gradually decreased from left to right with significant variations in lower elevation levels.During the rainy season,surface runoff was abundant,and steep topography exacerbated changes in surface features.This route method is suitable for unmanned aerial vehicle(UAV)landslide surveys in complex mountainous environments.The geomorphological analysis methods used will provide references for identifying and describing topographic features.
基金Supported by the Natural Science Foundation of Shandong Province(ZR2023MA023,ZR2021MA047)Guangdong Provincial Featured Innovation Projects of High School(2023KTSCX067).
文摘A class of Sturm-Liouville problems with discontinuity is studied in this paper.The oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions are obtained.The main method used in this paper is based on Prufer transformation,which is different from the classical ones.Moreover,we give two examples to verify our main results.
文摘为提高城市轨道交通网络关键节点的识别精度,基于复杂网络理论、交通网络性能特征,考虑轨道交通站点的城市活力信息,建立关键节点评价指标,采用轻量级梯度提升决策树(light gradient boosting machine,LightGBM)机器学习算法计算逼近理想解排序(technique for order preference by similarity to ideal solution,TOPSIS)法中各项评价指标的权重,提出融合LightGBM算法和TOPSIS法的城市轨道交通关键节点识别模型。以杭州城市轨道交通网络为例,针对识别出的前15个关键节点进行动态攻击,通过计算删除关键节点后的网络效率与最大连通子图比例验证模型的识别精度。结果表明:删除前5个关键节点后,网络效率与最大连通子图比例分别为46.89%、56.47%,在一定程度上破坏了轨道交通的网络结构;删除前15个关键节点时,网络效率与最大连通子图比例分别下降至25.61%与16.6%,轨道交通网络基本完全破坏。基于LightGBM和TOPSIS法的城市轨道交通关键节点模型可有效识别交通网络中的关键节点,识别精度较高。
