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Luwei Feng

Biographic Data

ID7879921
NAMELuwei Feng
GIVEN NAMESLuwei
FAMILY NAMEFeng
SIGNATUREFENG L
AFFILIATIONSWuhan University
ORCID0009-0004-3181-6018
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS0
AUTHOR COUNT3
EDITOR COUNT0
FIRST PUBLICATION YEAR2017
LATEST PUBLICATION YEAR2021
H-INDEX0
  • Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

    Open Access•Huihui Zhang, Hugo A Loaiciga et al.•ARTICLE•ISPRS International Journal of…•2021

    Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density withou…

  • Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China

    Open Access•Yumiao Wang, Xueling Wu et al.•ARTICLE•International Journal of…•2019

    The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, g…

  • Spatiotemporal Changes in Fine Particulate Matter Pollution and the Associated Mortality Burden in China between 2015 and 2016

    Open Access•Luwei Feng, Bo Ye et al.•ARTICLE•International Journal of…•2017

    In recent years, research on the spatiotemporal distribution and health effects of fine particulate matter (PM 2.5 ) has been conducted in China. However, the limitations of different research scopes and methods have led to low comparability between regions regarding the mortality burden of PM 2.5 . A kriging model was used to simulate the distribution of PM 2.5 in 2015 and 2016. Relative risk (RR) at a specified PM 2.5 exposure concentration was…

No prominent works on this page.

  • Spatiotemporal Changes in Fine Particulate Matter Pollution and the Associated Mortality Burden in China between 2015 and 2016

    Open Access•Luwei Feng, Bo Ye et al.•ARTICLE•International Journal of…•2017

    In recent years, research on the spatiotemporal distribution and health effects of fine particulate matter (PM 2.5 ) has been conducted in China. However, the limitations of different research scopes and methods have led to low comparability between regions regarding the mortality burden of PM 2.5 . A kriging model was used to simulate the distribution of PM 2.5 in 2015 and 2016. Relative risk (RR) at a specified PM 2.5 exposure concentration was…

  • Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China

    Open Access•Yumiao Wang, Xueling Wu et al.•ARTICLE•International Journal of…•2019

    The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, g…

  • Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

    Open Access•Huihui Zhang, Hugo A Loaiciga et al.•ARTICLE•ISPRS International Journal of…•2021

    Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density withou…

Geography (3 works) · Cartography (2 works) · Environmental Science (2 works) · Geology (2 works) · Remote sensing (2 works) · Air pollution (1 works) · Air Quality and Health Impacts (1 works) · Air quality index (1 works) · Artificial Intelligence (1 works) · Artificial neural network (1 works)

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