Chinese | English

Search in the results

IGSNRR OpenIR

Browse/Search Results:  1-10 of 111 Help

  Show only claimed items
Selected(0)Clear Items/Page:    Sort:
Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method 期刊论文
GEODERMA, 2020, 卷号: 369, 页码: 15
Authors:  Yang, Lin;  Li, Xinming;  Shi, Jingjing;  Shen, Feixue;  Qi, Feng;  Gao, Binbo;  Chen, Ziyue;  Zhu, A-Xing;  Zhou, Chenghu
Favorite  |  View/Download：29/0  |  Submit date：2020/05/19
Conditioned Latin hypercube sampling  Soil mapping  Representativeness  Sample randomness  
Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble 期刊论文
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 卷号: 718, 页码: 15
Authors:  Hong, Haoyuan;  Liu, Junzhi;  Zhu, A-Xing
Favorite  |  View/Download：10/0  |  Submit date：2020/05/19
Landslide  LogitBoost alternating decision trees  Forest by penalizing attributes  Bagging  Integration model  
Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data 期刊论文
SOIL & TILLAGE RESEARCH, 2020, 卷号: 196, 页码: 13
Authors:  Yang, Lin;  He, Xianglin;  Shen, Feixue;  Zhou, Chenghu;  Zhu, A-Xing;  Gao, Bingbo;  Chen, Ziyue;  Li, Manchun
Favorite  |  View/Download：12/0  |  Submit date：2020/05/19
Soil organic carbon  Digital soil mapping  Phenological parameters  NDVI time series  Crop rotation  
Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data 期刊论文
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 卷号: 9, 期号: 2, 页码: 17
Authors:  Fan, Nai-Qing;  Zhu, A-Xing;  Qin, Cheng-Zhi;  Liang, Peng
Favorite  |  View/Download：8/0  |  Submit date：2020/05/19
digital soil mapping  invalid data  environmental covariate  SoLIM  uncertainty  large areas  China  
Estimation of Travel Time Based on Ensemble Method With Multi-Modality Perspective Urban Big Data 期刊论文
IEEE ACCESS, 2020, 卷号: 8, 页码: 24819-24828
Authors:  Zou, Zhiqiang;  Yang, Haoyu;  Zhu, A-Xing
Favorite  |  View/Download：13/0  |  Submit date：2020/05/19
Travel time estimation  ensemble method  deep neural network  gradient boosted decision trees  
A graded proportion method of training sample selection for updating conventional soil maps 期刊论文
GEODERMA, 2020, 卷号: 357, 页码: 9
Authors:  Liu, Xueqi;  Zhu, A-Xing;  Yang, Lin;  Pei, Tao;  Liu, Junzhi;  Zeng, Canying;  Wang, Desheng
Favorite  |  View/Download：10/0  |  Submit date：2020/05/19
Training sample selection method  Data mining model  Update conventional soil map  Soil-environmental relationships  
Predictive mapping with small field sample data using semi-supervised machine learning 期刊论文
TRANSACTIONS IN GIS, 2019, 页码: 17
Authors:  Du, Fei;  Zhu, A-Xing;  Liu, Jing;  Yang, Lin
Favorite  |  View/Download：9/0  |  Submit date：2020/05/19
Evapotranspiration partitioning using an optimality-based ecohydrological model in a semiarid shrubland 期刊论文
INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2019, 卷号: 12, 期号: 12, 页码: 1423-1440
Authors:  Chen, Lajiao;  Su, Liying;  Liu, Weijiang;  Wang, Lizhe;  Wu, Hui;  Zhu, A-Xing;  Luo, Yiqi
Favorite  |  View/Download：9/0  |  Submit date：2020/05/19
ET partitioning  optimality-based ecohydrological model  VOM  semiarid shrubland  
A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods 期刊论文
CATENA, 2019, 卷号: 183, 页码: 17
Authors:  Zhu, A-Xing;  Miao, Yamin;  Liu, Junzhi;  Bai, Shibiao;  Zeng, Canying;  Ma, Tianwu;  Hong, Haoyuan
Favorite  |  View/Download：9/0  |  Submit date：2020/05/19
Landslide absence data  Sampling method  Data-driven methods  Landslide susceptibility mapping, similarity model, data mining and machine learning  
A modular and parallelized watershed modeling framework 期刊论文
ENVIRONMENTAL MODELLING & SOFTWARE, 2019, 卷号: 122, 页码: 16
Authors:  Zhu, Liang-Jun;  Liu, Junzhi;  Qin, Cheng-Zhi;  Zhu, A-Xing
Favorite  |  View/Download：10/0  |  Submit date：2020/05/19
Watershed modeling framework  Modular  Parallelization  Watershed process simulation  SEIMS