J
Junzhi Liu
Researcher at Nanjing Normal University
Publications - 70
Citations - 3445
Junzhi Liu is an academic researcher from Nanjing Normal University. The author has contributed to research in topics: Landslide & Digital soil mapping. The author has an hindex of 22, co-authored 61 publications receiving 2071 citations. Previous affiliations of Junzhi Liu include Chinese Academy of Sciences & Max Planck Society.
Papers
More filters
Journal ArticleDOI
Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
Haoyuan Hong,Haoyuan Hong,Junzhi Liu,Junzhi Liu,Dieu Tien Bui,Biswajeet Pradhan,Biswajeet Pradhan,Tri Dev Acharya,Binh Thai Pham,A-Xing Zhu,A-Xing Zhu,Wei Chen,Baharin Bin Ahmad +12 more
TL;DR: Wang et al. as mentioned in this paper investigated and compared the use of current state-of-the-art ensemble techniques, such as AdaBoost, Bagging, and Rotation Forest, for landslide susceptibility assessment with the base classifier of J48 Decision Tree (JDT).
Journal ArticleDOI
Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.
Haoyuan Hong,Haoyuan Hong,Mahdi Panahi,Ataollah Shirzadi,Tianwu Ma,Tianwu Ma,Junzhi Liu,Junzhi Liu,A-Xing Zhu,A-Xing Zhu,Wei Chen,Ioannis Kougias,Nerantzis Kazakis +12 more
TL;DR: This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS and an adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling.
Journal ArticleDOI
Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.
TL;DR: A novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China is proposed by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods and the fuzzy WofE-SVM model was the model with the highest predictive performance.
Journal ArticleDOI
Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China.
Wei Chen,Jianbing Peng,Haoyuan Hong,Haoyuan Hong,Himan Shahabi,Biswajeet Pradhan,Junzhi Liu,A-Xing Zhu,A-Xing Zhu,Xiangjun Pei,Zhao Duan +10 more
TL;DR: Four advanced machine learning techniques, namely the Bayes' net (BN), radical basis function (RBF) classifier, logistic model tree (LMT), and random forest (RF) models, are compared for landslide susceptibility modelling in Chongren County, China to assess and compare the predictive capabilities of the models.
Journal ArticleDOI
Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach
Martin Jung,Christopher R. Schwalm,Mirco Migliavacca,Sophia Walther,Gustau Camps-Valls,Sujan Koirala,Peter Anthoni,Simon Besnard,Simon Besnard,Paul Bodesheim,Paul Bodesheim,Nuno Carvalhais,Nuno Carvalhais,Frédéric Chevallier,Fabian Gans,Daniel S. Goll,Vanessa Haverd,Philipp Köhler,Kazuhito Ichii,Atul K. Jain,Junzhi Liu,Junzhi Liu,Danica Lombardozzi,Julia E. M. S. Nabel,Jacob A. Nelson,Michael O'Sullivan,Martijn Pallandt,Dario Papale,Dario Papale,Wouter Peters,Julia Pongratz,Julia Pongratz,Christian Rödenbeck,Stephen Sitch,G. Tramontana,G. Tramontana,Anthony P. Walker,Ulrich Weber,Markus Reichstein +38 more
TL;DR: In this paper, the authors provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods and sets of predictor variables were employed.