C
Chinh Luu
Researcher at National University of Civil Engineering
Publications - 22
Citations - 807
Chinh Luu is an academic researcher from National University of Civil Engineering. The author has contributed to research in topics: Flood myth & Flood risk assessment. The author has an hindex of 10, co-authored 22 publications receiving 337 citations. Previous affiliations of Chinh Luu include University of Newcastle.
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Journal ArticleDOI
A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
Dong Van Dao,Abolfazl Jaafari,Mahmoud Bayat,Davood Mafi-Gholami,Chongchong Qi,Hossein Moayedi,Tran Van Phong,Hai-Bang Ly,Tien-Thinh Le,Phan Trong Trinh,Chinh Luu,Nguyen Kim Quoc,Bui Nhi Thanh,Binh Thai Pham +13 more
TL;DR: A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over the quadratic discriminant analysis, Fisher's linear discriminantAnalysis, and multi-layer perceptron neural network.
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Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
Viet-Ha Nhu,Ataollah Shirzadi,Himan Shahabi,Sushant K. Singh,Nadhir Al-Ansari,John J. Clague,Abolfazl Jaafari,Wei Chen,Shaghayegh Miraki,Jie Dou,Chinh Luu,Krzysztof Górski,Binh Thai Pham,Huu Duy Nguyen,Baharin Bin Ahmad +14 more
TL;DR: Comparisons of the power and effectiveness of five machine learning, benchmark algorithms in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran suggest the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions is recommended.
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Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam
TL;DR: In this paper, a case study of the 2013 flood event in Quang Nam, Vietnam is used to assess the criteria and sub-criteria of the flood hazard using flood mark data.
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Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam
Binh Thai Pham,Chinh Luu,Tran Van Phong,Huu Duy Nguyen,Hiep Van Le,Thai Quoc Tran,Huong Thu Ta,Indra Prakash +7 more
TL;DR: In this paper, two hybrid Artificial Intelligence (AI) models, namely ABMDT (AdaBoost-DT) and BDT (Bagging-DT), were developed with Decision Table (DT) as a base classifier for creating a flood susceptibility map.
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Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling
Binh Thai Pham,Chinh Luu,Tran Van Phong,Phan Trong Trinh,Ataollah Shirzadi,Somayeh Renoud,Shahrokh Asadi,Hiep Van Le,Jason von Meding,John J. Clague +9 more
TL;DR: Novel deep learning algorithms such as the one used in this study can improve the accuracy of flood susceptibility maps that are required by planners, decision makers, and government agencies to manage of areas vulnerable to flood-induced damage.