Results 131 to 140 of about 1,693,251 (168)

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results. [PDF]

Sci Rep
Yu X, Chen H.
europepmc   +1 more source

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. [PDF]

Int J Environ Res Public Health, 2020
Nhu VH, Shirzadi A, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, Jaafari A, Chen W, Miraki S, Dou J, Luu C, Górski K, Thai Pham B, Nguyen HD, Ahmad BB.   +15 more
europepmc   +1 more source

GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh. [PDF]

Heliyon
Chowdhury MS, Rahman MN, Sheikh MS, Sayeid MA, Mahmud KH, Hafsa B.   +5 more
europepmc   +1 more source

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. [PDF]

Sensors (Basel), 2018
Shirzadi A, Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, Chen W, Khosravi K, Thai Pham B, Pradhan B, Ahmad A, Bin Ahmad B, Tien Bui D.   +13 more
europepmc   +1 more source

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method. [PDF]

Environ Monit Assess, 2018
Arca D, Kutoğlu HŞ, Becek K.
europepmc   +1 more source

A heuristic approach to global landslide susceptibility mapping. [PDF]

Nat Hazards (Dordr), 2017
Stanley T, Kirschbaum DB.
europepmc   +1 more source

A Novel Performance Assessment Approach Using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest. [PDF]

Sensors (Basel), 2019
Sevgen E, Kocaman S, Nefeslioglu HA, Gokceoglu C.   +3 more
europepmc   +1 more source

The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China. [PDF]

Entropy (Basel), 2019
Ma Z, Qin S, Cao C, Lv J, Li G, Qiao S, Hu X.   +6 more
europepmc   +1 more source

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

Int J Environ Res Public Health, 2019
Wang Y, Wu X, Chen Z, Ren F, Feng L, Du Q.   +5 more
europepmc   +1 more source

Distinguishing compound and cumulative hazards using machine learning and fuzzy logic in multi-hazard susceptibility mapping. [PDF]

Sci Rep
Einali J, Gholamnia K, Einali G, Moghaddam HT, Monfared BA, Cheraghi M, Ghorbanzadeh O.   +6 more
europepmc   +1 more source