A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

2016 ◽  
Vol 84 ◽  
pp. 240-250 ◽  
Author(s):  
Binh Thai Pham ◽  
Biswajeet Pradhan ◽  
Dieu Tien Bui ◽  
Indra Prakash ◽  
M.B. Dholakia
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Learning Methods ◽  
Landslide Susceptibility Assessment ◽  
Machine Learning Methods

Geotechnical investigation and landslide susceptibility assessment along the Neelum road: a case study from Lesser Himalayas, Pakistan

2021 ◽  
Vol 14 (11) ◽  
Author(s):  
Khawaja Shoaib Ahmed ◽  
Muhammad Basharat ◽  
Muhammad Tayyib Riaz ◽  
Yasir Sarfraz ◽  
Amir Shahzad
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Geotechnical Investigation ◽  
Landslide Susceptibility Assessment ◽  
Lesser Himalayas

Machine learning methods for cyber security intrusion detection: Datasets and comparative study

2021 ◽  
Vol 188 ◽  
pp. 107840
Author(s):  
Ilhan Firat Kilincer ◽  
Fatih Ertam ◽  
Abdulkadir Sengur
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Cyber Security ◽  
Learning Methods ◽  
Machine Learning Methods

Landslide susceptibility mapping based on convolutional neural network and conventional machine learning methods

2021 ◽  
Author(s):  
Rui Liu ◽  
Xin Yang ◽  
Chong Xu ◽  
Luyao Li ◽  
Xiangqiang Zeng
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Conventional Machine

Abstract Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced Convolutional Neural Network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected the Jiuzhaigou region in Sichuan Province, China as the study area. A total number of 710 landslides and 12 predisposing factors were stacked to form spatial datasets for LSM. The ROC analysis and several statistical metrics, such as accuracy, root mean square error (RMSE), Kappa coefficient, sensitivity, and specificity were used to evaluate the performance of the models in the training and validation datasets. Finally, the trained models were calculated and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine-learning based models have a satisfactory performance (AUC: 85.72% − 90.17%). The CNN based model exhibits excellent good-of-fit and prediction capability, and achieves the highest performance (AUC: 90.17%) but also significantly reduces the salt-of-pepper effect, which indicates its great potential of application to LSM.

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