Kaleem Ullah Jan Khan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Kaleem Ullah Jan Khan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Changming Wang
College of Construction Engineering, Jilin University, Changchun, China
1 shared publication - Hailiang Liu
College of Construction Engineering, Jilin University, Changchun 130012, China
1 shared publication - Xiaoyang Liu
College of Construction Engineering, Jilin University, Changchun 130026, China
1 shared publication - Zhijie Duan
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
1 shared publication - Zhu Liang
Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
1 shared publication
Latest Publications
- Open Access
- 10 April 2021
- Published by MDPI AG in Remote Sensing
- Vol. 13 (8) , 1464
A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping
Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.
- 10 April 2021
- Published by MDPI AG in Remote Sensing
- Vol. 13 (8) , 1464
Open AccessA Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping
Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.