＞ Kangwon National University Division of Science Education. 2019
- Application of Machine Learning Technique for Groundwater Potential Mapping
＞ Jenderal Soedirman University Geological Engineering, (Indonesia). 2016
- Remote sensing data for land cover classification in volcano area
- GIS modelling for landslide susceptibility
＞ E-mail : email@example.com
1) Kadavi, P.-R, Lee, C.-W, S.-R. Lee (2019): Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models, Environmental Earth Sciences, Vol. 78, NO. 116.
2) Syifa, M, Kadavi, P.-R, Lee, C.-W (2019): An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia, Sensors, Vol. 19, NO. 3, 542.
3) Kadirhodjaev, A.-Z., Kadavi, P.-R, Lee, C.-W & S.-R. Lee (2018): Analysis of the relationships between topographic factors and landslide occurrence and their application to landslide susceptibility mapping: a case study of Mingchukur, Uzbekistan, Geosciences Journal, Vol. 22, NO. 6, 1053–1067.
4) Oh, H.-J., Kadavi, P.-R, Lee, C.-W & S.-R. Lee (2018): Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models, Geomatics, Natural Hazards and Risk, Vol. 9, NO. 1, 1053–1070.
5) Park, S.-J., Kadavi, P.-R, C.-W. Lee*(2018): Landslide Susceptibility Apping and Comparison Using Probabilistic Models: A Case Study of Sacheon, Jumunzin Area, Korea , Korea Journal of Remote Sensing, Vol. 34, No. 5, 721-738.
6) Prima Riza, K. and Lee, C.-W and S.-R. Lee (2018): Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping, Remote Sensing, 10(8), 1252.
7) Prima Riza, K. and Lee, C.-W( 2018): Land cover classification analysis of volcanic island in Aleutian Arc using an Artificial Neural Network (ANN) and a Support Vector Machine (SVM) from Landsat imagery, Geosciences Journal, Vol. 22, Issue 4, pp 653–665.
8) Prima Riza, K., W.-J, Lee and Lee, C.-W (2017): Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach. Applied Sciences, Vol.7, No.9, 935-949.
Kangwon National University Earth Observation Laboratory
KNU Chuncheon Campus 1, Gangwondaehakgil, Chuncheon-si, 24341 Republic of Korea
Kangwon National University College of Education #4 - 301. TEL : +184.108.40.20623