| Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea S Lee, JC Kim, HS Jung, MJ Lee, S Lee Geomatics, Natural Hazards and Risk 8 (2), 1185-1203, 2017 | 315 | 2017 |
| Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea JC Kim, S Lee, HS Jung, S Lee Geocarto international 33 (9), 1000-1015, 2018 | 232 | 2018 |
| Groundwater potential mapping using remote sensing and GIS-based machine learning techniques S Lee, Y Hyun, S Lee, MJ Lee Remote Sensing 12 (7), 1200, 2020 | 109 | 2020 |
| Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment R Costache, QB Pham, M Avand, NTT Linh, M Vojtek, J Vojteková, S Lee, ... Journal of Environmental Management 265, 110485, 2020 | 89 | 2020 |
| Groundwater potential mapping using data mining models of big data analysis in Goyang-si, South Korea S Lee, Y Hyun, MJ Lee Sustainability 11 (6), 1678, 2019 | 77 | 2019 |
| Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools S Lee, S Lee, MJ Lee, HS Jung Sustainability 10 (3), 648, 2018 | 77 | 2018 |
| Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea S Lee, MJ Lee, HS Jung, S Lee Geocarto international 35 (15), 1665-1679, 2020 | 75 | 2020 |
| Data mining approaches for landslide susceptibility mapping in Umyeonsan, Seoul, South Korea S Lee, MJ Lee, HS Jung Applied Sciences 7 (7), 683, 2017 | 61 | 2017 |
| Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia M Vojtek, J Vojteková, R Costache, QB Pham, S Lee, A Arshad, S Sahoo, ... Geomatics, Natural Hazards and Risk 12 (1), 1153-1180, 2021 | 51 | 2021 |
| Susceptibility mapping on urban landslides using deep learning approaches in Mt. Umyeon S Lee, WK Baek, HS Jung, S Lee Applied Sciences 10 (22), 8189, 2020 | 37 | 2020 |
| Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees S Lee, MJ Lee, S Lee Environmental earth sciences 77, 1-22, 2018 | 24 | 2018 |
| Mapping forest vertical structure in Jeju Island from optical and radar satellite images using artificial neural network YS Lee, S Lee, WK Baek, HS Jung, SH Park, MJ Lee Remote Sensing 12 (5), 797, 2020 | 19 | 2020 |
| Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning S Lee, S Lee, W Song, MJ Lee Applied Sciences 7 (9), 912, 2017 | 18 | 2017 |
| Susceptibility mapping of Umyeonsan using logistic regression (LR) model and post-validation through field investigation S Lee, MJ Lee Korean Journal of Remote Sensing 33 (6_2), 1047-1060, 2017 | 13 | 2017 |
| Susceptibility analysis of the Mt. Umyeon landslide area using a physical slope model and probabilistic method S Lee, J Jang, Y Kim, N Cho, MJ Lee Remote Sensing 12 (16), 2663, 2020 | 11 | 2020 |
| Mapping forest vertical structure in Gong-Ju, Korea using Sentinel-2 satellite images and artificial neural networks YS Lee, S Lee, HS Jung Applied Sciences 10 (5), 1666, 2020 | 10 | 2020 |
| Detection of change in water system due to collapse of Laos Xe pian-Xe namnoy dam using KOMPSAT-5 satellites Y Kim, M Lee, S Lee Korean Journal of Remote Sensing 35 (6_4), 1417-1424, 2019 | 7 | 2019 |
| Mapping forest vertical structure in Sogwang-ri forest from full-waveform lidar point clouds using deep neural network SH Park, HS Jung, S Lee, ES Kim Remote Sensing 13 (18), 3736, 2021 | 6 | 2021 |
| Analysis of trends in marine water quality using environmental impact assessment monitoring data: A case study of Busan new port S Lee, E Lee, HS Yoo, MJ Lee Journal of Coastal Research 102 (SI), 39-46, 2020 | 5 | 2020 |
| Multi-temporal analysis of deforestation in Pyeongyang and Hyesan, North Korea S Lee, SH Park, HS Jung Korean Journal of Remote Sensing 32 (1), 1-11, 2016 | 4 | 2016 |
