| A review of neural networks for air temperature forecasting TTK Tran, SM Bateni, SJ Ki, H Vosoughifar Water 13 (9), 1294, 2021 | 123 | 2021 |
| Deep learning-based maximum temperature forecasting assisted with meta-learning for hyperparameter optimization T Thi Kieu Tran, T Lee, JY Shin, JS Kim, M Kamruzzaman Atmosphere 11 (5), 487, 2020 | 75 | 2020 |
| Increasing neurons or deepening layers in forecasting maximum temperature time series? TTK Tran, T Lee, JS Kim Atmosphere 11 (10), 1072, 2020 | 42 | 2020 |
| Improving the prediction of wildfire susceptibility on Hawaiʻi Island, Hawaiʻi, using explainable hybrid machine learning models TTK Tran, S Janizadeh, SM Bateni, C Jun, D Kim, C Trauernicht, F Rezaie, ... Journal of Environmental Management 351, 119724, 2024 | 11 | 2024 |
| Enhancing predictive ability of optimized group method of data handling (GMDH) method for wildfire susceptibility mapping TTK Tran, SM Bateni, F Rezaie, M Panahi, C Jun, C Trauernicht, ... Agricultural and Forest Meteorology 339, 109587, 2023 | 9 | 2023 |
| Advancing the LightGBM approach with three novel nature-inspired optimizers for predicting wildfire susceptibility in Kauaʻi and Molokaʻi Islands, Hawaii S Janizadeh, TTK Tran, SM Bateni, C Jun, D Kim, C Trauernicht, E Heggy Expert Systems with Applications, 124963, 2024 | 2 | 2024 |
| Is Deep Better in Extreme Temperature Forecasting? TTK Tran, T Lee Journal of the Korean Society of Hazard Mitigation 19 (7), 55-62, 2019 | 2 | 2019 |
| Filling gaps in MODIS NDVI data using hybrid multiple imputation–Machine learning and DINCAE techniques: Case study of the State of Hawaii TTK Tran, SM Bateni, H Mohebzadeh, C Jun, M Pandey, D Kim Advances in Engineering Software 201, 103856, 2025 | | 2025 |
