An Innovative Flood Mapping Approach Using PolSAR Decompositions from C-band Dual- and Quad-Polarization SAR Data, Feature Selection,and Deep Learning, Submitted to Remote Sensing of Environment
Integrating Sentinel-1 C-band SAR and machine learning for water body detection and water level estimation over Yeongsan River Basin, South Korea, Submitted to Journal of Hydrology
A novel heterogeneous weighting strategy for leveraging cross-basin data in deep learning models for hydrological prediction, Submitted to AGU Advances
An extension of the BROOK90 hydrological model for estimation of subdaily water and energy fluxes, In Review, Geoscientific Model Development
Advanced Modeling Approach for Global-Scale Analysis of Nutrient-Chlorophyll Relationships Across Lake Depths and Trophic States, Submitted to Environmental Science and Technology.
Comparative Assessment of Recurrent Neural Network Models Family in Flood Susceptibility Mapping: An Explainable Glass-Box Approach, Submitted to Catena
The Reliability–Resilience and Vulnerability of Talar Watershed in Iran, assessed using Remote Sensing Data, Submitted to Environmental Monitoring and Assessment
Physics-informed deep-learning model for mitigating spatiotemporal imbalances in FLUXNET2015 global evapotranspiration data, Submitted to Nature Water
River Total Dissolved Gas Modeling Using Advanced Hybridized Greedy-Stepwise and Bidirectional Long Short-Term Memory Deep Learning Algorithms: A New Methodology For Poor Data Regions. Submitted to Ecological Informatics
Rediscovering the climate analogue method: insights from a data-driven approach, Submitted to Hydrology Earth System Sciences
Unveiling Groundwater Quality Vulnerability through Spatially Explicit Assessment , Submitted to Environmental and Sustainability Indicators
Iran’s Ramsar Convention Wetlands Are Shrinking at Alarming Rates, submitted to Journal of Environmental Management
Remote Sensing Data Based Assessment of Reliability–Resilience and Vulnerability of Talar Watershed in Iran, Submitted to Journal of Flood Risk Management
Forecasting Daily Reference Evapotranspiration in Different Hydrological Conditions Through Hybrid Wavelet–Bayesian Optimization–Gaussian Process Regression, Sumbitted to Engineering Applications of Artificial Intelligence
Exploring the relationship between flood resilience and risk management, Submitted to Environmental Science and Pollution Research.
Multi-Scale Groundwater-based Drought Prediction Model Integrating Deep Learning and Hydrometeorological Factors, Submitted to Journal of Environmental Management
Mapping Flood Susceptibility on a Global Scale: A Future Perspective on Climate and Land-Use Change Impacts, Submitted to Nature Communications
Advancing the LightGBM Approach with Three Novel Nature-Inspired Optimizers for Predicting Wildfire Susceptibility in Kauaʻi and Molokaʻi Islands, Hawaii, Submitted to Expert Systems With Applications
Generation of High-resolution Flood Susceptibility Map with Integration of Boosting-based Ensemble Algorithms and Multi-temporal Synthetic Aperture Radar and Optical Images, Submitted to Geoscientific Frontiers.
106. Simulation of future sub-hourly rainfall using the Bartlett-Lewis based rainfall model: Application for entire area of Germany and South Korea, Accepted, SERRA.
105. A novel approach of mapping snow disaster-prone areas based on areal disaster density optimization: a case study of South Korea,, Accepted, Natural Hazards
104. Nasiri Khiavi, A., Vafakhah, M., Kim, D., Jun, C., & Bateni, S. M. (2025). Integrating Remote Sensing, Machine Learning, and Local Knowledge for Innovative Flood Susceptibility and Vulnerability Mapping. Journal of Flood Risk Management, 18(4), e70149. https://onlinelibrary.wiley.com/doi/full/10.1111/jfr3.70149
103. Chi Vuong Tai, Dongkyun Kim*, Christian Onof, Li-Pen Wang, Jeongha Park, Accurate reproduction of sub-hourly rainfall extremes in Poisson cluster rainfall models with a variable sinusoidal pulse, Journal of Hydrology, 2025, 134821, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2025.134821
102. Wang, J., Xu, T., Liu, S., Kim, D., Jun, C., Bateni, S. M., ... & Ming, W. (2025). Estimation and mechanism analysis of global evapotranspiration based on a physics-informed deep-learning model. Journal of Hydrology, 134351. https://doi.org/10.1016/j.jhydrol.2025.134351
101. Choi, H., Kim, Y., Kim, S., & Kim, D.* (2025). Unveiling the role of weighted loss functions in deep learning‑based nowcasting of extreme rainfall events. IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/TGRS.2025.3592853
100. Lee, J., Janizadeh, S., Melancon, A., Bateni, S. M., Kim, D., Molthan, A., Jun, C., Farhadiani, R., & Dinka, M. (2025). Flood detection using PolSAR decomposition, feature selection, and deep learning. Gondwana Research, https://doi.org/10.1016/j.gr.2025.06.022
99. Kim, H., Kim, T. S., Chen, S., Kim, D., & Kim, T. W. (2024). Hydrological Risk Assessment of Drought in South Korea According to Climate Change Scenarios Using a Multiple Drought Index Integrated With a Dynamic Naive Bayesian Classifier. KSCE Journal of Civil Engineering, 100118. https://doi.org/10.1016/j.kscej.2024.100118
98. Lee, J., Chung, E. S., Kim, S., & Kim, D.* (2025). Streamflow forecasting in ungauged basins with CNN-LSTM and radar-based precipitation. Journal of Hydro-environment Research, 100666. https://doi.org/10.1016/j.jher.2025.100666
97. Ahmadi, M., Mohajeri, S. H., Jun, C., Kim, D., Ezati, S., Bateni, S. M., & Azizi, S. (2025). Satellite-Driven Machine Learning Framework for Monitoring River Sediment Discharge: Integrating Sentinel-2 Spectral Data with Hydrometric Insights. Advances in Space Research, https://doi.org/10.1016/j.asr.2025.04.061
96. Mozafari, Z., Noori, R., Bateni, S. M., Jun, C., Kim, D., Saravani, M. J., ... & Abolfathi, S. (2025). Impact of climatic factors on eutrophication in the World’s largest lake. Ecological Indicators, 175, 113497, https://doi.org/10.1016/j.ecolind.2025.113497
95. Jun, C., Kim, D., Bateni, S. M., Biyari, M., Salwana, E., Sajedi Hosseini, F., … Choubin, B. (2025). Aquifer vulnerability assessment in data-scarce areas: a spatially explicit assessment. Geomatics, Natural Hazards and Risk, 16(1). https://doi.org/10.1080/19475705.2025.2487816
94. Tai, C. V., Kim, D.*, Kronenberg, R., Vorobevskii, I., & Luong, T. T. (2025). Beneath the surface: Exploring relationship between pluvial floods and income disparities for residential basements in Seoul, South Korea. International Journal of Disaster Risk Reduction, 105501. , https://doi.org/10.1016/j.ijdrr.2025.105501.
93. Khosravi, K., Mosallanejad, A., Bateni, S. M., Kim, D., Jun, C., Shahvaran, A. R., ... & Ali, M. (2025). Assessing Pan-Canada wildfire susceptibility by integrating satellite data with novel hybrid deep learning and black widow optimizer algorithms. Science of The Total Environment, 977, 179369. https://doi.org/10.1016/j.scitotenv.2025.179369.
92. De Luca, D. L., Napolitano, F., Kim, D., Onof, C., Biondi, D., Wang, L. P., ... & Marconi, F. (2025). Rainfall nowcasting models: state of the art and possible future perspectives. Hydrological Sciences Journal, https://doi.org/10.1080/02626667.2025.2490780
91. Bordbar, M., Khosravi, K., Jun, C., Kim, D., Bateni, S. M., Safarzadeh, M., ... & Azizi, S. (2025). Improving aquifer vulnerability assessment and its explainability in the Zanjan aquifer: integrating DRASTIC model and optimized long short-term memory-based metaheuristic algorithms. Results in Engineering, 104674. https://doi.org/10.1016/j.rineng.2025.104674
90. Naderian, D., Noori, R., Bateni, S. M., Jun, C., Kim, D., Shahmohammad, M., ... & Woolway, R. I. (2025). Pivotal role of snow depth, local atmospheric conditions, and large-scale climate signals on ice thinning in Finnish lakes. Science of The Total Environment, 966, 178715. https://doi.org/10.1016/j.scitotenv.2025.178715
89. Naderian, D., Noori, R., Kim, D., Jun, C., Bateni, S. M., Woolway, R. I., ... & Maberly, S. C. (2025). Environmental controls on the conversion of nutrients to chlorophyll in lakes. Water Research, 274, 123094. https://doi.org/10.1016/j.watres.2025.123094
88. Saravani, M. J., Noori, R., Jun, C., Kim, D., Bateni, S. M., Kianmehr, P., & Woolway, R. I. (1801). Predicting chlorophyll-a concentrations in the world’s largest lakes using Kolmogorov-Arnold networks. Environmental Science & Technology. https://pubs.acs.org/doi/10.1021/acs.est.4c11113
87. Jeong, Y., Kim, D., & Byun, K. (2025). A novel deep learning-based approach for reconstruction of historical long-term high-quality gridded meteorological dataset. Journal of Hydrology, 132850. https://doi.org/10.1016/j.jhydrol.2025.132850
86. Khosravi, K. et al., Multistep hourly based significant wave height forecasting using reduced error pruning tree reinforced with weighted instances handler wrapper algorithms: A case study in Queensland’s wave energy hub, Heliyon, 2025, e42798, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2025.e42798.
85. Rezaie, F., Panahi, M., Jun, C. et al. Deep learning models for drought susceptibility mapping in Southeast Queensland, Australia. Stoch Environ Res Risk Assess (2025). https://doi.org/10.1007/s00477-024-02879-w
84. Morbidelli, R., Flammini, A., Echeta, O., Albano, R., Anzolin, G., Zumr, D., ... & Saltalippi, C. (2025). A reassessment of the history of the temporal resolution of rainfall data at the global scale. Journal of Hydrology, 654, 132841. https://doi.org/10.1016/j.jhydrol.2025.132841
83. Kim, S., Park, J., Chung, G. and Kim, D.* (2025) High-resolution snow depth modeling in South Korea using radar-based precipitation data. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-024-02902-0
82. Shiru, M. S., Kim, D., & Chung, E. S. (2024). Evaluating CMIP6 Global Climate Models Performances Over Nigeria: An Integrated Approach. International Journal of Climatology. https://doi.org/10.1002/joc.8739
81. Lian, T., Rieckermann, J., Kim, D*., & Cook, L. M. (2025). Context-Specific Selection of Poisson cluster rainfall models for accurate urban hydrological simulations. Journal of Hydrology, 132637. https://doi.org/10.1016/j.jhydrol.2024.132637
80. Kim, H., Kim, T. S., Chen, S., Kim, D., & Kim, T. W. (2024). Hydrological Risk Assessment of Drought in South Korea According to Climate Change Scenarios Using a Multiple Drought Index Integrated With a Dynamic Naive Bayesian Classifier. KSCE Journal of Civil Engineering, 100118.
79. Tran, T. T. K., Bateni, S., Mohebzadeh, H., Jun, C., Pandey, M., Kim, D., Filling gaps in MODIS NDVI data using hybrid multiple imputation–Machine learning and DINCAE techniques: Case study of the State of Hawaii, Advances in Engineering Software, Volume 201, 2025, 103856, ISSN 0965-9978, https://doi.org/10.1016/j.advengsoft.2024.103856
78. Jungwon Lee, Seungjun Ahn, Daeho Kim, Dongkyun Kim, Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval, Automation in Construction, Volume 168, Part B, 2024, 105846, ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2024.105846.
77. Jun, C., Kim, D., Bateni, S. M., Qasem, S. N., Mansor, Z., Band, S. S., ... & Pai, H. T. (2025). Prediction of earth-fissure hazards: Unraveling the crucial roles of land use and groundwater fluctuations. Environmental Impact Assessment Review, 110, 107692, https://doi.org/10.1016/j.eiar.2024.107692
76. Khabat Khosravi, Nasrin Attar, Sayed M. Bateni, Changhyun Jun, Dongkyun Kim, Mir Jafar Sadegh Safari, Salim Heddam, Aitazaz Farooque, Soroush Abolfathi, Daily River flow Simulation Using Ensemble Disjoint Aggregating M5-Prime Model, Heliyon, 2024, e37965, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2024.e37965
75. Kim, H.I., Kim, D*., Salamattalab, M.M. et al. Machine learning-based modeling of surface water temperature dynamics in arctic lakes. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-35173-x
74. Tai, C. V., Chung, E. S., & Kim, D.* (2024). A socio-economic vulnerability assessment framework against natural disasters: A case study in Seoul, South Korea. Urban Climate, 58, 102139. https://doi.org/10.1016/j.uclim.2024.102139
73. Chae, Seung Taek, Eun-Sung Chung, and Dongkyun Kim. "Evaluation of Optimized Multi-Model Ensembles for Extreme Precipitation Projection Considering Various Objective Functions." Water Resources Management (2024): 1-19. https://link.springer.com/article/10.1007/s11269-024-03948-z
72. Gangqiang Zhang, Tongren Xu, Wenjie Yin, Sayed M. Bateni, Changhyun Jun, Dongkyun Kim, Shaomin Liu, Ziwei Xu, Wenting Ming, Jiancheng Wang, A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly, Remote Sensing of Environment, Volume 313, 2024, 114359, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2024.114359
71. Soohyun Kim, Yongchan Kim, Mohammad Ali Ghorbani, Dongkyun Kim*, Analysis on the temporal scaling behavior of extreme rainfall in Korean Peninsula based on high-resolution radar-based precipitation data, Journal of Hydrology: Regional Studies, Volume 55, 2024, 101915, ISSN 2214-5818, https://doi.org/10.1016/j.ejrh.2024.101915
70. Saeid Janizadeh, Dongkyun Kim*, Changhyun Jun, Sayed M. Bateni, Manish Pandey, Varun Narayan Mishra, Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms, Journal of Environmental Management, Volume 366, 2024, 121764, ISSN 0301-4797, https://doi.org/10.1016/j.jenvman.2024.121764
69. He, X., Liu, S., Bateni, S. M., Xu, T., Jun, C., Kim, D., ... & Wei, J. (2024). Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations. Agricultural and Forest Meteorology, 355, 110136. https://doi.org/10.1016/j.agrformet.2024.110136
68.Jinwook Lee, Sayed M. Bateni, Changhyun Jun, Essam Heggy, Mehdi Jamei, Dongkyun Kim, Hamid Reza Ghafouri, Jonathan L. Deenik, Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting, Engineering Applications of Artificial Intelligence, Volume 135, 2024, 108744, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2024.108744.
67. Hyung Il Kim, Dongkyun Kim*, Mehran Mahdian, Mohammad Milad Salamattalab, Sayed M. Bateni, Roohollah Noori, Incorporation of Water Quality Index Models with Machine Learning-Based Techniques for Real-Time Assessment of Aquatic Ecosystems, Environmental Pollution, 2024, 124242, ISSN 0269-7491, https://doi.org/10.1016/j.envpol.2024.124242.
66. Bordbar, M., Heggy, E., Jun, C. et al. Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-32706-2
65. Y. Kim, D. Kim*, J. Park and C. Jun, "An Effective Algorithm of Outlier Correction in Space-time Radar Rainfall Data Based on the Iterative Localized Analysis," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2024.3366400. https://ieeexplore.ieee.org/document/10436656
64. Vorobevskii, I., Park, J., Kim, D., Barfus, K., and Kronenberg, R.: Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models: case studies from Germany and South Korea, Hydrol. Earth Syst. Sci., 28, 391–416, https://doi.org/10.5194/hess-28-391-2024, 2024
63. Jeon, J., Kim, Y., Kim, D. et al. Flume Experiments for Flow around Debris Accumulation at a Bridge. KSCE J Civ Eng (2024). https://doi.org/10.1007/s12205-024-1442-4
62.Bordbar, M., Rezaie, F., Bateni, S.M. et al. Global Review of Modification, Optimization, and Improvement Models for Aquifer Vulnerability Assessment in the Era of Climate Change. Curr Clim Change Rep (2024). https://doi.org/10.1007/s40641-023-00192-2
61. Vorobevskii, I., Park, J., Kim, D., Barfus, K., & Kronenberg, R. (2023). Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models–case studies from Germany and South Korea. Hydrology and Earth System Sciences Discussions, 2023, 1-36.
60. Trang Thi Kieu Tran, Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, Dongkyun Kim, Clay Trauernicht, Fatemeh Rezaie, Thomas W. Giambelluca, Mahdi Panahi, Improving the prediction of wildfire susceptibility on Hawaiʻi Island, Hawaiʻi, using explainable hybrid machine learning models, Journal of Environmental Management, Volume 351, 2024, https://doi.org/10.1016/j.jenvman.2023.119724
59. Song, Y. H., Chung, E. S., Shahid, S., Kim, Y., & Kim, D. (2023). Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data, 10(1), 568, https://www.nature.com/articles/s41597-023-02475-7
58. Kim, D., Lee, Y. O., Jun, C., & Kang, S. (2023). Understanding the Way Machines Simulate Hydrological Processes-A Case Study of Predicting Fine-scale Watershed Response on a Distributed Framework. IEEE Transactions on Geoscience and Remote Sensing, https://ieeexplore.ieee.org/abstract/document/10153688
57. Kim, Y., Chung, E. S., Cho, H., Byun, K., & Kim, D.* (2023). The future water vulnerability assessment of the Seoul metropolitan area using a hybrid framework composed of physically-based and deep-learning-based hydrologic models. Stochastic Environmental Research and Risk Assessment, 1-22. https://link.springer.com/article/10.1007/s00477-022-02366-0
56. Peiman Parisouj, Hadi Mohammadzadeh Khani, Md Feroz Islam, Changhyun Jun, Sayed M. Bateni, and Dongkyun Kim AI-Based Runoff Simulation Based on Remote Sensing Observations: A Case Study of Two River Basins in the United States and Canada, Journal of American Water Resources Association, https://onlinelibrary.wiley.com/doi/10.1111/1752-1688.13098
55. Park, J., & Kim, D.* (2022). A Stochastic Approach to Simulate Realistic Continuous Snow Depth Time Series. Journal of Hydrology, 128980. https://www.sciencedirect.com/science/article/abs/pii/S0022169422015505
54. Dao, D.A.., Haiha, T. D., Kim, D.*, Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D-2D drainage model simulation, Stochastic Environmental Research and Risk Assessment, 2021(6), https://link.springer.com/article/10.1007/s00477-021-02049-2
53. Idrees, B. M., Lee, J.-Y., Kim, D., Kim., T.-W., Complementary Modeling Approach for Estimating Sedimentation and Hydraulic Flushing Parameters Using Artificial Neural Networks and RESCON2 Model, KSCE Journal of Civil Engineering, 2021(6), http://link.springer.com/article/10.1007/s12205-021-1877-9
52. Park, J., Cross, D., Onof, C., Chen, Y., Kim, D.* (2021) A simple scheme to adjust Poisson cluster rectangular pulse rainfall models for improved performance at sub-hourly timescales, Journal of Hydrology, https://www.sciencedirect.com/science/article/pii/S0022169421003437
51. Jehanzaib, M., Idrees, M. B., Kim, D., and Kim, T. (2021), Comprehensive Evaluation of Machine Learning Techniques for Hydrological Drought Forecasting, Journal of Irrigation and Drainage Engineering, https://ascelibrary.org/doi/full/10.1061/%28ASCE%29IR.1943-4774.0001575
50. Poschmann, J., Kim, D., Kronenberg, R., and Bernhofer, C. (2021) An analysis on temporal scaling behavior of extreme rainfall of Germany based on radar precipitation QPE data, Natural Hazards and Earth System Sciences, https://nhess.copernicus.org/articles/21/1195/2021/nhess-21-1195-2021.html
49. Lee S, Park J, Choi E, Kim D*. Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry. Remote Sensing. 2021; 13(4):828. https://doi.org/10.3390/rs13040828
48. Idrees, M., Jehanzaib, M., Kim, D., Kim, T., Comprehensive Evaluation of Machine Learning Models for Suspended Sediment Load Inflow Prediction in a Reservoir. Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-021-01982-6
47. Chen, Y., Paschalis, A., Kendon, E., Kim, D., & Onof, C. Changing Spatial Structure of Summer Heavy Rainfall, Using Convection‐Permitting Ensemble. Geophysical Research Letters. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL090903
46. Han, J., Olivera, F., & Kim, D.* (2020). An Algorithm of Spatial Composition of Hourly Rainfall Fields for Improved High Rainfall Value Estimation. KSCE Journal of Civil Engineering, 1-13. https://link.springer.com/article/10.1007/s12205-020-0526-z
45. Dao, D. A., Kim, D.*, Park, J., & Kim, T. (2020). Precipitation threshold for urban flood warning-an analysis using the satellite-based flooded area and radar-gauge composite rainfall data. Journal of Hydro-environment Research, 32, 48-61. , https://doi.org/10.1016/j.jher.2020.08.001
44. Kim, D.*, and Onof, C., (2020) A stochastic rainfall model that can reproduce important rainfall properties across the timescales from several minutes to a decade, Journal of Hydrology, 589(10), https://doi.org/10.1016/j.jhydrol.2020.125150
43. Dao, D.A.., Kim, D*, Kim, S., and Park, J. (2020) Determination of flood-inducing rainfall and runoff for highly urbanized area based on high-resolution radar-gauge composite rainfall data and flooded area GIS Data, Journal of Hydrology, 584(5), https://doi.org/10.1016/j.jhydrol.2020.124704
42. Kim, D.*, Choi, M., Kim J., and Kim U. (2019). Editorial: Advances in Remote Sensing to Understand Extreme Hydrological Events. Advances in Meteorology, https://www.hindawi.com/journals/amete/2019/8235037/
41. Ahmad, W., & Kim, D.* (2019). Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea. International Journal of Applied Earth Observation and Geoinformation, 83, 101930. https://www.sciencedirect.com/science/article/pii/S0303243419300509
40. Mohammadzadeh, B., Choi, E., and Kim, D. Vibration of sandwich plates considering elastic foundation, temperature change and FGM faces, Structural Engineering and Mechanics, Vol. 70, No. 4 (2019), DOI: https://doi.org/10.12989/sem.2019.70.4.000
39. Shim, K., Abdellatif, M., Kim, D.*, and Park, J., Antifouling effect of water-soluble phosphate glass frit for filtration plants, Folia Microbiologica, https://link.springer.com/article/10.1007/s12223-019-00743-x
38. Ahmad, W., Kim, D.*, Choi, M., and Kim, S., Detection of Land Subsidence and Its Relationship with Land Cover Types Using ESA Sentinel Satellites Data: A Case Study of Quetta Valley, Pakistan, International Journal of Remote Sensing, https://doi.org/10.1080/01431161.2019.1633704
37. Sur, C., Kim, D., Lee J., Iqbal, M., and Choi, M., Hydrological Drought Assessment of Energy-based Water Deficit Index (EWDI) at different geographical regions, Advances in Meteorology, https://doi.org/10.1155/2019/8512727
36. Cho H, Park J, Kim D. Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL. Water. 2019; 11(3):447. https://www.mdpi.com/2073-4441/11/3/447
35. Park, J., Onof, C., and Kim, D*., A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales, Hydrol. Earth Syst. Sci., 23, 989-1014, https://doi.org/10.5194/hess-23-989-2019, 2019.
34. Lee, G., Kim, D.*, Kwon, H., and Choi, E. (2019). Estimation of Maximum Daily Fresh Snow Accumulation Using an Artificial Neural Network Model, Advances in Meteorology, https://doi.org/10.1155/2019/2709351
33. Kim, J., Lee, J., Kim, D.*, & Kang, B. (2019). The role of rainfall spatial variability in estimating areal reduction factors. Journal of Hydrology, 568(1) 416-426
32. Park, J., Onof, C., and Kim, D.* (2018). A Hybrid Stochastic Rainfall Model That Reproduces Rainfall Characteristics at Hourly through Yearly Time Scale, Hydrology Earth System Sciences Discussions , May, 2019
31. Kim, D.*, Kwon, H., Giustolisi, O., & Savic, D. (2018). Current water challenges require holistic and global solutions. Journal of Hydroinformatics, 20(3), 533-534.
30. Briaud, J. L., Jung, I., Govindasamy, A., Kim, D., & Lee, J. (2018). The Observation Method for Bridge Scour: Case Histories. ISSMGE International Journal of Geoengineering Case Histories, 4(3), 185-202.
29. Chen, S., Park, D. H., Kim, D., & Kim, T. W. (2017). An integrated hidden Markov model with an adaptive exponential weighting scheme for forecasting a meteorological drought index. Journal of Hydrology (New Zealand), 56(2), 69.
28. Choi, E., Mohammadzadeh, B., Kim, D., & Jeon, J. S. (2018). A new experimental investigation into the effects of reinforcing mortar beams with superelastic SMA fibers on controlling and closing cracks. Composites Part B: Engineering, 137, 140-152.
27. Ahmad, W., Choi, M., Kim, S., & Kim, D. (2017). Detection of land subsidence due to excessive groundwater use varying with different land cover types in quetta valley pakistan using esa sentinel satellite data. Nat. Hazards Earth Syst. Sci. Discuss.
26. Nguyen, H. H., Tran, H., Sunwoo, W., Yi, J. H., Kim, D., & Choi, M. (2017). Integrated change detection and temporal trajectory analysis of coastal wetlands using high spatial resolution Korean Multi-Purpose Satellite series imagery. Journal of Applied Remote Sensing, 11(2), 026030.
25. Shim, K., Abdellatif, M., Choi, E., & Kim, D.* (2017). Nanostructured ZnO films on stainless steel are highly safe and effective for antimicrobial applications. Applied microbiology and biotechnology, 101(7), 2801-2809.
24. Kim, D.*, Cho, H., Onof, C., & Choi, M. (2017). Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling. Stochastic Environmental Research and Risk Assessment, 31(4), 1023-1043.
23. Cho, W., Lee, J., Park, J., & Kim, D.* (2017). Radar polygon method: an areal rainfall estimation based on radar rainfall imageries. Stochastic environmental research and risk assessment, 31(1), 275-289.
22. Kim, J. G., Kwon, H. H., & Kim, D. (2017). A hierarchical Bayesian approach to the modified Bartlett-Lewis rectangular pulse model for a joint estimation of model parameters across stations. Journal of Hydrology, 544, 210-223.
21. Kim, D., Lee, J., Kim, H., & Choi, M. (2016). Spatial composition of AMSR2 soil moisture products by conditional merging technique with ground soil moisture data. Stochastic environmental research and risk assessment, 30(8), 2109-2126.
20. Choi, E., Kim, D. J., Nam, T. H., Kim, D., & Kang, J. W. (2016). Bond Resistance of L-Shaped Shape Memory Alloy Fibers in Mortar. Journal of Nanoscience and Nanotechnology, 16(11), 11500-11504.
19. Kim, D.*, Kwon, H. H., Lee, S. O., & Kim, S. (2016). Regionalization of the Modified Bartlett–Lewis rectangular pulse stochastic rainfall model across the Korean Peninsula. Journal of Hydro-environment Research, 11, 123-137.
18. Kim, D. H., Rao, P. S. C., Kim, D., & Park, J. (2016). 1/f noise analyses of urbanization effects on streamflow characteristics. Hydrological Processes, 30(11), 1651-1664.
17. Lee, J., Ahn, J., Choi, E., & Kim, D.* (2016). Mesoscale spatial variability of linear trend of precipitation statistics in Korean Peninsula. Advances in Meteorology, 2016.
16. Yoo, J., Kim, D., Kim, H., & Kim, T. W. (2016). Application of copula functions to construct confidence intervals of bivariate drought frequency curve. Journal of Hydro-environment Research, 11, 113-122.
15. So, B. J., Kwon, H. H., Kim, D., & Lee, S. O. (2015). Modeling of daily rainfall sequence and extremes based on a semiparametric Pareto tail approach at multiple locations. Journal of Hydrology, 529, 1442-1450.
14. Choi, E., Kim, D., & Park, K. (2014). Effect of confining pressure due to external jacket of steel plate or shape memory alloy wire on bond behavior between concrete and steel reinforcing bars. Journal of nanoscience and nanotechnology, 14(12), 9657-9661.
13. Kim, D.*, Kim, J., & Cho, Y. S. (2014). A poisson cluster stochastic rainfall generator that accounts for the interannual variability of rainfall statistics: validation at various geographic locations across the united states. Journal of Applied Mathematics, 2014.
12. Jung, Y., Kim, D., Kim, D., Kim, M., & Lee, S. O. (2014). Simplified flood inundation mapping based on flood elevation-discharge rating curves using satellite images in gauged watersheds. Water, 6(5), 1280-1299.
11. Kim, D., Park, T. S., Park, J., & Lee, S. O. (2014). A river environment index for Korean national rivers: rationale, methods and application. Water Policy, 16(3), 481-500.
10. Kim, D., Kim, B. J., Lee, S. O., & Cho, Y. S. (2014). Best-fit distribution and log-normality for tsunami heights along coastal lines. Stochastic environmental research and risk assessment, 28(4), 881-893.
9. Kim, D., Olivera, F., Cho, H., & Lee, S. O. (2013). Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 2. Impact on watershed response variables. Stochastic environmental research and risk assessment, 27(7), 1611-1619.
8. Kim, D.*, Olivera, F., & Cho, H. (2013). Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 1. Impact on peak and extreme rainfall values. Stochastic environmental research and risk assessment, 27(7), 1601-1610.
7. Kim, D.*, Olivera, F., Cho, H., & Socolofsky, S. A. (2013). Regionalization of the Modified Bartlett-Lewis Rectangular Pulse Stochastic Rainfall Model. Terrestrial, Atmospheric & Oceanic Sciences, 24(3).
6. Govindasamy, A. V., Briaud, J. L., Kim, D., Olivera, F., Gardoni, P., & Delphia, J. (2012). Observation method for estimating future scour depth at existing bridges. Journal of Geotechnical and Geoenvironmental Engineering, 139(7), 1165-1175.
5. Cho, Y. S., Kim, Y. C., & Kim, D.* (2013). On the spatial pattern of the distribution of the tsunami run-up heights. Stochastic environmental research and risk assessment, 27(6), 1333-1346.
4. Kim, D.*, & Olivera, F. (2011). Relative importance of the different rainfall statistics in the calibration of stochastic rainfall generation models. Journal of Hydrologic Engineering, 17(3), 368-376.
3. Cho, H., Kim, D., Olivera, F., & Guikema, S. D. (2011). Enhanced speciation in particle swarm optimization for multi-modal problems. European Journal of Operational Research, 213(1), 15-23.
2. Bernhardt, M., Briaud, J. L., Kim, D., Leclair, M., Storesund, R., Lim, S. G., ... & Rogers, J. D. (2011). Mississippi river levee failures: June 2008 flood. ISSMGE International Journal of Geoengineering Case Histories, 2(2), 127-162.
1. Olivera, F., Choi, J., Kim, D., & Li, M. H. (2008). Estimation of average rainfall areal reduction factors in Texas using NEXRAD data. Journal of Hydrologic Engineering, 13(6), 438-448.
2024
29. 이건학, 손덕주, 김동균, 김대현. 하천생물지형학적 되먹임 작용 연구의 난점. 한국지형학회지 제, 31(1).
2022
28. 김용찬, 김영란, 황성환, 김동균*, 물리기반 분포형 수문 모형과 딥러닝 기반 LSTM 모형을 활용한 충주댐 및 소양강댐 유역의 미래 수자원 전망, 한국수자원학회 논문집, Vol. 55(12):1115-1124
27. 김수현, 김동균*, 평면최단거리를 활용한 Sentinel-1 영상의 중소규모 하천 수체 추출, 방재학회 논문집, 2022;22(6):363-376.
2021
26. 김동균, 강석구, 강수-일유출량 추정 LSTM 모형의 구축을 위한 자료 수집 방안, 한국수자원학회 논문집, Vol. 54, No. 10 (2021-10) P.795
25. 심규대, 김창용, 정준연, 김동균*, 해수담수 공정의 전력 평가기준에 관한 연구, 대한토목학회 논문집.
2019
24. 김수현, 이상구, 김태웅, 김동균*, 인공위성영상과 지형자료를 동시에 활용한 침수지역 판별, 방재학회논문집, Vol 19(7): 471-483
23. 박정하, Dao Duc Anh, 김수현, 김동균*, Sentinel-1 위성영상을 활용한 침수지역판별 및 적용성평가, 한국방재학회논문집, Vol. 19, No. 6:53-61
22. Dao Duc Anh, 김수현, 김태웅, 김동균*, Influence of Rain Gauge Density and Temporal Resolution on the Performance of Conditional Merging method, 한국방재학회논문집, Vo. 19, No.6:41-51
21. 이진영, 김동균, 박경운, 김태웅,정량적 평가 지표를 활용한 호우피해 예측지도의 정확도 판단기준 설정, 대한토목학회논문집, Vol. 39, No. 3: 381-389/ June, 2019
2018
20. 심규대, 강용석, 정준연, Mohamed Abdellatif, 김동균, 오리피스를 고려한 송수관로 수충격 완화방법에 대한 연구, 한국방재학회논문집, 2018 18(7)
19. 서민지, 김동균, 아매드 와카스, 차준호, 합성개구레이더 인공위성 영상을 활용한 중소규모 하천에서의 유량추정, 한국수자원학회논문집, 2018 51(12)
2017
18. 이재현, 조희대, 최민하, 김동균, 소양강댐 유역에 대한 지표수문모형의 구축, 한국수자원학회논문집, 2016 50(12)
17. 이건, 이동률, 김동균, 우리나라에서 일최심신적설의 추정을 위한 인공신경망모형의 활용, 한국수자원학회논문집, 2017 50(10)
2016
16. 오민관, 이동률, 권현한, 김동균, 서울 삼성 1분구에 대한 침수면적 GIS 데이터베이스 구축, 한국수자원학회논문집, 2016 49(12)
15. 한재문, 김동균, 포아송 클러스터 가상강우생성 웹 어플리케이션 개발 및 검증 - 우리나라에 대해서, 한국수자원학회논문집, 2016 49(4)335-346
14. 이재현, 최민하, 김동균, 조건부 합성방법을 이용한 위성관측 토양수분과 지상관측 토양수분의 합성, 한국수자원학회논문집, 2016, 49(3)263-273
2015
13. 조운기, 이동률, 이재현, 김동균, Radar Polygon 기법의 개발 : 유사강우발생 확률에 근거한 면적강우량 산정기법, 한국수자원학회 논문집, 2015, 50(11).
12. 박현진, 양정석, 한재문, 김동균, 포아송 클러스터 강우생성모형을 이용한 도시홍수 해석, 한국수자원학회논문집, 2015, 48(9),729-741
11. 남윤수, 김동균, 우리나라 연최대강우량의 지형학적 특성 및 이에 근거한 최적확률밀도함수의 산정, 한국습지학회논문집, 2015, 17(3)251-263
2014
10. 송호용, 김동균, 김병식, 황석환, 김태웅, 멀티프랙탈 시공간 격자강우량 생산기법의 수문학적 적용성 평가: 충주댐상류유역 중심으로, 한국수자원학회논문집, 2014,48(10)959-972
9. 이동률, 이진수, 김동균, 강우의 시공간적 멀티프랙탈 특성에 기반을 둔 강우다운스케일링 기법의 한반도 호우사상에 대한 적용성 평가, 한국수자원학회논문집, 2014, 47(9) 839-852
8. 김장경, 권현한, 김동균, Bayesian MBLRP 모형을 이용한 시간강수량 모의 기법 개발, 대한토목학회논문집, 2014, 34(6)
7. 김동균, 권현한, 황석환, 김태웅, 극한수문사상의 모의를 위한 포아송 클러스터 강우생성모형의 적용성 평가, 대한토목학회논문집, 2014, 34(6)
6. 조희대, 김동균, 이강희, 입자군집최적화 알고리듬을 이용한 효율적인 TOPMODEL의 불확실도 분석, 한국수자원학회논문집, 2014, 47(3)285~295
2013
5. 조희대, 김동균, 이강희, 이진수, 이동률, 타원체로 모형화된 폭풍우 판별 알고리즘의 개발 및 적용, 한국방재학회논문집, 2013, 13(5)325-335
4. 김동균, 신지예, 이승오, 김태웅, 포아송 클러스터 강우생성의 홍수 모의 적용성 평가, 한국수자원학회지, 2013, 46(5)439-447
3. 유지영, 신지예, 김동균, 김태웅, 추계학적 강우발생모형과 Copula 함수를 이용한 가뭄위험분석, 한국수자원학회지, 2013, 46(4)425-437
2. 백종진, 김동균, 변규현, 최민하, 천리안 위성의 일사량 검증: 설마천, 청미천, 대한원격탐사학회지, 2013, 29(1)137-150
1.김동균, Searsville 댐 상류부를 대상으로 한 퇴적토와 저수지로 구성된 지하수 시스템의 지하수-지표수 상호작용, 한국습지학회지 , 2013, 15(1)1-7