Publications

My Google Scholar Profile:

http://scholar.google.co.kr/citations?user=UbCyvS0AAAAJ&hl=en

* Corresponding Author

96. Predicting chlorophyll-a concentrations in the world’s largest lakes using Kolmogorov-Arnold Networks, submitted to Water Research

95.  Unveiling Vulnerability of Groundwater Quality through Predictive Machine Learning-Based Assessment, Submitted to Groundwater for Sustainable Development.

94. Prediction of Earth-Fissure Hazard in Arid Regions Using Advanced Machine-Learning Techniques and Implications for Sustainable Land Management, Submitted to Environmental Research

93. Prediction of space-time precipitation field using an ConvLSTM model with the hybrid cost function considering both amount and spatial structure for improved performance to predict extreme rainfall, under preparation.

92.  Impact of Climatic Factors on Eutrophication in the World’s Largest Lake, Sumbitted to Sciences of the Total Environmentl

91. Evaluation of optimized multi-model ensembles for extreme precipitation projection considering various objective functions, Submitted to Atmospheric Research

90. Remote Sensing Data Based Assessment of Reliability–Resilience and Vulnerability of Talar Watershed in Iran, Submitted to Journal of Flood Risk Management

89. Beneath the surface: Exploring pluvial floods and income disparities in Seoul’s underground life, Submitted to Sustainable Cities and Society

88. High-Resolution Snow Depth Modeling in South Korea Using Radar-Based Precipitation Data, Submitted to Journal of Hydrology

87. Performances of Retrieval-Augmented Generation (RAG) Models and Fine-tuned Large-Language Models (LLMs) in Retrieving Construction Safety Management Knowledge, Submitted to Developments in the Built Environment 

86. Forecasting Daily Reference Evapotranspiration in Different Hydrological Conditions Through Hybrid Wavelet–Bayesian Optimization–Gaussian Process Regression, Sumbitted to Engineering Applications of Artificial Intelligence

85. Exploring the relationship between flood resilience and risk management, Submitted to Environmental Science and Pollution Research.

84. Context-Specific Selection of Poisson Cluster Rainfall Models for Accurate Urban Hydrological Simulations, Submitted to Water Research

83. Multi-Scale Groundwater-based Drought Prediction Model Integrating Deep Learning and Hydrometeorological Factors, Submitted to Journal of Environmental Management

82. Machine learning-based modeling of surface water temperature dynamics in Arctic lakes, Under Review, Natural Resources Research

81. 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, Under review

80. Global high-resolution water storage anomaly (HWSA v1.0) product using a machine learning downscaling framework based on a physically constrained sliding window technique, Submitted to Earth System Science Data 

79. Socio-economic vulnerability assessment and validation of Seoul, South Korea, submitted to Sustainable Cities and Society 

78. Mapping Flood Susceptibility on a Global Scale: A Future Perspective on Climate and Land-Use Change Impacts, Submitted to Nature Communications

77. 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 

76. Remote sensing and Mathematica-based analysis using net-encoder and deconvolution models for predicting lake surface area changes, Submitted to Science of the Total Environment

75. Assessment of hydrologic risk of drought in South Korea according to climate change scenarios using a multiple drought index based a on dynamic naive Bayesian classifier, Submitted to Stochastic Research and Risk Asssessment

74. Simulation of future sub-hourly rainfall using the Bartlett-Lewis based rainfall model: Application for entire area of Germany and South Korea, 1st round revision in Journal of Hydrology

73. 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.

72. Deep learning models for drought susceptibility mapping in Southeast Queensland, Australia, Submitted to Stochastic Research and Risk Assessment.

71. Enhancing Streamflow Forecasting with CNN-LSTM and High-Resolution Radar-Based Precipitation, Submitted to Engineering Applications of Artificial Intelligence 

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. 

2020

1. 기후변화에 따른 재난위기 극복을 위한 대비체계 구축방안 연구 (행정안전부, 재난관리실 재난대응정책관 기후재난대응과, 수행연구원: 김동균).  다운로드 링크

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