Repository for Ideas & Research
Open Source GIS, Hydrologic Modeling, Optimization


Web of Science Master Journal List

1   Journal articles

*: Corresponding author

  1. Huidae Cho*, September 2020. A Recursive Algorithm for Calculating the Longest Flow Path and Its Iterative Implementation. Environmental Modelling & Software 131, 104774. doi:10.1016/j.envsoft.2020.104774. SCIE, Author’s Version.

    Author’s Note: Regrettably, I found three mathematical typos in this article. They do not impact any of my discussion or conclusions at all. (1) $\text{LFL}_\text{min}$ in Figure 3(a) and (2) one place referring to this figure should be $2\sqrt{A/\pi}$. (3) In the last sentence of the same subsection, the said equation should be rewritten as $2s\sqrt{\text{FAC}/\pi}$ accordingly, which is still greater than Eq. (13). Also, I found typographical errors by the publisher in the published article. The $\omega$'s in Eqs. (14) and (15) should be corrected to $w$'s. Figure 7(a) does not show green dots for r.accumulate–recursive (labeled as r.accumulate in my initial submission, which was relabeled to r.accumulate-recursive after both versions of r.accumulate were merged after acceptance). The author’s version has none of these issues.

    The longest flow path is widely used for studying hydrology. Traditionally, both or either of upstream and downstream flow length rasters are required to calculate the longest flow path. When processing multiple subwatersheds, this approach requires separate calculations of the downstream flow length raster for all the subwatersheds. However, raster computation involves a lot of disk input/output and can be slow. By defining the longest flow path recursively and introducing a branching strategy based on Hack’s law, this study proposes a new longest flow path algorithm that computes as few rasters as possible to reduce computational time and improve efficiency. To avoid stack overflows by excessive recursion, its iterative counterpart algorithm was also proposed. The proposed algorithms were implemented as a GRASS GIS module. Benchmark experiments proved that the new module outperforms an existing tool for a commercial GIS.

  2. Huidae Cho*, Jeongha Park, Dongkyun Kim, March 2019. Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL. Water 11 (3), 447. doi:10.3390/w11030447. SCIE.

    We tested four likelihood measures including two limits of acceptability and two absolute model residual methods within the generalized likelihood uncertainty estimation (GLUE) framework using the topography model (TOPMODEL). All these methods take the worst performance of all time steps as the likelihood of a model and none of these methods were successful in finding any behavioral models. We believe that reporting this failure is important because it shifted our attention from which likelihood measure to choose to why these four methods failed and how to improve these methods. We also observed how large parameter samples impact the performance of a hybrid uncertainty estimation method, isolated-speciation-based particle swarm optimization (ISPSO)-GLUE using the Nash-Sutcliffe (NS) coefficient. Unlike GLUE with random sampling, ISPSO-GLUE provides traditional calibrated parameters as well as uncertainty analysis, so over-conditioning the model parameters on the calibration data can affect its uncertainty analysis results. ISPSO-GLUE showed similar performance to GLUE with a lot less model runs, but its uncertainty bounds enclosed less observed flows. However, both methods failed in validation. These findings suggest that ISPSO-GLUE can be affected by over-calibration after a long evolution of samples and imply that there is a need for a likelihood measure that can better explain uncertainties from different sources without making statistical assumptions.

  3. Huidae Cho*, Tien M. Yee, Joonghyeok Heo, October 2018. Automated Floodway Determination Using Particle Swarm Optimization. Water 10 (10), 1420. doi:10.3390/w10101420. SCIE.

    The floodway plays an important role in flood modeling. In the United States, the Federal Emergency Management Agency requires the floodway to be determined using an approved computer program for developed communities. It is a local government’s interest to minimize the floodway area because encroachment areas may be permitted for human activities. However, manual determination of the floodway can be time-consuming and subjective depending on the modeler’s knowledge and judgments, and may not necessarily produce a small floodway especially when there are many cross sections because of their correlation. Very little work has been done in terms of floodway optimization. In this study, we propose an optimization method for minimizing the floodway area using the Isolated-Speciation-based Particle Swarm Optimization algorithm and the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). This method optimizes the floodway by defining an objective function that considers the floodway area and hydraulic requirements, and automating operations of HEC-RAS. We used a floodway model provided by HEC-RAS and compared the proposed, manual, and default HEC-RAS methods. The proposed method consistently improved the objective function value by 1-40%. We believe that this method can provide an automated tool for optimizing the floodway model using HEC-RAS.

  4. Jaehyeon Lee, Huidae Cho, Minha Choi, Dongkyun Kim*, December 2017. 소양강댐 유역에 대한 지표수문모형의 구축 (Development of a Land Surface Model for the Soyang River Basin). Journal of the Korean Water Resources Association 50 (12), 837–847. doi:10.3741/JKWRA.2017.50.12.837. KCI.

    A Land Surface Model (LSM) was developed for the Soyang river basin located in the Korean Peninsula to clarify the spatio-temporal variability of hydrological weather parameters. The Variable Infiltration Capacity (VIC) model was used as an LSM. The spatial resolution of the model was 10 km and the time resolution was 1 day. Based on the daily flow data from 2007 to 2010, seven parameters of the model were calibrated using the Isolated-speciation-based Particle Swarm Optimization (ISPSO) algorithm and the model was verified using the daily flow data from 2011 to 2014. The model showed a Nash-Sutcliffe coefficient of 0.90 and a correlation coefficient of 0.95 for both the calibration and validation periods. The hydrometeorological variables estimated for the Soyang river basin reflected well the seasonal characteristics of summer rainfall concentration, the change of short and shortwave radiation due to temperature change, the change of surface temperation, the evaporation and vegetation increase in the over layer, and the corresponding change in total evapotranspiration. The model soil moisture data was compared with in-situ soil moisture data. The slope of the trend line relating the two data was 1.087 and the correlation coefficient was 0.723 for the spring, summer, and fall seasons. The result of this study suggests that the LSM can be used as a powerful tool in developing precise and efficient water resources plans by providing accurate understanding on the spatio-temporal variation of hydrometeorological variables.

  5. Dongkyun Kim*, Huidae Cho, Christian Onof, Minha Choi, May 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. doi:10.1007/s00477-016-1234-6. SCI.

    We present a web application named Let-It-Rain that is able to generate a 1-hour temporal resolution synthetic rainfall time series using the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) model, a type of Poisson stochastic rainfall generator. Let-It-Rain, which can be accessed through the web address, adopts a web-based framework combining ArcGIS Server from server side for parameter value dissemination and JavaScript from client side to implement the MBLRP model. This enables any desktop and mobile end users with internet access and web browser to obtain the synthetic rainfall time series at any given location at which the parameter regionalization work has been completed (currently the contiguous United States and Republic of Korea) with only a few mouse clicks. Let-It-Rain shows satisfactory performance in its ability to reproduce observed rainfall mean, variance, auto-correlation, and probability of zero rainfall at hourly through daily accumulation levels. It also shows a reasonably good performance in reproducing watershed runoff depth and peak flow. We expect that Let-It-Rain can stimulate the uncertainty analysis of hydrologic variables across the world.

  6. Huidae Cho*, Emma Bones, August 2016. Quantification of Uncertainties in the 100-year Flow at an Ungaged Site Near a Gaged Station and Its Application in Georgia. Journal of Hydrology 539, 640–647. doi:10.1016/j.jhydrol.2016.05.070. SCI, Author’s Version.

    The Federal Emergency Management Agency has introduced the concept of the “1-percent plus” flow to incorporate various uncertainties in estimation of the 100-year or 1-percent flow. However, to the best of the authors’ knowledge, no clear directions for calculating the 1-percent plus flow have been defined in the literature. Although information about standard errors of estimation and prediction is provided along with the regression equations that are often used to estimate the 1-percent flow at ungaged sites, uncertainty estimation becomes more complicated when there is a nearby gaged station because regression flows and the peak flow estimate from a gage analysis should be weighted to compute the weighted estimate of the 1-percent flow. In this study, an equation for calculating the 1-percent plus flow at an ungaged site near a gaged station is analytically derived. Also, a detailed process is introduced for calculating the 1-percent plus flow for an ungaged site near a gaged station in Georgia as an example and a case study is performed. This study provides engineers and practitioners with a method that helps them better assess flood risks and develop mitigation plans accordingly.

  7. Joonghyeok Heo, Jaehyung Yu*, John R. Giardino, Huidae Cho, August 2015. Response of Water Resources to Climate and Land-Cover Changes in a Semi-Arid Watershed, New Mexico, USA. Terrestrial, Atmospheric and Oceanic Sciences 26 (4), 463–474. doi:10.3319/TAO.2015.03.24.01(Hy). SCI.

    This research evaluates a climate-land cover-water resources interconnected system in a semi-arid watershed with minimal human impact from 1970 to 2009. We found 0.9°C increase in temperature and 10.9% decrease in precipitation. The temperature exhibited a lower increase trend and precipitation showed a similar decrease trend compared to previous studies. The dominant land-cover change trend was grass and forest conversion into bush/shrub and developed land and crop land into barren and grass land. These alterations indicate that changes in temperature and precipitation in the study area may be linked to changes in land cover although human intervention is recognized as the major land-cover change contributor for the short term. These alterations also suggest that decreasing human activity in the study area leads to developed land and crop land conversion into barren and grass land. Hydrological responses to climate and land-cover changes for surface runoff, groundwater discharge, soil water content and evapotranspiration decreased by 10.2, 10.0, 4.1 and 10.5%, respectively. Hydrological parameters generally follow similar trends to that of precipitation in semi-arid watersheds with minimal human development. Soil water content is sensitive to land-cover change and offset relatively by the changes in precipitation.

  8. Joonghyeok Heo, Jaehyung Yu*, John R. Giardino, Huidae Cho, March 2015. Impacts of Climate and Land-Cover Changes on Water Resources in a Humid Subtropical Watershed: A Case Study from East Texas, USA. Water and Environment Journal 29 (1), 51–60. doi:10.1111/wej.12096. SCI.

    This study investigates the response of water resources regarding the climate and land-cover changes in a humid subtropical watershed during the period 1970–2009. A 0.7°C increase in temperature and a 16.3% increase in precipitation were observed. Temperature had a lower increase trend and precipitation showed a definite increasing trend compared to previous studies. The main trend of land-cover change was a conversion of vegetation and barren lands to developed and crop lands affected by human intervention, and forest and grass to bush/shrub, which is considered to be caused by the natural climate system. Hydrologic responses to climate and landcover changes resulted in increases of surface runoff (15.0%), soil water content (2.7%), evapotranspiration (20.1%), and a decrease in groundwater discharge (9.2%). We found that surface runoff is relatively stable with precipitation whereas groundwater discharge and soil water content are sensitive to changes in land cover, especially land cover brought about by human intervention.

  9. Huidae Cho, Dongkyun Kim*, Kanghee Lee, March 2014. 입자군집최적화 알고리듬을 이용한 효율적인 TOPMODEL의 불확실도 분석 (Efficient Uncertainty Analysis of TOPMODEL Using Particle Swarm Optimization). Journal of the Korean Water Resources Association 47 (3), 285–295. doi:10.3741/JKWRA.2014.47.3.285. KCI.

    We applied the ISPSO-GLUE method, which integrates the Isolated-Speciation-based Particle Swarm Optimization (ISPSO) with the Generalized Likelihood Uncertainty Estimation (GLUE) method, to the uncertainty analysis of the Topography Model (TOPMODEL) and compared its performance with that of the GLUE method. When we performed the same number of model runs for the both methods, we were able to identify the point where the performance of ISPSO-GLUE exceeded that of GLUE, after which ISPSO-GLUE kept improving its performance steadily while GLUE did not. When we compared the 95% uncertainty bounds of the two methods, their general shapes and trends were very similar, but those of ISPSO-GLUE enclosed about 5.4 times more observed values than those of GLUE did. What it means is that ISPSO-GLUE requires much less number of parameter samples to generate better performing uncertainty bounds. When compared to ISPSO-GLUE, GLUE overestimated uncertainty in the recession limb following the maximum peak streamflow. For this recession period, GLUE requires to find more behavioral models to reduce the uncertainty. ISPSO-GLUE can be a promising alternative to GLUE because the uncertainty bounds of the method were quantitatively superior to those of GLUE and, especially, computationally expensive hydrologic models are expected to greatly take advantage of the feature.

  10. Huidae Cho, Francisco Olivera*, March 2014. Application of Multimodal Optimization for Uncertainty Estimation of Computationally Expensive Hydrologic Models. Journal of Water Resources Planning and Management 140 (3), 313–321. doi:10.1061/(ASCE)WR.1943-5452.0000330. SCI.

    The generalized likelihood uncertainty estimation (GLUE) framework has been widely used in hydrologic studies. However, the extensive random sampling causes a high computational burden that prohibits the efficient application of GLUE to costly distributed hydrologic models such as the soil and water assessment tool (SWAT). In this study, a multimodal optimization algorithm called isolated-speciation-based particle swarm optimization (ISPSO) is employed to take samples from the search space. A comparison between the ISPSO-GLUE, proposed here, and traditional GLUE approaches shows that the two approaches generate similar uncertainty bounds, but that the convergence rate to stable uncertainty bounds is much faster for ISPSO-GLUE than for GLUE. That is, ISPSO-GLUE needs a much smaller number of samples than GLUE to arrive to a very similar answer. Although the ISPSO-GLUE slightly underestimated the prediction uncertainty and missed a number of observed values, the proposed approach is considered to be a good alternative to the typical GLUE approach that employs random sampling.

  11. Huidae Cho, Dongkyun Kim, Kanghee Lee, Jinsu Lee, Dongryul Lee*, October 2013. 타원체로 모형화된 폭풍우 판별 알고리듬의 개발 및 적용 (Development and Application of a Storm Identification Algorithm That Conceptualizes Storms by Elliptical Shape). Journal of the Korean Society of Hazard Mitigation 13 (5), 325–335. doi:10.9798/KOSHAM.2013.13.5.325. KCI.

    A storm identification algorithm conceptualizing storms by elliptical shape was developed. The new algorithm identifies the center, major and minor axes, and the inclination angle of the ellipse that contains the maximum volume of rainfall for a given area, using the Isolated-Speciation-based Particle Swarm Optimization algorithm. It was applied to radar precipitation imagery of 10 major storms observed in Korea during the years 2008 and 2012. The algorithm successfully identified the storm shapes for all time steps of all the 10 major storms. The following conclusions were drawn from the results of the storm identification: (1) as the size of the ellipse becomes smaller, the diversity of the storm shape increased, and the diversity decreased as the size of the ellipse increases; (2) the temporal variation of the storm center identified by the ellipse is not always continuous; (3) the tracking capability of the algorithm is expected to be improved when the center and shape of the ellipse for the previous time step are considered in the objective function of the optimization algorithm.

  12. Dongkyun Kim, Francisco Olivera, Huidae Cho, Seung Oh Lee*, October 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. doi:10.1007/s00477-013-0697-y. SCI.

    This study analyzes how the stochastically generated rainfall time series accounting for the inter-annual variability of rainfall statistics can improve the prediction of watershed response variables such as peak flow and runoff depth. The modified Bartlett-Lewis rectangular pulse (MBLRP) rainfall generation model was improved such that it can account for the inter-annual variability of the observed rainfall statistics. Then, the synthetic rainfall time series was generated using the MBLRP model, which was used as input rainfall data for SCS hydrologic models to produce runoff depth and peak flow in a virtual watershed. These values were compared to the ones derived from the synthetic rainfall time series that is generated from the traditional MBLRP rainfall modeling. The result of the comparison indicates that the rainfall time series reflecting the inter-annual variability of rainfall statistics reduces the biasness residing in the predicted peak flow values derived from the synthetic rainfall time series generated using the traditional MBLRP approach by 26-47%. In addition, it was observed that the overall variability of the peak flow and run off depth distribution was better represented when the inter-annual variability of rainfall statistics are considered.

  13. Dongkyun Kim*, Francisco Olivera, Huidae Cho, October 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. doi:10.1007/s00477-013-0696-z. SCI.

    A noble approach of stochastic rainfall generation that can account for inter-annual variability of the observed rainfall is proposed. Firstly, we show that the monthly rainfall statistics that is typically used as the basis of the calibration of the parameters of the Poisson cluster rainfall generators has significant inter-annual variability and that lumping them into a single value could be an oversimplification. Then, we propose a noble approach that incorporates the inter-annual variability to the traditional approach of Poisson cluster rainfall modeling by adding the process of simulating rainfall statistics of individual months. Among 132 gage-months used for the model verification, the proportion that the suggested approach successfully reproduces the observed design rainfall values within 20% error varied between 0.67 and 0.83 while the same value corresponding to the traditional approach varied between 0.21 and 0.60. This result suggests that the performance of the rainfall generation models can be largely improved not only by refining the model structure but also by incorporating more information about the observed rainfall, especially the inter-annual variability of the rainfall statistics.

  14. Dongkyun Kim*, Francisco Olivera, Huidae Cho, Scott A. Socolofsky, June 2013. Regionalization of the Modified Bartlett-Lewis Rectangular Pulse Stochastic Rainfall Model. Terrestrial, Atmospheric and Oceanic Sciences 24 (3), 421–436. doi:10.3319/TAO.2012.11.12.01(Hy). SCI.

    Parameters of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall simulation model were regionalized across the contiguous United States. 3,444 National Climate Data Center (NCDC) rain gauges were used to obtain spatial and seasonal patterns of the model parameters. The MBLRP model was calibrated to minimize the discrepancy between the precipitation depth statistics between observed and MBLRP-generated precipitation time series. These statistics included the mean, variance, probability of zero rainfall and autocorrelation at 1-, 3-, 12- and 24-hour accumulation intervals. The Ordinary Kriging interpolation technique was used to generate maps of the six MBLRP model parameters for each of the 12 months of the year. All parameters had clear to discernible regional tendency; except for one related to the rain cell duration distribution. Parameter seasonality, though, was not obvious and it was more apparent in some locations than in others, depending on the seasonality of the rainfall statistics. Cross-validation was used to assess the validity of the parameter maps. The results indicate that the suggested maps reproduce well the statistics of the observed rainfall for different accumulation intervals, except for the lag-1 autocorrelation coefficient. The boundary of the expected residual, with 95% confidence, between the observed rainfall statistics and the statistics of the simulated rainfall based on the map parameters was approximately ±0.064 mm/hr, ±1.63mm2/hr2, ±0.16, and ±0.030 for mean, variance, lag-1 autocorrelation, and probability of zero rainfall at hourly accumulation level, respectively. The estimated parameter values were also used to estimate storm and rain cell characteristics.

  15. Huidae Cho, Dongkyun Kim, Francisco Olivera*, Seth D. Guikema, August 2011. Enhanced Speciation in Particle Swarm Optimization for Multi-Modal Problems. European Journal of Operational Research 213 (1), 15–23. doi:10.1016/j.ejor.2011.02.026. SCIE, Author’s Version, R Script.

    In this paper, we present a novel multi-modal optimization algorithm for finding multiple local optima in objective function surfaces. We build from Species-based particle swarm optimization (SPSO) by using deterministic sampling to generate new particles during the optimization process, by implementing proximity-based speciation coupled with speciation of isolated particles, and by including “turbulence regions” around already found solutions to prevent unnecessary function evaluations. Instead of using error threshold values, the new algorithm uses the particle’s experience, geometric mean, and “exclusion factor” to detect local optima and stop the algorithm. The performance of each extension is assessed with leave-it-out tests, and the results are discussed. We use the new algorithm called Isolated-Speciation-based particle swarm optimization (ISPSO) and a benchmark algorithm called Niche particle swarm optimization (NichePSO) to solve a six-dimensional rainfall characterization problem for 192 rain gages across the United States. We show why it is important to find multiple local optima for solving this real-world complex problem by discussing its high multi-modality. Solutions found by both algorithms are compared, and we conclude that ISPSO is more reliable than NichePSO at finding optima with a significantly lower objective function value.

  16. Huidae Cho, Francisco Olivera*, June 2009. Effect of the Spatial Variability of Land Use, Soil Type, and Precipitation on Streamflows in Small Watersheds. Journal of the American Water Resources Association 45 (3), 673–686. doi:10.1111/j.1752-1688.2009.00315.x. SCI.

    The spatial variability of the data used in models includes the spatial discretization of the system into subsystems, the data resolution, and the spatial distribution of hydrologic features and parameters. In this study, we investigate the effect of the spatial distribution of land use, soil type, and precipitation on the simulated flows at the outlet of “small watersheds” (i.e., watersheds with times of concentration shorter than the model computational time step). The Soil and Water Assessment Tool model was used to estimate runoff and hydrographs. Different representations of the spatial data resulted in comparable model performances and even the use of uniform land use and soil type maps, instead of spatially distributed, was not noticeable. It was found that, although spatially distributed data help understand the characteristics of the watershed and provide valuable information to distributed hydrologic models, when the watershed is small, realistic representations of the spatial data do not necessarily improve the model performance. The results obtained from this study provide insights on the relevance of taking into account the spatial distribution of land use, soil type, and precipitation when modeling small watersheds.

  17. Huidae Cho, Francisco Olivera*, Seth D. Guikema, October 2008. A Derivation of the Number of Minima of the Griewank Function. Applied Mathematics and Computation 204 (2), 694–701. doi:10.1016/j.amc.2008.07.009. SCIE, Author’s Version, Cited in Griewank Function in MathWorld.

    The Griewank function is commonly used to test the ability of different solution procedures to find local optima. It is important to know the exact number of minima of the function to support its use as a test function. However, to the best of our knowledge, no attempts have been made to analytically derive the number of minima. Because of the complex nature of the function surface, a numerical method is developed to restrict domain spaces to hyperrectangles satisfying certain conditions. Within these domain spaces, an analytical method to count the number of minima is derived and proposed as a recursive functional form. The numbers of minima for two search spaces are provided as a reference.

  18. Francisco Olivera*, Milver Valenzuela, Raghavan Srinivasan, Janghwoan Choi, Huidae Cho, Srikanth Koka, Ashish Agrawal, April 2006. ArcGIS-SWAT: A Geodata Model and GIS Interface for SWAT. Journal of the American Water Resources Association 42 (2), 295–309. doi:10.1111/j.1752-1688.2006.tb03839.x, Erratum doi:10.1111/j.1752-1688.2006.tb04496.x. SCI.

    This paper presents ArcGIS-SWAT, a geodata model and geographic information system (GIS) interface for the Soil and Water Assessment Tool (SWAT). The ArcGIS-SWAT data model is a system of geodatabases that store SWAT geographic, numeric, and text input data and results in an organized fashion. Thus, it is proposed that a single and comprehensive geodatabase be used as the repository of a SWAT simulation. The ArcGIS-SWAT interface uses programming objects that conform to the Component Object Model (COM) design standard, which facilitate the use of functionality of other Windows-based applications within ArcGIS-SWAT. In particular, the use of MS Excel and MATLAB functionality for data analysis and visualization of results is demonstrated. Likewise, it is proposed to conduct hydrologic model integration through the sharing of information with a not-model-specific hub data model where information common to different models can be stored and from which it can be retrieved. As an example, it is demonstrated how the Hydrologic Modeling System (HMS)—a computer application for flood analysis—can use information originally developed by ArcGIS-SWAT for SWAT. The application of ArcGIS-SWAT to the Seco Creek watershed in Texas is presented.

2   Conference papers and presentations

*: Faculty mentor

  1. Owen Smith, Huidae Cho*, Jennifer McCollum, July 13–16, 2020. Tree Canopy Dataset Creation for the State of Georgia with NAIP Imagery and Python. 2020 Esri User Conference. Esri. Virtual GIS Event. Esri Map Gallery.
  2. Owen Smith, Huidae Cho*, March 13, 2020. A Reproducible Supervised Classification System for Tree Canopy and Deforestation Detection Within an Open Source Python Framework Utilizing NAIP Imagery. University of North Georgia 25th Annual Research Conference. Gainesville, GA.
  3. Jennifer McCollum, Huidae Cho*, March 13, 2020. Georgia Statewide Tree Canopy Analysis. University of North Georgia 25th Annual Research Conference. Gainesville, GA.
  4. Tyler Henderson, Huidae Cho*, March 13, 2020. Expansion of Topographic Wetness Index to Include Remotely Sensed Soil Data. University of North Georgia 25th Annual Research Conference. Gainesville, GA.
  5. Huidae Cho, April 16, 2019. Revisiting the Longest Flow Path Algorithm. 2019 Georgia Water Resources Conference. University of Georgia. Athens, GA.
  6. Huidae Cho, Dongkyun Kim, Christian Onof, Minha Choi, October 2, 2018. Let-It-Rain: A Web-based Stochastic Rainfall Generator. 2018 Georgia Geospatial Conference. Georgia Urban and Regional Information Systems Association. Classic Center, Athens, GA.
  7. Marcus Flores, Huidae Cho, May 2, 2018. Bridging the Gap Between Esri and CRM. 2018 Esri Southeast User Conference. Esri. Charlotte Convention Center, Charlotte, NC.
  8. Tien Yee, Huidae Cho, May 22, 2017. Towards an Automated Floodway Optimizer for HEC-RAS. World Environmental & Water Resources Congress 2017. Environmental & Water Resources Institute, American Society of Civil Engineers. Sacramento, CA.
  9. Jaehyeon Lee, Huidae Cho, Dongkyun Kim, April 24, 2017. Assessment of the Applicability of the Satellite-In-Situ Composite Soil Moisture Data Assimilation Using Ensemble Kalman Filter. European Geosciences Union General Assembly 2017. European Geosciences Union. Vienna, Austria.
  10. Huidae Cho, April 20, 2017. Web-based Hydrologic Modeling System for Texas. Proceedings of 2017 Georgia Water Resources Conference. University of Georgia. Athens, GA.
  11. Tien Yee, Huidae Cho, April 20, 2017. Floodway Optimization Algorithm for Streams in Georgia. Proceedings of 2017 Georgia Water Resources Conference. University of Georgia. Athens, GA.
  12. Jaehyeon Lee, Huidae Cho, Dongkyun Kim, August 22, 2016. Applicability of AMSR2 Soil Moisture Data in a Real-Time Land Surface Model. HIC 2016, 12th International Conference on Hydroinformatics: Smart Water for the Future. Society of Smart Water Grid. Songdo ConvensiA, Incheon, South Korea.
  13. Dongkyun Kim, Huidae Cho, Jaemoon Han, May 15, 2014. Development and Validation of a Web Application for Synthetic Rainfall Generation based on the Poisson Cluster Process. Korea Water Resources Association Conference 2014. Korea Water Resources Association. Busan, South Korea.
  14. Huidae Cho, Dongkyun Kim, March 8, 2014. Spatiotemporal Storm Tracking for Hydrologic Modeling Using Particle Swarm Optimization. Southeastern Regional Conference 2014: Future Preparedness—Smart Technologies and Science. Korean-American Scientists and Engineers Association. Atlanta, GA.
  15. Huidae Cho, Janghwoan Choi, James Demby, Sam Crampton, Sivasankkar Selvanathan, March 4, 2013. Development of FEMA’s GeoDam-BREACH Toolset for Simplified Dam Break Analysis. Virginia Water Conference 2013. Virginia Lakes and Watersheds Association. Richmond, VA.
  16. Diwakar Sharma, Janghwoan Choi, Jay Sadhu, Sivasankkar Selvanathan, Huidae Cho, Ken Logsdon Jr., October 21, 2010. Improved Visualization of Contours/Bands as Symbology Using ESRI Terrain for Flood Mapping and Engineering Analysis. 6th Annual MAFSM Conference: New Maps, New Regs—Reducing Flood and Stormwater Impacts in Maryland. Maryland Association of Floodplain and Stormwater Managers. Linthicum, MD.
  17. Jay Sadhu, Janghwoan Choi, Diwakar Sharma, Sivasankkar Selvanathan, Huidae Cho, Ken Logsdon Jr., October 21, 2010. Overcoming Depth Grid Creation Challenges Through the Use of Depth TIN/Terrain. 6th Annual MAFSM Conference: New Maps, New Regs—Reducing Flood and Stormwater Impacts in Maryland. Maryland Association of Floodplain and Stormwater Managers. Linthicum, MD.
  18. Janghwoan Choi, Sivasankkar Selvanathan, Jay Sadhu, Diwakar Sharma, Huidae Cho, October 14, 2010. Automated Peakflow Computations Using NSS and ArcGIS. 6th NJAFM Annual Conference: Proactive Floodplain Management—Reducing Vulnerability and Leveraging Resources. New Jersey Association for Floodplain Management. Somerset, NJ.
  19. Katherine Hermann, Ken Logsdon Jr., Huidae Cho, May 18, 2010. Digital Flood Insurance Rate Map Panel Management Module. ASFPM 34th Annual National Conference: Building Blocks of Floodplain Management. Association of State Floodplain Managers. Oklahoma City, OK.
  20. Ken Logsdon Jr., Janghwoan Choi, Huidae Cho, May 18, 2010. Layered Flood Theme and an Integrated QC Module. ASFPM 34th Annual National Conference: Building Blocks of Floodplain Management. Association of State Floodplain Managers. Oklahoma City, OK.
  21. Huidae Cho, Tamrat Bedane, Mathini Sreetharan, Jean Huang, October 15, 2009. Floodplain Development for Flood Insurance Studies Using GeoTerrain. 5th NJAFM Annual Conference: Effective Floodplain Management—Solutions Using Limited Resources. New Jersey Association for Floodplain Management. Somerset, NJ.
  22. Francisco Olivera, Huidae Cho, July 4, 2007. Importance of Spatial Distribution in Small Watersheds. 4th International Soil and Water Assessment Tool (SWAT) Conference. UNESCO-IHE. Delft, The Netherlands.
  23. Francisco Olivera, Huidae Cho, July 14, 2005. Two-Step Calibration Method for SWAT. 3rd International Soil and Water Assessment Tool (SWAT) Conference. Swiss Federal Institute for Environmental Science and Technology. Zurich, Switzerland.
  24. Francisco Olivera, Huidae Cho, April 2005. The Two-Step Calibration Method of Distributed Models. VII IAHS Scientific Assembly. International Association of Hydrologic Sciences (IAHS). Foz do Iguacu, Brazil.
  25. Jonghae Kim, Kunyeun Han, Huidae Cho, Hyunsang Choi, November 2001. GRASS와 연계한 GIS 수문모의 시스템 (GIS-Based Hydrological Modeling by Using GRASS). Proceedings of Korean Society of Civil Engineers, 1–4.

3   Book reviews

  1. Huidae Cho, January 2019. Book Review of “GIS for Surface Water: Using the National Hydrography Dataset” by Jeff Simley. Photogrammetric Engineering & Remote Sensing 85 (1), 11–12. doi:10.14358/PERS.85.1.11. SCI.

4   Workshop manuals

  1. Kunyeun Han, Sangho Kim, Inho Son, Changhyun Baek, Kyuhyun Choi, Huidae Cho, February 1999. 하천·호소 수질예측 모형(QUAL2E, WASP 등) (Riverine & Lacustrine Water Quality Prediction Models (QUAL2E, WASP, etc.)). 7th Water Resources Engineering Workshop Manual. Korea Water Resources Association.