Yonsoo Kim

Evaluation for Snowfall Depth Forecasting using Neural Network and Multiple Regression Models

Since snowfall is related to various meteorological variables such as temperature and precipitation, it is generated in nonlinear manner. Therefore this study constructs snowfall forecasting model using neural networks and multiple regression which can consider nonlinear process of snowfall. The study constructs the forecasting models for each station using temperature, precipitation, and snowfall depth observed from starting time of observation to 1999. And snowfalls are calculated for all stations by using temperature and precipitation in the period of 2000 to 2011. From the statistical analysis of the calculated snowfall, the proper model is selected. The selected models show the correlation coefficients of 0.700 to 0.949 and the adjusted determination coefficients of 41.7% to 89.8%. The applicability of neural network models is superior to other model at almost every station. But in some cases multiple regression models show better results than neural network models due to the lack of observational data during learning period and the extreme peak values which are not learned during forecasting period. According to the study, the results of the models confirm the predicting snowfall depth by using temperature and precipitation is possible and show neural network model is better than the existing statistical models.

A Review on the Management of Water Resources Information based on Big Data and Cloud Computing

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

Analysis of Relationship between Inundation Depth and Plant Habitat of Binae Wetland

In this study, we analyzed the flow duration of Binae wetland, which has a well-developed natural ecosystem and simulated the inundation depth of Binae wetland according to flow duration using HEC-RAS and RAS Mapper. Then, the plant habitats in inundation depth were analyzed. As a result, Salix koreensis Anderson inhabits from the area where the flooding did not occur to 0.8m below water level. Miscanthus sacchariflorus Benth inhabits from the area where the flooding did not occur to 0.4 m below water level. Artemisia princeps Pampanini-Erigeron canadensis L and Phragmites japonica steud inhabit from 0 to 0.4 m below water level. And Humulus japonicus inhabits from 0 to 0.8 m below water level. Here, plant habitats are analyzed by inundation depth and this study could be used as an idea for wetland design considering inundation depth. For more accurate research, methodology for the analysis of relationship between inundation depth and plants should be developed with accumulated data in long term.

Identifying and Evaluating Chaotic Behavior in Hydro-Meteorological Processes

The aim of this study is to identify and evaluate chaotic behavior in hydro-meteorological processes. This study poses the two hypotheses to identify chaotic behavior of the processes. First, assume that the input data is the significant factor to provide chaotic characteristics to output data. Second, assume that the system itself is the significant factor to provide chaotic characteristics to output data. For solving this issue, hydro-meteorological time series such as precipitation, air temperature, discharge, and storage volume were collected in the Great Salt Lake and Bear River Basin, USA. The time series in the period of approximately one year were extracted from the original series using the wavelet transform. The generated time series from summation of sine functions were fitted to each series and used for investigating the hypotheses. Then artificial neural networks had been built for modeling the reservoir system and the correlation dimension was analyzed for the evaluation of chaotic behavior between inputs and outputs. From the results, we found that the chaotic characteristic of the storage volume which is output is likely a byproduct of the chaotic behavior of the reservoir system itself rather than that of the input data.

Estimation of Frequency Based Snowfall Depth Considering Climate Change Using Neural Network

In recent years, extreme weather event due to the climate change has been frequently occurred over the world. Meanwhile, Korean peninsula has been suffered from the natural disasters such as snowfall. This study estimated the snowfall depth of climate change by using temperature, precipitation based on KMA-RegCM3 climate model and climate change scenario. We estimated the frequency based daily snowfall depth(50yr, 80yr, 100yr and 200yr) at 18 weather stations for four different target periods(Target I: 1971~2010, Target II: 2011~2040, Target III: 2041~2070, Target IV: 2071~2100) under climate change. Snowfall has nonlinear relationship with temperature and precipitation and so this study used a neural network and multiple regression models which can consider nonlinearity between snowfall and meteorological variables for its forecasting. As the results, the average rate of frequency based snowfall depth will be decreased by 6~18% for Target I and the rate will be continuously decreased in Target II, III and IV. The results of this study could be used as the basic information for the future disaster prevention planning and design criteria related to snowfall.

Calibration of Gauge Rainfall Considering Wind Effect

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using . Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.

Risk assessment for water quality of a river using QUAL2E model

In this study, we consider ability of self-purification for a rational water quality management. And we assess the risk of Alkyl Benzene Sulfonic acid sodium salt(ABS) of harmful ingredients in Anseong Cheon watershed using QUAL2E model. The observations and simulated results were fitted well for BOD and ABS, but even though the trend of DO concentration change was well represented, the error between observation and simulation values was existed. We assessed the Risk assessment by calculating Risk quotient(RQ) by Predicted Exposure Concentration(PEC) and Predicted No-Effect Concentration(PNEC). Results of the impact of ABS on the self-purification of the river were Anseongcheon[0.0003(Bressan), 0.06(Criteria of Ministry of environment)], Jinwicheon[0.0002(Bressan), 0.04(Criteria of Ministry of environment). And result of the impact of ABS on the Aquatic ecosystem of the river were Anseongcheon[0.0667(Bressan), 0.005(Criteria of Ministry of environment)], Jinwicheon[0.1(Bressan), 0.0075(Criteria of Ministry of environment). All of these results were smaller than the 1.0 which is the reference value suggested by Norification No.30 of the National Institute of Environment Research. So, ABS did not affect a self-purification and aquatic ecosystem of the river. The method suggested in the study is a simple one and can provide more information for harmful ingredients than criteria of Ministry of environment.

A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area

This study establishes a methodology for the application of downscaled GCM data in a mountainous area having large spatial variations of rainfall and attempts to estimate the change of rainfall characteristics in the future under climate change. The Namhan river basin, which is in the mountainous area of the Korean peninsula, has been chosen as the study area. neural network-simple kriging with varying local means (ANN-SKlm) has been built by combining the artificial neural network, which is one of the general downscaling techniques, with the SKlm regionalization technique, which can reflect the geomorphologic characteristics. The ANN-SKlm technique was compared with the Thiessen technique and the ordinary kriging (OK) technique in the study area and the SKlm technique showed the best results. Future rainfall levels have been predicted by downscaling the data from CNRM-CM3 climate model, which was simulated under the A1B scenario. According to the results of future annual average rainfall by each regionalization technique, the Thiessen and OK techniques underestimated the future rainfall when compared to the ANN-SKlm technique. Therefore this methodology will be very useful for the prediction of future rainfall levels under climate change, most notably in a mountainous area.