The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and   were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.

본 연구에서는 관개효율의 연별 변화와 필요수량을 고려하여 추정된 관개지구 용수 공급량이 현장에서 실제 공급되는 수량을 잘 모의하는지를 평가하였다. 대상지구로 이동저수지 지구를 선정하여, 2001∼2009년 기간에 대한 실측 공급량 자료를 구축하였다. 관개효율, 물꼬 높이, 침투량 등 총 6개의 매개변수에 대해 민감도 분석, 보정 및 검정을 수행하였다. 민감도 분석 결과, 관개효율이 가장 민감한 매개변수로 나타났다. 관개효율은 가장 민감하게 나타난 점과 연마다 값이 달라지는 특징을 반영하여 연별로 보정하였다. 통계적 지표 산정 결과 월단위에 대한 PBIAS, NSE, 그리고 RSR은 보정기간 동안 각각 2.7%, 0.93, 0.26로, 검정기간 동안 각각 3.9%, 0.89, 0.32로 매우 우수하게 나타났다. 따라서 비록 농업용수 공급량은 인위적 요소이나, 적절한 매개변수 값을 사용하여 모의한다면 모의치가 실측치와 유사하게 모의될 수 있을 것이다. 하지만 대상지구의 실측 자료가 확보되지 않아 보정되지 않은 매개변수를 사용하는 경우 결과가 매우 안 좋을 수 있을 가능성이 나타났다. 따라서 농업용수 공급량의 모의 시 적절한 매개변수의 선정은 매우 중요할 것으로 사료되며, 특히 관개효율은 연별로 보정하는 것을 제안한다.

The objective of this study was to predict land use change based on the land use change scenarios for the Hwangguji river watershed, South Korea. The land use change scenario was derived from the representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The CLUE (conversion of land use and its effects) model was used to simulate the land use change. The CLUE is the modeling framework to simulate land use change considering empirically quantified relations between land use types and socioeconomic and biophysical driving factors through dynamical modeling. The Hwangguji river watershed, South Korea was selected as study area. Future land use changes in 2040, 2070, and 2100 were analyzed relative to baseline (2010) under the RCP4.5 and 8.5 scenarios. Binary logistic regressions were carried out to identify the relation between land uses and its driving factors. CN (Curve number) and impervious area based on the RCP4.5 and 8.5 scenarios were calculated and analyzed using the results of future land use changes. The land use change simulation of the RCP4.5 scenario resulted that the area of urban was forecast to increase by 12% and the area of forest was estimated to decrease by 16% between 2010 and 2100. The land use change simulation of the RCP8.5 scenario resulted that the area of urban was forecast to increase by 16% and the area of forest was estimated to decrease by 18% between 2010 and 2100. The values of Kappa and multiple resolution procedure were calculated as 0.61 and 74.03%. CN (Ⅲ) and impervious area were increased by 0-1 and 0-8% from 2010 to 2100, respectively. The study findings may provide a useful tool for estimating the future land use change, which is an important factor for the future extreme flood.

The objective of this study was to develop agricultural reservoir water supply simulation system to assess water cycle of agricultural water district. Developed system was named as ARWS (Agricultural Reservoir Water supply simulation System). ARWS consists of platform and independent modules. In ARWS, reservoir inflow was calculated using Tank model, and agricultural water supply was calculated considering current farming period and mid-summer drainage. ARWS was applied to simulate water level of Gopung and Tapjung reservoir in 2011 - 2012. The results were compared to simulation results of HOMWRS and observed data. Average R2, EI, RMSE of ARWS were 0.76, 0.46, 1.78 (m), average R2, EI, RMSE of HOMRWS were 0.88, -0.14, 2.37 (m) espectively. Considering statistical variances, water level simulation results of ARWS were more similar to observed data than HOMWRS. ARWS can be useful to estimate reservoir water supply and assess hydrological processes of agricultural water district.

The objective of this research was to develop a relational database management system(RDBMS) to collect, manage and analyze data on agricultural non-point source(NPS) pollution. The system consists of the relational database for agricultural NPS data and data process modules. The data process modules were composed of four sub-modules for data input, management, analysis, and output. The data collected from the watershed of the upper Cheongmi stream and Geunsam-Ri were used in this study. The database was constructed using Apache Derby with meteorological, hydrological, water quality, and soil characteristics. Agricultural NPS-Data Management System(ANPS-DMS) was developed using Oracle Java. The system developed in this study can deal with a variety of agricultural NPS data and is expected to provide an appropriate data management tool for agricultural NPS studies.