• International Journal of Contents
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• v.13 no.4
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• pp.23-28
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• 2017

Of the total economic loss caused by disasters, 40% are due to floods and floods have a severe impact on human health and life. So, it is important to monitor the water level of a river and to issue a flood warning during unfavorable circumstances. In this paper, we propose a modified error function to improve a hydrological modeling using a multi-layer perceptron (MLP) neural network. When MLP's are trained to minimize the conventional mean-squared error function, the prediction performance is poor because MLP's are highly tunned to training data. Our goal is achieved by preventing overspecialization to training data, which is the main reason for performance degradation for rare or test data. Based on the modified error function, an MLP is trained to predict the water level with rainfall data at upper reaches. Through simulations to predict the water level of Nakdong River near a UNESCO World Heritage Site "Hahoe Village," we verified that the prediction performance of MLP with the modified error function is superior to that with the conventional mean-squared error function, especially maximum error of 40.85cm vs. 55.51cm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• Journal of The Geomorphological Association of Korea
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• v.27 no.1
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• pp.61-89
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• 2020

The objective of this study is to characterize landslide susceptibility depending on various geo-environmental variables as well as to compare the Frequency Ratio (FR) and Evidential Belief Function (EBF) methods for landslide susceptibility analysis of rainfall-induced landslides. In 2013, a total of 259 landslides occurred in Chuncheon, Gangwon Province, South Korea, due to heavy rainfall events with a total cumulative rainfall of 296~721mm in 106~231 hours duration. Landslides data were mapped with better accuracy using the geographic information system (ArcGIS 10.6 version) based on the historic landslide records in Chuncheon from the National Disaster Management System (NDMS), the 2013 landslide investigation report, orthographic images, and aerial photographs. Then the landslides were randomly split into a testing dataset (70%; 181 landslides) and validation dataset (30%; 78 landslides). First, geo-environmental variables were analyzed by using FR and EBF functions for the full data. The most significant factors related to landslides were altitude (100~200m), slope (15~25°), concave plan curvature, high SPI, young timber age, loose timber density, small timber diameter, artificial forests, coniferous forests, soil depth (50~100cm), very well-drained area, sandy loam soil and so on. Second, the landslide susceptibility index was calculated by using selected geo-environmental variables. The model fit and prediction performance were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under Curve (AUC) methods. The AUC values of both model fit and prediction performance were 80.5% and 76.3% for FR and 76.6% and 74.9% for EBF respectively. However, the landslide susceptibility index, with classes of 'very high' and 'high', was detected by 73.1% of landslides in the EBF model rather than the FR model (66.7%). Therefore, the EBF can be a promising method for spatial prediction of landslide occurrence, while the FR is still a powerful method for the landslide susceptibility mapping.

• Journal of Korean Society of Forest Science
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• v.84 no.4
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• pp.502-516
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• 1995

The objective of this study was to estimate throughfall, stemflow, interception loss and net rainfall in relation to rainfall interception, and to understand the factors affecting interception process at Pinus taeda stand and Pinus densiflora stand in the Research Forests of Seoul National University, located in Choosan, Kwangyang, Chollanamdo. 1. The gross rainfall during the period of field observation was 3,107.6mm(average 1,035.9mm/year). Most of the daily rainfall intensity was under 30mm, which was 90% in 1992, 81% in 1993 and 88% in 1994. 2. In this study the throughfall, stemflow, interception loss and net rainfall were expressed separately as a function of gross rainfall. The overall throughfall collected during the period of field observation was 2,432.5mm(78.3%) at Pinus taeda stand and 2,699.6mm at Pinus densiflora stand, out of total rainfall of 3107.6mm. The canopy storage capacity, which was determined by the prediction equation between gross rainfall and throughfall was 1.1mm at Pinus taeda stand and 1.3mm at Pinus densiflora stand. 3. The sums of stemflow from measurement of total rainfall at Pinus taeda stand and Pinus densiflora stand was 227.3mm(7.3%) and 62.7mm(2.0%), respectively. The minimum rainfall causing stemflow was estimated as 7.2mm at Pinus taeda stand and 1.9mm at Pinus densiflora stand. 4. Interception loss accounted for 447.8mm(14.4%) at Pinus taeda stand and 345.3mm(11.1%) at Pinus densiflorra stand. 5. Net rainfall was 2,659.8mm(85.6%) at Pinus taeda stand and 2,762.3mm(88.9%) at Pinus densiflora stand. 6. The rates of throughfall and stemflow increased with increasing the gross rainfall. However, the amounts of throughfall and the stemflow were constant above 30mm at Pinus taeda stand and 50mm at Pinus densiflora stand. The rates of interception loss decreased with increasing the gross rainfall. However, the amount of interception loss was constant above 50mm at Pinus taeda stand and Pinus densiflora stand.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.52-67
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• 2008

Recently, frequent occurrence of flash floods caused by climactic change has necessitated prompt and quantitative prediction of precipitation. In particular, the usability of rainfall radar that can carry out real-time observation and prediction of precipitation behavior has increased. Moreover, the use of distributed hydrological model that enables grid level analysis has increased for an efficient use of rainfall radar that provides grid data at 1km resolution. The use of distributed hydrologic model necessitates grid-type spatial data about target basins; to enhance reliability of flood runoff simulation, the use of visible and precise data is necessary. In this paper, physically based $Vflo^{TM}$ model and ModClark, a quasi-distributed hydrological model, were used to carry out flood runoff simulation and comparison of simulation results with data from Imjin River Basin, two-third of which is ungauged. The spatial scope of this study was divided into the whole Imjin River basin area, which includes ungauged area, and Imjin River basin area in South Korea for which relatively accurate and visible data are available. Peak flow and lag time outputs from the two simulations of each region were compared to analyze the impact of uncertainty in topographical parameters and soil parameters on flood runoff simulation and to propose effective methods for flood runoff simulation in ungauged regions.

• Atmosphere
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• v.19 no.1
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• pp.37-51
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• 2009

Kinematic characteristics of a heavy rainfall event occurred in Changma front are analyzed using synoptic weather charts, satellite imagery and NCEP(National Centers for Environmental Prediction) / NCAR(National Centers for Atmospheric Research) reanalysis data. The heavy rainfall is accompanied with mesoscale rain clouds developing over the Southwest region of Korea during the period from 0300 LST to 2100 LST 25 June 2006. The surface cyclone in the Changma front is generated and developed rapidly when it meets following vertical conditions: The maximum value of relative vorticity is appeared at 700 hPa and is extended gradually near the surface. It is thought that the vertical structure of relative vorticity is closely related with the descent of strong wind zone exceeding $10ms^{-1}$. The jet core at 200 hPa is shifted southward and extended downward and the low-level jet stream associated with upper-level jet stream appeared at 850 hPa. Kinematic features of heavy rainfall system at cyclone-generating point are as follows: In the generating stage of cyclone, the relative vorticity below 850 hPa increased and the convergence below 850 hPa and the divergence at 400 hPa are intensified by southward movement of jet core at 200 hPa. The heavy rainfall system seems to locate to the south of the exit region of upper-level jet streak; In the developing stage of cyclone, the relative vorticity below 850 hPa and the convergence near surface are further strengthened and upward vertical velocity between 850 hPa and 200 hPa is increased.

• Journal of Korea Water Resources Association
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• v.50 no.5
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• pp.335-346
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• 2017

This study generated the radar-based forecasted rainfall using spatial-scale decomposition method (SCDM) and evaluated the hydrological applicability with forecasted rainfall by KMA (MAPLE, KONOS) in terms of urban flood forecasting. SCDM is to separate the small-scale field (convective cell) and large-scale field (straitform cell) from radar rainfield. And each separated field is forecasted by translation model and storm tracker nowcasting model for improvement of QPF accuracy. As the evaluated results of various QPF for three rainfall events in Seoul and Metropolitan area, proposed method showed better prediction accuracy than MAPLE and KONOS considering the simplicity of the methodology. In addition, this study assessed the urban hydrological applicability for Gangnam basin. As the results, KONOS simulated the peak of water depth more accurately than MAPLE and SCDM, however cannot simulated the timeseries pattern of water depth. In the case of SCDM, the quantitative error was larger than observed water depth, but the simulated pattern was similar to observation. The SCDM will be useful information for flood forecasting if quantitative accuracy is improved through the adjustment technique and blending with NWP.

• Journal of Korea Water Resources Association
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• v.43 no.7
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• pp.645-655
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• 2010

In this study, occurrences of relative probabilistic changing points between Chukwooki rainfall data (CWK) and modern rain gage data (MRG) were analyzed using Barry and Hartigan (BH) Bayesian changing points estimation method which estimated the changing points by calculation of change probabilities at each point. Since any natural phenomenon cannot be simulated identically and perfectly, a statistical method which can not consider the sequential order has its limitation on prediction of a specific time of occurrence. In this respect, Homogeneity analysis between CWK and MRG was performed through the occurrence investigation of relative probabilistic changing points for four rainfall characteristics of data sets using BH bayesian model which estimate the change point by calculating the relative probabilities in each data points. The results show that statistical characteristics of CWK are not different significantly from MRG, even though considered that there may be little quantitative difference CWK and MRG caused from limitation of measurement accuracy of CWK.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.71-79
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• 2017

Climate change phenomenon is posing a serious threat to sustainable corn production in Ghana. This study investigated the impacts of climate change on the rain-fed corn yield in the Dangme East district, Ghana by using Aquacrop model with a daily weather data set of 22-year from 1992 to 2013. Analysis of the weather data showed that the area is facing a warming trend as the numbers of years hotter and drier than the normal seemed to be increasing. Aquacrop model was assessed using the limited observed data to verify model's sufficiency, and showed credible results of $R^2$ and Nash-Sutcliffe efficiency (NSE). In order to simulate the corn yield response to climate variability four climate change scenarios were designed by varying long-term average temperature in the range of ${\pm}1^{\circ}C{\sim}{\pm}3^{\circ}C$ and average annual rainfall to ${\pm}5%{\sim}{\pm}30%$, respectively. Generally, the corn yield was negatively correlated to temperature rise and rainfall reduction. Rainfall variations showed more prominent impacts on the corn yield than that of temperature variations. The reduction in average rainfall would instantly limit the crop growth rate and the corn yield irrespective of the temperature variations.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.381-393
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• 2021

As part of the National Disaster Management Research Institute's Official Development Assistance (ODA) projects for transferring new technologies in the field of disaster-safety management, a flood forecasting and warning system was established in 2019 targeting the Borikhan in the Namxan River Basin in Bolikhamxai Province, Laos. In the target area, which is an ungauged small and medium river basin, observation stations for real-time monitoring of rainfall and runoff and alarm stations were installed, and a software that performs real-time data management and flood forecasting and warning functions was also developed. In order to establish a flood warning standard and develop a nomograph for flood prediction, hydraulic and hydrological analysis was performed based on the 30-year annual maximum daily rainfall data and river morphology survey results in the target area. This paper introduces the process and methodology used in this study, and presents the results of the system's applicability review based on the data observed and collected in 2020 after system installation.