• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.706-714
• /
• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• Journal of Environmental Health Sciences
• /
• v.46 no.6
• /
• pp.636-645
• /
• 2020

Objectives: This study was performed to find phytoncide (monoterpene) emission characteristics and weather factors for application in prediction modeling for phytoncide weather services. Methods: From 2017 to 2019, one coniferous forest and one deciduous forest were selected to investigate the monthly emission characteristics and identify the correlation with weather factors. Research items were analyzed for 11 species known to be emitting the most monoterpenes. Results: Phytoncide (monoterpene) began to increase in April when trees were activated and continued to be released until November. The concentration range of monoterpene in deciduous forests was 0.0 to 427.4 ng/S㎥ and coniferous forests was 0.0 to 1,776.8 ng/S㎥. Phytoncide emission concentrations in deciduous forests were 20 to 90 percent of those in coniferous forests, and averaged 39 percent overall. The correlation between monoterpene and temperature was very close, with 0.835 for the broadleaf forest and 0.875 for the coniferous forest. Monoterpene and humidity were found to be 0.731 for the broadleaf forest and 0.681 for the coniferous forest, while wind speed showed a negative correlation of -0.482 and -0.424, respectively. Regression of temperature with phytoncide showed that the coefficient of determination (r2) was highly correlated with 0.75 for the broadleaf forest and 0.80 for the coniferous forest. Not only is phytoncide concentration affected by temperature, humidity, and wind speed, but also rainfall over the preceeding one to three days. Nearby rainfall on the day of sampling was found to have a direct effect on the physiological activities of the trees. Conclusions: Overall, if the values of monoterpene and temperature, humidity, and wind speed are used as basic factors, and rainfall from one to three days previous is replaced with complementary values, it is believed that the numerical analysis and modeling of daily and monthly phytoncide will be possible.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1119-1130
• /
• 2021

Urban flooding caused by localized heavy rainfall with unstable climate is constantly occurring, but a system that can predict spatial flood information with weather forecast has not been prepared yet. The worst flood situation in urban area can be occurred with difficulties of structural measures such as river levees, discharge capacity of urban sewage, storage basin of storm water, and pump facilities. However, identifying in advance the spatial flood information can have a decisive effect on minimizing flood damage. Therefore, this study presents a methodology that can predict the urban flood map in real-time by using rainfall data of the Korea Meteorological Administration (KMA), the results of two-dimensional flood analysis and random forest (RF) regression model. The Ujeong district in Ulsan metropolitan city, which the flood is frequently occurred, was selected for the study area. The RF regression model predicted the flood map corresponding to the 50 mm, 80 mm, and 110 mm rainfall events with 6-hours duration. And, the predicted results showed 63%, 80%, and 67% goodness of fit compared to the results of two-dimensional flood analysis model. It is judged that the suggested results of this study can be utilized as basic data for evacuation and response to urban flooding that occurs suddenly.

• Journal of Korean Society of Forest Science
• /
• v.98 no.3
• /
• pp.272-278
• /
• 2009

This study was conducted to develope calculation method of standard rainfall, which was used for predicting the outbreaking time of disaster by using Tank model, on warning and evacuation for soil sediment disaster. We investigate adeption possibility of developed method through comparing storage function method with Tank model. We calculated storage amount rainfall by storage function method and Tank model with 36 hillslope failures which have record on outbreaking time of disaster. The result in case of Sedimentary (quarternary period) showed that the difference of outbreaking time was 1.6 hour in case of tank model, but 3.2 hour in case of storage function method. In addition, the deviation of the peak storage were 7% in case of tank model, but 63% in case of storage function method. Total evacuation period was analyzed by using observed 5 years (1993-1997) rainfall data as well as each standard rainfalls which were determinated by two methods. The result showed that evacuation time by storage function method was about twice as many as that by tank model. Therefore, we concluded that calculation by tank model for predicting the outbreaking time of disaster was more useful and accurate than storage function method.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.1
• /
• pp.801-806
• /
• 2015

The incidence of flood damage by global climate change has increased recently. Because of the increased frequency of flooding in Korea, the technology of flood prediction and prevalence has developed mainly for large river watersheds. On the other hand, there is a limit on predicting flooding through the most present flood forecasting systems because local floods in small watersheds rise quite quickly with little or no advance warning. Therefore, this study estimated the flood warning rainfall using a flood forecasting model at the two alarm trigger points in the Suamcheon basin, which is an urban basin with backwater effects. The flood warning rainfall was estimated to be 25.4mm/120min ~ 78.8mm/120min for the low water alarm, and 68.5mm/120min ~ 140.7mm/120min for the high water alarm. The frequency of the flood warning rainfall is 3-years for the low water alarm, and 80-years for the high water alarm. The results of this analysis are expected to provide a basic database in forecasting local floods in urban watersheds. Nevertheless, more tests and implementations using a large number of watersheds will be needed for a practical flood warning or alert system in the future.

• The Journal of the Korea Contents Association
• /
• v.8 no.3
• /
• pp.252-263
• /
• 2008

This study was conducted to derive the optimal regionalization of the precipitation data which can be classified on the basis of climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. A total of 65 rain gauges were used to regional analysis of precipitation. Annual maximum series for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr were used for various statistical analyses. K-means clustering mettled is used to identify homogeneous regions all over the regions. Five homogeneous regions for the precipitation were classified by the K-means clustering. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the generalized extreme value (GEV) distribution among applied distributions. The regional and at-site parameters of the generalized extreme value distribution were estimated by the linear combination of the probability weighted moments, L-moment. The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE were computed and compared with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• Journal of Korea Water Resources Association
• /
• v.47 no.8
• /
• pp.671-684
• /
• 2014

Change point analysis is a efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, this fundamental information to change points to future rainfall data identified by reasonable detection skills can affect the prediction of flood and drought occurrence because well detected change points provide a key to resolve the non-stationary or inhomogeneous problem by climate change. Therefore, in this study, the comparative study to assess the performance of the 3 change point detection skills, cumulative sum (CUSUM) method, Bayesian change point (BCP) method, and segmentation by dynamic programming (DP) was performed. After assessment of the performance of the proposed detection skills using the 3 types of the synthetic series, the 2 reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in hydrological modeling considering inhomogeneity or nonstationarity.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.3
• /
• pp.76-92
• /
• 2018

This study aimed to establish database of rainfall inundation area by rainfall scenarios and conduct a real time prediction for urban flood mitigation. the data leaded model was developed for the mapping of inundated area with rainfall forecast data provided by korea meteorological agency. for the construction of data leaded model, 1d-2d modeling was applied to Gangnam area, where suffered from severe flooding event including september, 2010. 1d-2d analysis result agree with observed in term of flood depth. flood area and flood occurring report which maintained by NDMS(national disaster management system). The fitness ratio of the NDMS reporting point and 2D flood analysis results was revealed to be 69.5%. Flood forecast chart was created using pre-flooding database. It was analyzed to have 70.3% of fitness in case of flood forecast chart of 70mm, and 72.0% in case of 80mm flood forecast chart. Using the constructed pre-flood area database, it is possible to present flood forecast chart information with rainfall forecast, and it can be used to secure the leading time during flood predictions and warning.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.12
• /
• pp.737-746
• /
• 2019

Most landslides in the country are shallow failures triggered by intense rainfall. Many researchers have revealed the possibility of predicting shallow failure through the volumetric water content (VWC). This study examined how to determine shallow failure using the gradient characteristics of the volumetric water content. For this, flume experiments were conducted using weathered granite soil. To confirm the saturation state of the surface layer under a rainfall intensity of 30 and 50mm/hr, VWC sensors were installed at depths of 10 and 20 cm on the upper, middle and lower slope. The test results showed that a shallow failure determination using VWC could be applied limitedly according to the slope degree. In addition, the effective cumulative rainfall due to the rainfall infiltration velocity is considered the main factor for the failure time. The failure prediction using the gradient of the VWC depends on the installation location and depth of the sensor. According to the experimental data, the measured value at 20 cm below the slope was most effective. Therefore, an analysis method of VWC and the method of selecting the installation location confirmed through this study can provide important data for presenting the measurement criteria using VWC in the future.

• Korean Journal of Remote Sensing
• /
• v.30 no.5
• /
• pp.587-596
• /
• 2014

Soil moisture is one of the most important interests in hydrological response and the interaction between the land surface and atmosphere. Estimation of Antecedent Wetness Conditions (AWC) which is soil moisture condition prior to a rainfall in the basin should be considered for rainfall-runoff prediction. In this study, Soil Wetness Index (SWI), Antecedent Precipitation Index ($API_5$), remotely sensed Soil Moisture ($SM_{rs}$), and 5 days ground Soil Moisture ($SM_{g5}$) were selected to estimate the AWC at four study area in the Korean Peninsula. The remotely sensed soil moisture data were taken from the AMSR-E soil moisture archive. The maximum potential retention ($S_{obs}$) was obtained from direct runoff and rainfall using Soil Conservation Service-Curve Number (SCS-CN) method by rainfall data of 2011 for each study area. Results showed the great correlations between the maximum potential retention and SWI with a mean correlation coefficient which is equal to -0.73. The results of time length representing the time scale of soil moisture showed a gap from region to region. It was due to the differences of soil types and the characteristics of study area. Since the remotely sensed soil moisture has been proved as reasonable hydrological variables to predict a wetness in the basin, it should be continuously monitored.