• Entomological Research
• /
• v.48 no.6
• /
• pp.540-549
• /
• 2018

Climate change due to global warming and changes in land use increase the development time and distribution, as well as the abundance, of mosquitoes, thereby negatively affecting human life and health. In this study, we investigated the habitat characteristics of mosquito occurrence sites in Danwon-gu, Ansan city, Korea, based on a daily record of civil pest complaints lodged at the Danwon-gu Community Health Center. We considered two types of factors (natural and artificial) known to affect mosquito occurrence. We confirmed a total of 554 cases of mosquito occurrence from March 2014 to August 2017. The total study area was $49.11km^2$, with urban areas constituting the largest proportion. Additionally, we investigated habitat preferences of mosquitoes in terms of terrain factors; generally, mosquitoes occurred at low elevations and on low slopes. Regarding the preference of oviposition sites, mosquitoes occurred at higher rates near streams and crops lands. The rate of mosquito occurrence tended to increase in areas with dense human populations. Regarding climatic factors, the rate of mosquito occurrence increased in areas with high temperature and low precipitation.

• Journal of The Geomorphological Association of Korea
• /
• v.23 no.1
• /
• pp.77-85
• /
• 2016

To understand the difference of runoff discharge processes between Gwangneung deciduous and coniferous forest catchments, we collected hydrological data (e.g., precipitation, soil moisture, runoff discharge) and conducted hydrochemical analyses in the deciduous and coniferous forest catchments in Gwangneung National Arboretum in the northwest part of South Korea. Based on the end-member mixing analysis of the three storm events during the summer monsoon in 2005, the hillslope runoff in the deciduous forest catchment was higher 20% than the coniferousforest catchment during the firststorm event. Howerver, hillslope runoff increased from the second storm event in the coniferous catchment. We conclude that low soil water contents and topographical gradient characteristics highly influence runoff in the coniferous forest catchment during the first storm events. In general, coniferous forests are shown high interception loss and low soil moisture compared to the deciduous forests. It may also be more likely to be a reduction in soil porosity development when artificial coniferous forests reduced soil biodiversity. The forest soil porosity is an important indicator to determine the water recharge of the forest. Therefore, in order to secure the water resources, it should be managed coniferous forests for improving soil biodiversity and porosity.

• Journal of Korea Water Resources Association
• /
• v.54 no.10
• /
• pp.795-805
• /
• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.23 no.3
• /
• pp.26-67
• /
• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.34 no.3
• /
• pp.220-225
• /
• 2014

Generally, the nonlinearity of ultrasonic waves is measured using a nonlinear parameter ${\beta}$, which is defined as the ratio of the second harmonic's magnitude to the power of the fundamental frequency component after the ultrasonic wave propagates through a material. Nonlinear parameter ${\beta}$ is recognized as an effective parameter for evaluating material degradation. In this paper, we evaluated the nonlinear parameter of Al6061-T6 which had been subjected to an artificial aging heat treatment. The measurement was using the transmitted signal obtained from contact-type transducers. After the ultrasonic test, a micro Vickers hardness test was conducted. From the result of the ultrasonic nonlinear parameter, the microstructural changes resulting from the heat treatment were estimated and the hardness test proved that these estimates were reasonable. Experimental results showed a correlation between the ultrasonic nonlinear parameter and microstructural changes produced by precipitation behavior in the material. These results suggest that the evaluation of mechanical properties using ultrasonic nonlinear parameter ${\beta}$ can be used to monitor variations in the mechanical hardness of aluminum alloys in response to an artificial aging heat-treatment.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.2B
• /
• pp.155-163
• /
• 2011

In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.

• Journal of Korea Water Resources Association
• /
• v.50 no.10
• /
• pp.681-690
• /
• 2017

This study estimated the daily maximum snow depth using the Artificial Neural Network (ANN) model in Korean Peninsula. First, the optimal ANN model structure was determined through the trial-and-error approach. As a result, daily precipitation, daily mean temperature, and daily minimum temperature were chosen as the input data of the ANN. The number of hidden layer was set to 1 and the number of nodes in the hidden layer was set to 10. In case of using the observed value as the input data of the ANN model, the cross validation correlation coefficient was 0.87, which is higher than that of the case in which the daily maximum snow depth was spatially interpolated using the Ordinary Kriging method (0.40). In order to investigate the performance of the ANN model for estimating the daily maximum snow depth of the ungauged area, the input data of the ANN model was spatially interpolated using Ordinary Kriging. In this case, the correlation coefficient of 0.49 was obtained. The performance of the ANN model in mountainous areas above 200m above sea level was found to be somewhat lower than that in the rest of the study area. This result of this study implies that the ANN model can be used effectively for the accurate and immediate estimation of the maximum snow depth over the whole country.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1131-1141
• /
• 2021

In this study, the artificial neural network model is applied for real-time dam inflow prediction and then evaluated for the prediction lead times (1, 3, 6 hr) in dam basins in Korea. For the training and testing the model, hourly precipitation and inflow are used as input data according to average annual inflow. The results show that the model performance for up to 6 hour is acceptable because the NSE is 0.57 to 0.79 or higher. Totally, the predictive performance of the model in dry seasons is weaker than the performance in wet seasons, and this difference in performance increases in the larger basin. For the 6 hour prediction lead time, the model performance changes as the sequence length increases. These changes are significant for the dry season with increasing sequence length compared to the wet season. Also, with increasing the sequence length, the prediction performance of the model improved during the dry season. Comparison of observed and predicted hydrographs for flood events showed that although the shape of the prediction hydrograph is similar to the observed hydrograph, the peak flow tends to be underestimated and the peak time is delayed depending on the prediction lead time.

• Ecology and Resilient Infrastructure
• /
• v.10 no.4
• /
• pp.161-170
• /
• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• Journal of Korea Water Resources Association
• /
• v.53 no.4
• /
• pp.273-283
• /
• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.