• Journal of Environmental Science International
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• v.30 no.12
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• pp.1053-1065
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• 2021

In this study, the prediction technology of Hydrological Quantitative Precipitation Forecast (HQPF) was improved by optimizing the weather predictors used as input data for machine learning. Results comparison was conducted using bias and Root Mean Square Error (RMSE), which are predictive accuracy verification indicators, based on the heavy rain case on August 21, 2021. By comparing the rainfall simulated using the improved HQPF and the observed accumulated rainfall, it was revealed that all HQPFs (conventional HQPF and improved HQPF 1 and HQPF 2) showed a decrease in rainfall as the lead time increased for the entire grid region. Hence, the difference from the observed rainfall increased. In the accumulated rainfall evaluation due to the reduction of input factors, compared to the existing HQPF, improved HQPF 1 and 2 predicted a larger accumulated rainfall. Furthermore, HQPF 2 used the lowest number of input factors and simulated more accumulated rainfall than that projected by conventional HQPF and HQPF 1. By improving the performance of conventional machine learning despite using lesser variables, the preprocessing period and model execution time can be reduced, thereby contributing to model optimization. As an additional advanced method of HQPF 1 and 2 mentioned above, a simulated analysis of the Local ENsemble prediction System (LENS) ensemble member and low pressure, one of the observed meteorological factors, was analyzed. Based on the results of this study, if we select for the positively performing ensemble members based on the heavy rain characteristics of Korea or apply additional weights differently for each ensemble member, the prediction accuracy is expected to increase.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.1
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• pp.57-65
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• 2007

Shallow water waves are hindcasted from sea wind fields, which include wave transformations such as shoaling, refraction, diffraction, reflection and wave breaking. In case of estimating sea wind field in shallow water, the sea wind revised from free wind obtained by the typhoon model is widely used. However, this method is not able to consider the effect of land topography on the wind field, which will be important factor for shallow water wave forecasting and hindcasting. In this study, therefore, the effect of land topography on sea wind field in shallow water is investigated for shallow water wave forecasting and hindcasting with high accuracy. The 3-D MASCON model is introduced to consider the influence of land topography on the wind field. And, for two areas divided by the topographical characteristics, i.e. shielded and opened coastal areas, sea wind field is examined by comparison between initial wind field by typhoon model and modified wind field by 3-D MASCON model. Finally, applying these sea wind fields to SWAN model, the results of shallow water wave calculated in shielded and opened coastal areas are compared, and, also, the effect of MASCON model on shallow water wave forecasting and hindcasting is discussed.

• Journal of Environmental Science International
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• v.29 no.1
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• pp.45-54
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• 2020

High-resolution meteorological simulations were conducted using a Weather Research and Forecasting (WRF) model with an Urban Canopy Model (UCM) in the Ulsan Metropolitan Region (UMR) where large-scale industrial facilities are located on the coast. We improved the land cover input data for the WRF-UCM by reclassifying the default urban category into four detailed areas (low and high-density residential areas, commercial areas, and industrial areas) using subdivided data (class 3) of the Environmental and Geographical Information System (EGIS). The urban area accounted for about 12% of the total UMR and the largest proportion (47.4%) was in the industrial area. Results from the WRF-UCM simulation in a summer episode with high temperatures showed that the modeled temperatures agreed greatly with the observations. Comparison with a standard WRF simulation (WRF-BASE) indicated that the temporal and spatial variations in surface air temperature in the UMR were properly captured. Specifically, the WRF-UCM reproduced daily maximum and nighttime variations in air temperature very well, indicating that our model can improve the accuracy of temperature simulation for a summer heatwave. However, the WRF-UCM somewhat overestimated wind speed in the UMR largely due to an increased air temperature gradient between land and sea.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

• Atmosphere
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• v.17 no.1
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• pp.87-99
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• 2007

Simulation results of Asian Dust Aerosol Model (ADAM) for the period of April 7-9, 2006 were analyzed, comparing with observed PM10 data. ADAM simulated around ten times lower than on-site PM10 concentration in the source regions: Zhurihe, Tongliao, Yushe, Dalian and Huimin. As the result of this low concentration, transported amounts of Asian Dust were under-estimated as well. In order to quantify a forecasting accuracy, Bias and RMSE were calculated. Even though remarkably negative Biases and high RMSEs were observed, ADAM simulation had followed well up the time of dust outbreak and a transported path. However, the emission process to generate dust from source regions requires a great enhancement. The PM10 concentration at the surface reached up to $2,300{\mu}gm^{-3}$ at Baeknyoungdo and Seoul (Mt. Gwanak), up to $1,750{\mu}gm^{-3}$ at KGAWO about 18:00 LST in April 8, respectively; however, ADAM did not simulate the same result on its second peak. It is considered that traveling Asian dust might have been lagged over the Korean peninsula by the blocking of surface high pressure. Moreover, the current RDAPS's 30 km grid resolution (which ADAM adopts as the meteorological input data) might not adequately represent small-scale atmospheric motions below planetary boundary layer.

• Advances in environmental research
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• v.4 no.4
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• pp.233-246
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• 2015

High amounts of air pollution in crowded urban areas are always considered as one of the major environmental challenges especially in developing countries. Despite the errors in air pollution prediction, the forecasting of future data helps air quality management make decisions promptly and properly. We studied the air quality of the Aqdasiyeh location in Tehran using factor analysis and the Box-Jenkins time series methods. The Air Quality Control Company (AQCC) of the Municipality of Tehran monitors seven daily air quality parameters, including carbon monoxide (CO), Nitrogen Monoxide (NO), Nitrogen dioxide ($NO_2$), $NO_x$, ozone ($O_3$), particulate matter ($PM_{10}$) and sulfur dioxide ($SO_2$). We applied the AQCC data for our study. According to the results of the factor analysis, the air quality parameters were divided into two factors. The first factor included CO, $NO_2$, NO, $NO_x$, and $O_3$, and the second was $SO_2$ and $PM_{10}$. Subsequently, the Box- Jenkins time series was applied to the two mentioned factors. The results of the statistical testing and comparison of the factor data with the predicted data indicated Auto Regressive Integrated Moving Average (0, 0, 1) was appropriate for the first factor, and ARIMA (1, 0, 1) was proper for the second one. The coefficient of determination between the factor data and the predicted data for both models were 0.98 and 0.983 which may indicate the accuracy of the models. The application of these methods could be beneficial for the reduction of developing numbers of mathematical modeling.

• Journal of the Korean earth science society
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• v.27 no.6
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• pp.677-686
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• 2006

Surface evapotranspiration is one of the most important factors to determine the surface energy budget, and its estimation is strongly related with the accuracy of weather forecasting. Surface evapotranspiration over Daegu Metropolitan was estimated using high resolution LANDSAT TM data. The estimation of surface evapotranspiration is based on the relationship between surface radiative temperature and vegetation index provided by a TM sensor. The distribution of NDVI (Normalized Difference of Vegetation Index) corresponds well with that of land-used in Deagu Metropolitan. The temperature of several part of downtown in Deagu metropolitan is lower in comparison with the averaged radiative temperature. This is caused by the high evapotranspiration from dense vegetation like DooRyu Park in Deagu Metropolitan. But, weak evapotranspiration availability is distinguished over the central part of downtown and the difference of evapotranspiration availability on industrial complexes and residential area is also clear.

• Geomechanics and Engineering
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• v.22 no.4
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• pp.291-303
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• 2020

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.

• Geomechanics and Engineering
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• v.23 no.4
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• pp.339-349
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• 2020

This study investigates the feasibility of using the results of the UPV (ultrasonic pulse velocity) test to assess the UCS (unconfined compressive strength) of unsaturated soil. A series of laboratory tests was conducted on samples of unsaturated lateritic soils of northern Taiwan. Specifically, the unconfined compressive test was combined with the pressure plate test to obtain the unconfined compressive strength and its matric suction (s) of the samples. Soil samples were first compacted at the designated water content and subsequently subjected to the wetting process for saturation and the following drying process to its target suction using the apparatus developed by the authors. The correlations among the UCS, s and UPV were studied. The test results show that both the UCS and UPV significantly increased with the matric suction regardless of the initial compaction condition, but neither the UCS nor UPV obviously varied when the matric suction was less than the air-entry value. In addition, the UCS approximately linearly increased with increasing UPV. According to the investigation of the test results, simplified methods to estimate the UCS using the UPV or matric suction were established. Furthermore, an empirical formula of the matric suction calculated from the UPV was proposed. From the comparison between the predicted values and the test results, the MAPE values of UCS were 4.52-9.98% and were less than 10%, and the MAPE value of matric suction was 17.3% and in the range of 10-20%. Thus, the established formulas have good forecasting accuracy and may be applied to the stability analysis of the unsaturated soil slope. However, further study is warranted for validation.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.6
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• pp.509-519
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• 2017

Crop simulation models are valuable tools for estimating crop yield, environmental factors and management practices. The objective of this study was to evaluate the effect of soil types on barley productivity using CERES (Crop Environment REsource Synthesis)-barley, cropping system model. So the behavior of the model under various soil types and climatic conditions was evaluated. The results of the sensitivity analysis in temperature, $CO_2$, and precipitation showed that soil types had a direct impact on the simulated yield of CERES-barley model. We found that barley yield in clay soils would be more sensitive to precipitation and $CO_2$ in comparison with temperature. And the model showed limited accuracy in simulating water and nitrogen stress index for soil types. In general, the barley grown on clay soils were less sensitive to water stress than those grown on sandy soils. Especially it was found that the CERES model underestimated the effect of water stress in high precipitation which led to overprediction of crop yield in clay soils. In order to solve these problems and successfully forecast grain yield, further studies on the modification of the water stress response of crops should be considered prior to use of the CERES-barley model for yield forecasting.