• Journal of Korean Society of Environmental Engineers
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• v.28 no.3
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• pp.308-315
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• 2006

Since sewage characteristics are the most important factors that can affect the biological reactions in wastewater treatment plants, a detailed understanding on the characteristics and on-line measurement techniques of the influent sewage would play an important role in determining the appropriate control strategies. In this study, samples were taken at two hour intervals during 51 days from $1^{st}$ October to $21^{st}$ November 2005 from the influent gate of sewage treatment plant. Then the characteristics of sewage were investigated. It was found that the daily values of flow rate and concentrations of sewage components showed a defined profile. The highest and lowest peak values were observed during $11:00{\sim}13:00$ hours and $05:00{\sim}07:00$ hours, respectively. Also, it was shown that the concentrations of sewage components were strongly correlated with the absorbance measured at 300 nm of UV. Therefore, the objective of the paper is to develop on-line estimation technique of the concentration of each component in the sewage using accumulated profiles of sewage, absorbance, and flow rate which can be measured in real time. As a first step, regression analysis was performed using the absorbance and component concentration data. Then a neural network trained with the input of influent flow rate, absorbance, and inflow duration was used. Both methods showed remarkable accuracy in predicting the resulting concentrations of the individual components of the sewage. In case of using the neural network, the predicted value md of the measurement were 19.3 and 14.4 for TSS, 26.7 and 25.1 for TCOD, 5.4 and 4.1 for TN, and for TP, 0.45 to 0.39, respectively.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.281-286
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• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.53-68
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• 1998

Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• Clean Technology
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• v.28 no.1
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• pp.63-78
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• 2022

Electronics industrial wastewater treatment facilities release organic wastewaters containing high concentrations of organic pollutants and more than 20 toxic non-biodegradable pollutants. One of the major challenges of the fourth industrial revolution era for the electronics industry is how to treat electronics industrial wastewater efficiently. Therefore, it is necessary to develop an electronics industrial wastewater modeling technique that can evaluate the removal efficiency of organic pollutants, such as chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), and tetramethylammonium hydroxide (TMAH), by digital twinning an electronics industrial organic wastewater treatment facility in a cyber physical system (CPS). In this study, an electronics industrial wastewater activated sludge model (e-ASM) was developed based on the theoretical reaction rates for the removal mechanisms of electronics industrial wastewater considering the growth and decay of micro-organisms. The developed e-ASM can model complex biological removal mechanisms, such as the inhibition of nitrification micro-organisms by non-biodegradable organic pollutants including TMAH, as well as the oxidation, nitrification, and denitrification processes. The proposed e-ASM can be implemented as a Water Digital Twin for real electronics industrial wastewater treatment systems and be utilized for process modeling, effluent quality prediction, process selection, and design efficiency across varying influent characteristics on a CPS.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.