• Economic and Environmental Geology
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• v.48 no.6
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• pp.509-523
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• 2015

This study introduces newly implemented geological well logs database for Jeju public water wells, built for a research project focusing on integrated hydrogeology database of Jeju Island. A detailed analysis of the existing 1,200 Jeju Island geological logs for the public wells developed since 1970 revealed six major indications to be improved for their use in Jeju geological logs DB construction: (1) lack of uniformity in rock name classification, (2) poor definitions of pyroclastic deposits and sand and gravel layers, (3) lack of well borehole aquifer information, (4) lack of information on well screen installation in many water wells, (5) differences by person in geological logging descriptions. A new Jeju geological logs DB enabling standardized input and output formats has been implemented to overcome the above indications by reestablishing the names of Jeju volcanic and sedimentary rocks and utilizing a commercial, database-based input structured, geological log program. The newly designed database structure in geological log program enables users to store a large number of geology, well drilling, and test data at the standardized DB input structure. Also, well borehole groundwater and aquifer test data can be easily added without modifying the existing database structure. Thus, the newly implemented geological logs DB could be a standardized DB for a large number of Jeju existing public wells and new wells to be developed in the future at Jeju Island. Also, the new geological logs DB will be a basis for ongoing project 'Developing GIS-based integrated interpretation system for Jeju Island hydrogeology'.

• Journal of Bio-Environment Control
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• v.29 no.4
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• pp.440-447
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• 2020

The demand for large-scale horticultural complexes utilizing reclaimed lands is increasing, and one of the pending issues for the construction of large-scale facilities is to establish foundation design criteria. In this paper, we tried to review previous studies on the method of reinforcing the foundation of soft ground. Target construction methods are spiral piles, wood piles, crushed stone piles and PF (point foundation) method. In order to evaluate the performance according to the basic construction method, pull-out resistance, bearing capacity, and settlement amount were measured. At the same diameter, pull-out resistance increased with increasing penetration depth. Simplified comparison is difficult due to the difference in reinforcement method, diameter, and penetration depth, but it showed high bearing capacity in the order of crushed stone pile, PF method, and wood pile foundation. In the case of wood piles, the increase in uplift resistance was different depending on the slenderness ratio. Wood, crushed stone pile and PF construction methods, which are foundation reinforcement works with a bearing capacity of 105 kN/㎡ to 826 kN/㎡, are considered sufficient methods to be applied to the greenhouse foundation. There was a limitation in grasping the consistent trend of each foundation reinforcement method through existing studies. If these data are supplemented through additional empirical tests, it is judged that a basic design guideline that can satisfy the structure and economic efficiency of the greenhouse can be presented.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.