• Journal of Korean Society on Water Environment
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• v.33 no.1
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• pp.40-50
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• 2017

We were interested in the long-term temporal and spatial variability trends of water quality. Trend tests such as the Seasonal and Regional Kendall tests and LOWESS (LOcally WEighted Scatter plot Smoother) have been recommended as outstanding tools for trend detection. In this study, we conducted four types of nonparametric trend tests (Seasonal and Regional Kendall tests, LOWESS, and flow-adjusted Seasonal Kendall). We aimed to identify water quality trends using the monthly data for five variables (BOD, COD, TN, TP, and flow) collected from 24 sites in the Nakdong River from August 2004 to December 2013. According to the Regional Kendall test, BOD, COD, and TN increased but TP decreased trend. The Seasonal Kendall test showed that BOD, TN, and TP remained constant at 62.5-83.3% of the sites. COD remained constant at 58.3% of the sites. LOWESS showed that TP gradually increased between 2007 and 2008, then decreased slowly at the Gumi, Geumhogang6, Daeam-1 and Milyanggang3 sites. BOD increased slightly between 2008 and 2009, and then decreased slowly at the Namgang4-1 site. Lastly, a flow-adjusted Seasonal Kendall test was conducted. There were different results between Seasonal Kendall and flow-adjusted Seasonal Kendall tests at 11 of the 24 sites. According to the results from six of the eleven sites, BOD increased at one site, showed no trends at three sited, and decreased at two sites. Each of COD, TN increased at two, one site. but TP decreased at two sites.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.513-523
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• 2000

The X-12-ARIMA program was utilized on the analysis of the time series trend on 76 Korean industrial activities data in order to ensure that the trading day effect adjustment as well as the seasonal effect adjustment is needed to extract the fundamental trend-cycle factors from various economic time series data. The trading day effect is strongly correlated with the activity of production and shipping but not with the activity of inventory. Furthermore, the industrial activities were classified with respect to the sensitivity on the tranding day effect.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.153-162
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• 2015

In this study, we analyzed the trends of water quality along the main stream in Nakdong river basin using the recent data and seasonal Mann-Kendall test. Monthly averaged values of DO, BOD, SS, COD, TN, and TP from 1989 to 2014 for 14 stations (including 2 TMDLs stations) were used in the study. The trend analysis results showed that BOD and TP at most stations has decreasing temporal trend except a few stations while COD and SS showed increasing trend at most stations. Temporal trends in TN at 8 stations were found to be statistically significant and 5 of them showed increasing temporal trend. Temporally averaged BOD, COD, TN and TP were generally increasing as going downstream and the worst water quality were found at Goryeong and Hyunpung station. Overall, water quality of Nakdong river especially in COD, SS, and TN getting worse in time at most stations and as going downstream.

• Journal of the Korean Geographical Society
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• v.46 no.2
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• pp.152-167
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• 2011

This study aims to investigate the change of seasonal trend appeared in the daily normals of wintertime daily mean temperature of Seoul for 1941~1970 and 1971~2000 and the factors to affect it. The lowest temperature in wintertime is appeared in the period of the first and second ten-days of January in the daily normals for 1941~1970 and in the third ten-days of January and the first ten-days of February for 1971~2000. This means seasonal trend was changed. This change is due to the fact average temperature from 27 December to 20 January is rising much more than the wintertime mean temperature and average temperature from 21 January to 9 February less than that for two daily normals. This features are notable after 1971. The Siberian high and norther wind around the Korean Peninsula are weakened remarkably recently, so mean temperature of Seoul from 27 December to 20 January is warming much more. On the other hand, the Siberian high from 21 January to 9 February is weakened and the Aleutian low is strengthened recently and northerly is not change obviously, so temperature of Seoul is not warming so much.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Journal of Korean Society on Water Environment
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• v.24 no.4
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• pp.480-487
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• 2008

Spatial and temporal analyses of water qualities were performed for 11 monitoring stations located in Mangyung watershed in order to analyze the trends of monthly water quality data of Biochemical Oxygen Demand (BOD), Total Nitrogen (TN) and Total Phosphorus (TP) measured from 1995 to 2004. The long-term trends were analyzed utilizing Seasonal Mann-Kendall test, LOWESS and three-dimensional graphs were constructed with respect to distance and time. The graph can visualize spatial and temporal trend of the long-term water quality in a large river system. The results of trend analysis indicated that water quality of BOD and TN showed the downward trend. This quantitive and quantitative analysis is the useful tool to analyze and display the long-term trend of water quality in a large river system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.183-189
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• 2024

In order to effectively predict time series data, this study proposed a hybrid prediction model that decomposes the data into trend, seasonality, and residual components using Seasonal-Trend Decomposition on Loess, and then applies ARIMA to the trend component, Fourier Series Regression to the seasonality component, and XGBoost to the remaining components. In addition, performance comparison experiments including ARIMA, XGBoost, LSTM, EMD-ARIMA, and CEEMDAN-LSTM models were conducted to evaluate the prediction performance of each model. The experimental results show that the proposed hybrid model outperforms the existing single models with the best performance indicator values in MAPE(3.8%), MAAPE(3.5%), and RMSE(0.35) metrics.

• Journal of Environmental Science International
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• v.25 no.2
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• pp.295-310
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• 2016

In this study, changes in daily temperature range were investigated using daily maximum and minimum temperatures of Busan and Daegu for last 81 years (1934-2014), and also characteristics of daily temperature range and seasonal fluctuations by urbanization were examined. First, elapsing changes showed a lower decreasing trend in Busan ($0.32^{\circ}C$) than Daegu ($1.28^{\circ}C$) for last 81 years. Daily temperature range showed the highest rise in winter in both Busan and Daegu. Second, daily temperature range due to urbanization showed that Busan had a pronounced decreasing trend before urbanization meanwhile Daegu showed the same trend after urbanization. On seasonal changes, the results of Busan showed a decreasing trend in summer before urbanization, and in autumn after urbanization. For Daegu, the results showed a decreasing trend in spring before urbanization, and in winter after urbanization. Seasonal fluctuations of Busan showed little difference in the pre and post-urbanization, except in winter and summer. There was large difference in daily temperature range in winter after urbanization, and in summer before the urbanization. The results in Daegu showed that there was decreasing trend of daily temperature range in all seasons after urbanization.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.183-183
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• 2017

Pan evaporation (Epan) is an important indicator of water and energy balance. Despite global warming, decreasing annual Epan has been reported across different continents over last decades, which is claimed as pan evaporation paradox. However, such trend is not necessarily found in seasonal data because the level of contributions on Epan vary among meteorological components. This study investigates long-term trend in seasonal pan evaporation from 1908 to 2016 across South Korea. Meteorological variables including air temperature (Tair), wind speed (U), vapor pressure deficit (VPD), and solar radiation (Rs) are selected to quantify the effects of individual contributing factor to Epan. We found overall decreasing trend in Epan, which agrees with earlier studies. However, mixed tendencies between seasons due to variation of dominant factor contributing Epan were found. We also evaluated the reference evapotranspiration based on Penman-Monteith method and compared this with Epan to better understand the physics behind the evaporation paradox.