• Journal of Environmental Science International
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• v.26 no.10
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• pp.1135-1146
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• 2017

At riverbank filtration sites, groundwater levels of alluvial aquifers near rivers are sensitive to variation in river discharge and pumping quantities. In this study, the groundwater level fluctuation, pumping quantity, and streamflow rate at the site of a riverbank filtration plant, which produces drinking water, in the lower Nakdong River basin, South Korea were interrelated. The relationship between drawdown ratio and river discharge was very strong with a correlation coefficient of 0.96, showing a greater drawdown ratio in the wet season than in the dry season. Autocorrelation and cross-correlation were carried out to characterize groundwater level fluctuation. Autoregressive model analysis of groundwater water level fluctuation led to efficient estimation and prediction of pumping for riverbank filtration in relation to river discharge rates, using simple inputs of river discharge and pumping data, without the need for numerical models that require data regarding several aquifer properties and hydrologic parameters.

• Smart Structures and Systems
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• v.10 no.6
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• pp.517-546
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• 2012

In this paper, vibration-based methods to monitor damage in foundation-structure interface of harbor caisson structure are presented. The following approaches are implemented to achieve the objective. Firstly, vibration-based damage monitoring methods utilizing a variety of vibration features are selected for harbor caisson structure. Autoregressive (AR) model for time-series analysis and power spectral density (PSD) for frequency-domain analysis are selected to detect the change in the caisson structure. Also, the changes in modal parameters such as natural frequency and mode shape are examined for damage monitoring in the structure. Secondly, the feasibility of damage monitoring methods is experimentally examined on an un-submerged lab-scaled mono-caisson. Finally, numerical analysis of un-submerged mono-caisson, submerged mono-caisson and un-submerged interlocked multiple-caissons are carried out to examine the effect of boundary-dependent parameters on the damage monitoring of harbor caisson structures.

• Asian Journal of Business Environment
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• v.11 no.1
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• pp.39-50
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• 2021

Purpose: This study seeks to establish the environmental damage theory applicable to Kenya. The analysis is based on annual data drawn from World Bank on carbon dioxide emissions (CO2e) and gross domestic product per capita (GDPPC) for Kenya spanning 1963 to 2017. Research Methodology: The study adopts explanatory research design and autoregressive distributed lag model for analysis. Results: The results revealed a coefficient of -0.017 for GDPPC and 0.004 for GDPPC squared indicating that economic growth has negative effect on CO2e in the initial stages of growth but positive effect in the high growth regime with the marginal effect being higher in the initial growth regime. The findings suggest a U-shaped relationship consistent with Brundtland Curve Hypothesis (BCH). Conclusions: The findings emphasize the need for sustainable development path that enables present generations to meet own needs without compromising the capacity of future generations to meet their own. Sustainable development may include, investment in renewable energies like wind, solar and adoption of energy efficient technologies in production and manufacturing. The study concludes that BCH is applicable to Kenya and that developing affordable and effective mechanisms to boost sustainable development implementation is necessary to decrease the anthropogenic impact in the environment without any attendant reduction in the economic growth.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.49-59
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• 2022

This paper examined the impact of real exchange rate volatility on trade balance in Malaysia by using quarterly data from year 2000 until 2019. Generalized Autoregressive Heteroscedasticity (GARCH) model was used to extract the volatility component of real exchange rate before examining its impact on trade balance. Furthermore, Autoregressive Distributed Lag (ARDL) model was used to investigate the long-run relationship and short-run dynamic between trade balance, money supply, national income and volatility of exchange rate. Empirical results show the existence of co-movement between variables under study in the long-run. However, the results also suggest that volatility of real exchange rate does not significantly affect trade balance neither in the long-run nor short-run. The risk which is associated in the movement of exchange rate do not influence trader's behaviour toward Malaysia exports and imports. Thus, it should be note that any depreciation or appreciation in Malaysian Ringgit do not have an impact towards trade balance either it is being further improved or deteriorates. Hence, exchange rate volatility may not be too concern for policymakers. This may be partially due to manage floating exchange rate regime that has been adopted by Malaysia eventually eliminated the element of risk in the currency market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.1-9
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• 2021

This paper investigates the impact of the domestic and global outbreak of the coronavirus (COVID-19) pandemic on the trading size of the Malaysian stock (MS) market. The theoretical model posits that stock markets are affected by their response to disasters and events that arise in the international or local environments, as well as to several financial factors such as stock volatility and spread bid-ask prices. Using daily time-series data from 27 January to 12 May 2020, this paper utilizes the traditional Augmented Dickey and Fuller (ADF) technique and Zivot and Andrews with structural break' procedures for a stationarity test analysis, while the autoregressive distributed lag (ARDL) method is applied according to the trading size of the MS market model. The analysis considered almost all 789 listed companies investing in the main stock market of Malaysia. The results confirmed our hypotheses that both the daily growth in the active domestic and global cases of coronavirus (COVID-19) has significant negative effects on the daily trading size of the stock market in Malaysia. Although the COVID-19 has a negative effect on the Malaysian stock market, the findings of this study suggest that the COVID-19 pandemic may have an asymmetric effect on the market.

• Current Photovoltaic Research
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• v.10 no.1
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• pp.6-15
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• 2022

Climate change is among the most important issues facing mankind in modern society. However, global PV energy expansion has been driven mainly by OECD countries. We investigate the determinants of PV energy growth by panel data of selected OECD countries from 1991 to 2018. We investigate four categories of driving factors: socioeconomic, technological, country specific, and policy factors. The test results support that PV capacity growth is significantly driven by technology development and multidimensional environment policy factors. Socioeconomic factors such as CO₂, GDP, and electricity price are statistically significant on the growth of PV energy, too. Whereas, country-specific solar potential factor is the least related. As most of the socioeconomic factors are exogenous, we need to focus more on PV technology development and policy measures.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.887-896
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• 1994

It is common practice in the case of optimum reservoir operation model that the reservoir inflow series are generated by stochastic model with keeping other variable such as water demands from the reservoir constant. However, when the input and output of the water distribution system have close relationship the output variables can be stochastically generated in relation with the input variables. In the present study the reservoir inflow series, the input of the system, is generated by periodic autoregressive model with constant parameter, and the agricultural water demand series, the output, is generated using periodic multivariate autoregressive model with constant parameter. The time period of the data series generated is taken as 10-day which is the common period used for agricultural water uses. The results of data generation by two different models showed that the periodic stochastic models well represent the characteristics of the historical time series, and that in the case of generating model for agricultural demand series it has closer relation with reservoir inflow than with the series itself.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.303-313
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• 2018

This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.