• Wind and Structures
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• v.31 no.4
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• pp.299-309
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• 2020

In this study, wind data such as speeds, loads and potential of Muğla which is located in the southwest of Turkey were statistically analyzed. The wind data which consists of hourly wind speed between 2010 and 2013 years, was measured at the 10-meters height in four different ground stations (Datça, Fethiye, Marmaris, Köyceğiz). These stations are operated by The Turkish State Meteorological Service (T.S.M.S). Furthermore, wind data was analyzed by using Log-Normal and Gamma distributions, since these distributions fit better than Weibull, Normal, Exponential and Logistic distributions. Root Mean Squared Error (RMSE) and the coefficients of the goodness of fit (R²) were also determined by using statistical analysis. According to the results, extreme wind speed in the research area was 33 m/s at the Datça station. The effective wind load at this speed is 0.68 kN/㎡. The highest mean power densities for Datça, Fethiye, Marmaris and Köyceğiz were found to be 46.2, 1.6, 6.5 and 2.2 W/㎡, respectively. Also, although Log-normal distribution exhibited a good performance i.e., lower AD (Anderson - Darling statistic (AD) values) values, Gamma distribution was found more suitable in the estimation of wind speed and power of the region.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.779-789
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• 1999

To support daily ground-level $O_3$ forecasting in Seoul, a transfer function model(TFM) has been developed by using surface meteorological data and pollutant data(previous-day [$O_3$] and [$NO_2$]) from 1 May to 31 August in 1997. The forecast performance of the TFM was evaluated by statistical comparison with $O_3$ concentration observed during September it is shown that correlation coefficient(R), root mean squared error(RMSE), normalized mean squared error(NMSE) and mean relative error(MRE) were 0.73, 15.64, 0.006 and 0.101, respectively. The TFM appeared to have some difficulty forecasting very high $O_3$ concentrations. To compare with this model, multiple regression model(MRM) was developed for the same period. According to statistical comparison between the TFM and MRM. two models had similar predictive capability but TFM based on $O_3$ concentration higher than 60 ppb provided more accurate forecast than MRM. It was concluded that statistical model based on TFM can be useful for improving the accuracy of local $O_3$ forecast.

• Environmental Engineering Research
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• v.25 no.4
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• pp.515-521
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• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

• Journal of Biosystems Engineering
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• v.41 no.3
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• pp.232-239
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• 2016

Purpose: The aims of this study were to define the drying characteristics of rapeseed and to determine the optimum thin-layer drying model for rapeseed by considering the effects of drying temperature and relative humidity. Methods: The thin-layer drying experiments were conducted at different combinations of drying air temperature levels of 40, 50, and $60^{\circ}C$ and relative humidity levels of 30, 45, and 60%, on both of which drying rate depends. The drying rate increased with increasing air temperature as well as decreasing relative humidity. The 13 models were fitted to the experimental data. Results: From the results of the regression analysis for empirical constants of the Page model, the values of $R^2$ were the highest (ranging from 0.9924 to 0.9966) and the values of RMSE were the lowest (ranging from 0.0169 to 0.0296). Conclusions: For all drying conditions considered, the Page model was determined to be the most suitable model for describing the thin-layer drying of rapeseed (P-value < 0.01). The moisture diffusion coefficients were calculated using the moisture diffusion equation for a spherical shape, based on Fick's second law.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.153-155
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• 2007

Korea Meteorological Administration (KMA) has operationally produced Atmospheric Motion Vector (AMV) from the consecutive MTSAT-1R satellite image dataset. Comparing with radiosonde data, our current AMV scheme shows more than 10 m/s RMSE. Therefore we need to improve continuously its accuracy. Many AMV producers have stated that the bad performance of the Height Assignment (HA) algorithm is the main reason of degrading the accuracy of AMV. The uncertainties in AMV HA can occur in the algorithm itself, used NWP profiles, and the performance of Radiative Transfer Model (RTM) etc. This study introduces currently operated AMV HA schemes and the impacts of NWP profile data and RTM that these schemes use were investigated. Finally we analyzed the relationship between vectors by vector tracking and heights assigned to each vector by using collocated wind profile dataset with radiosonde data. This study is a preliminary work to improve the accuracy of AMV by removing or decreasing the uncertainties in AMV estimation.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.3
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• pp.1-8
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• 2009

The objectives of this study are to apply univariate search and genetic algorithm to tank model, and compare the two optimization methods. Hydrologic data of Baran watershed during 1996 and 1997 were used for correction the tank model, and the data of 1999 to 2000 were used for validation. RMSE and R2 were used for the tank model's optimization. Genetic algorithm showed better result than univariate search. Genetic algorithm converges to general optima, and more population of potential solution made better result. Univariate search was easy to apply and simple but had a problem of convergence to local optima, and the problem was not solved although search the solution more minutely. Therefore, this study recommend genetic algorithm to optimize tank model rather than univariate search.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.6
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• pp.139-147
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• 2014

Forecasting salinity in an estuary reservoir is essential to promise irrigation water for the reclaimed land. The objective of the research was to assess salinity balance and its temporal and spatial variations in the Iwon estuary reservoir which has been issued by its high contents of salinity in spite of desalination process for four years. Seepage flows through the see dikes which could be one of possible reason of high salinity level of the reservoir was calculated based on the salinity balance in the reservoir, and used as input data for salinity modeling. A three-dimensional hydrodynamic model, Environmental Fluid Dynamics Code (EFDC), was used to simulate salinity level in the reservoir. The model was calibrated and validated based on weekly or biweekly observed salinity data from 2006 to 2010 in four different locations in the reservoir. The values of $R^2$, RMSE and RMAE between simulated and observed salinity were calculated as 0.70, 2.16 dS/m, and 1.72 dS/m for calibration period, and 0.89, 1.15 dS/m, and 0.89 dS/m for validation period, respectively, showing that simulation results was generally consistent with the observation data.

• Journal of Biosystems Engineering
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• v.37 no.6
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• pp.385-397
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• 2012

Purpose: Drying characteristics of sawdust was studied under batch mode using lab scale microwave dryer. The objective of this study was to investigate the effect of material load and microwave output power on drying characteristics of sawdust. Methods: Material load and microwave output power were varied from 23 to 186 g and 530 to 370 W respectively. Different kinetic models were tested to fit the drying rates of sawdust. Similarly, the activation energy was calculated by employing the Arrhenius equation. Results: The drying efficiency increased considerably, whereas the specific energy consumption significantly decreased with increase in material load and microwave output power. The cumulative energy efficiency increased by 9%, and the specific energy consumption decreased by 8% when the material load was increased from 23 to 186 g. The effective diffusivity increased with decrease in material load and increase in microwave output power. The previously published model gave the best fit for data points with $R^2$ and RMSE values of 0.999 and 0.01, respectively. Conclusions: The data obtained from this study could be used as a basis for modeling of large scale industrial microwave dryers for the pellet production.

• Journal of Environmental Impact Assessment
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• v.21 no.2
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• pp.239-246
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• 2012

Daily net radiation is essential for heat budget analysis for environmental impact assessment in the coastal zone and longwave radiation is an important element of net radiation because there is a significant exchange of radiant energy between the earth's surface and the atmosphere in the form of radiation at longer wavelengths. However, radiation data is not commonly available, and there has been no direct measurement for most areas where coastal environmental impact assessment is usually most needed. Often an empirical equation, e.g., Penman and FAO-24 formulae is used to estimate longwave radiation using temperature, humidity, and sunshine hour data but local calibration may be needed. In this study, local recalibration was performed to have best fit from a widely used longwave equation using the measured longwave radiation data in Korea Global Atmospheric Watch Center (KGAWC). The results shows recalibration can provided better performance AE=0.23($W/m^2$) and RMSE=14.73($W/m^2$). This study will contribute to improve the accuracy of the heat budget analysis in the coastal area.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.