• Magazine of the Korean Society of Agricultural Engineers
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• v.19 no.3
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• pp.4472-4482
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• 1977

In order to estimate furture consumtive use, some statistical characteristics of 22-year pan evaporation data at four selected stations were calculated in this study. Districal distribution, trend analysis and time-series, statistical and periodic analysis for annual, monethly and ten-day values were performed in the statistical analysis. The stations are Seoul, Taeku, Jeonju and Mokpo for monthly data, and Suweon data are compared to the reported Penman values. The results are as followed: 1. Annual evaporation ranged to 990-1,375mm varying with the locations of the stations. The Districal distribution of evaporation in the Republic is shown in Fig. 1. 2. The trend analysis for annual evaporation resulted in detail in Table 2 and Fig. 2, through simple moving average methods. The results show relatively short-period data of about 10 years would be acceptable for field use. 3. The means and dispersions of monthly evaporation at four stations are detailed in Table 3. 4. The monthly evaporation approached to the trend of normal distribution Fig. 3 showed the examples of normal distribution for each typical monthly data. 5. The correlograms detailed in Fig. 4, shows the time-series characteristics of monthly evaporation, whose periodic term should be twelve months. 6. The periodic analysis for monthly evapolation results in Table 4. Fig. 5 shows the comparison of estimated values to actual and the trend approaches Shuster's periodic trend. 7. A periodic description of days after March 1 for irrigation periods was developed to predict ten-day evaporation in Fig. 6. The ten-day etraporation is different in the distribution form and occurence period of maximum values from the reported Penman's man's evapotranspiration.

• Journal of Korea Water Resources Association
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• v.41 no.2
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• pp.212-228
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• 2008

Five representative reference evapotranspiration(RET) equations were selected, and these equations were compared with pan evaporation by correlation analysis. Pan coefficients were also estimated. Furthermore, five selected RET equations were compared to find the similarity among those at the 21 meteorological stations located in South Korea. Five RET equations selected from 4 different category were Penman(combination approach), FAO Penman-Monteith(FAO P-M) (single source approach), Makkink and Priestley-Taylor (radiation approach) and Hargreaves(temperature approach) equations. In this study, the geographical and topographical conditions were considered for the selection of study stations. The daily meteorological data measured from 1970 at an interval of 5 years were applied in this study. The evapotranspiration estimates obtained by applying evapotranspiration equations were evaluated with numerical and graphical methods. The correlation coefficients between pan evaporation and RET in study stations were above 0.9 indicating very high correlation; however, the slopes of the individual regression lines show the values greater or less than 1.0. Hargreaves equation(temperature approach) shows the most similar evapotranspiration estimates to those of FAO P-M equation from 12 study stations, which are located near to seashore except Daegu station. On the other hand, Priestley-Taylor equation(radiation approach) shows the most similar evapotranspiration estimates to those of FAO P-M equation from 8 study stations, which are located in inland.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1207-1210
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1215-1218
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training data consist of the mixed data The mixed data involves the historic data and the generated data using PARMA (1,1). And, the testing data consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1211-1214
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training data consist of the generated data using PARMA (1,1). And, the testing data consist of the historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1346-1349
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• 2010

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.336-339
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• 2012

Accurate forecasting of pan evaporation (PE) is very important for monitoring, survey, and management of water resources. The purpose of this study is to develop and apply Kohonen self-organizing feature maps neural networks model (KSOFM-NNM) to forecast the daily PE for the dry climate region in south western Iran. KSOFM-NNM for Ahwaz station was used to forecast daily PE on the basis of temperature-based, radiation-based, and sunshine duration-based input combinations. The measurements at Ahwaz station in south western Iran, for the period of January 2002 - December 2008, were used for training, cross-validation and testing data of KSOFM-NNM. The results obtained by TEM 1 produced the best results among other combinations for Ahwaz station. Based on the comparisons, it was found that KSOFM-NNM can be employed successfully for forecasting the daily PE from the limited climatic data in south western Iran.

• Journal of the Korean institute of surface engineering
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• v.30 no.4
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• pp.223-230
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• 1997

Tungsten oxide and nickel oxide thin films were deposited onto ITO(Indium Tin Oxide) transparent glass by the E-beam evaporation and were used as a cathode and an anode for the EC(Electrochromic) smart window, respectively. Stoichiometric structures of the deposited films were investigated by the implementation of XPS(X-ray Photoelectron Spectroscopy) analysis and the results were $WO_{2.42}$ and $NiO_{0.44}$. This oxygen deficincy might affect affect the transparency of the thin films. The electrolyte for the EC smart windows was PAN-$LiCIO_4$ conducting polymer. EC(Ethylene Carbonate)and PC(Propylene Carbonate) were added as plasticizer to enhance ion conductivity. When the weight ratio of the EC : PC was 3 : 1, transmission difference and cycle life performance were tested. Polymer EC windows showed 40% $\Delta$T at 1.5V operating volage for 3,200 cycles. Structural degradation was observed by the SIMS(Secondary Ion Mass Spectroscopy) analysis and it was confirmed that structural degradation of polymer caused by the solvent evaporation was the main cause to degrade EC smart windows.

• Proceedings of the Korean Society of Crop Science Conference
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• 2006.11a
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• pp.32.2-32.2
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• 2006