• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.115-119
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• 2006

The goal of this research is to develop and apply the generalized regression neural networks model (GRNNM) embedding genetic algorithm (GA) for pan evaporation, which is missed or ungaged and for the alfalfa reference evapotranspiration, which is not measured in South Korea. The GRNNM-GA is evaluated using the training, the testing, and reproduction performance respectively for the estimation of the PE and the alfalfa reference evapotranspiration. Since the observed data of the alfalfa reference evapotranspiration using lysimeter have not been measured for a long time in South Korea, the PM method is used to assume and estimate the observed alfalfa reference evapotranspiration. From this research, we evaluate the impact of the limited climatical variables on the accuracy of the GRNNM-GA. We should, furthermore, construct the credible data of the PE and the alfalfa reference evapotranspiration and suggest the reference data for irrigation and drainage networks system in South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1439-1442
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• 2010

Time-lag recurrent neural networks model (Time-Lag RNNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$) and mean relative humidity ($RH_{mean}$). And, for the performances of Time-Lag RNNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of Time-Lag RNNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE using Time-Lag RNNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using Time-Lag RNNM.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1423-1426
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• 2007

The goal of this research is to develop and apply the integrational model for the pan evaporation and the alfalfa reference evapotranspiration in Republic of Korea. Since the observed data of the alfalfa reference evapotranspiration using lysimeter have not been measured for a long time in Republic of Korea, PM method is used to assume and estimate the observed alfalfa reference evapotranspiration. The integrational model consists of staochastics and neural networks processes respectively. The stochastics process is applied to extend for the short-term monthly pan evaporation and alfalfa reference evapotranspiration. The extended data of the monthly pan evaporation and alfalfa reference evapotranspiration is used to evaluate for the training performance. For the neural networks process, the generalized regression neural networks model(GRNNM) is applied to evaluate for the testing performance using the observed data respectively. From this research, we evaluate the impact of the limited climatical variables on the accuracy of the integrational operation of stochastics and neural networks processes. We should, furthermore, construct the credible data of the pan evaporation and the alfalfa reference evapotranspiration, and suggest the reference data for irrigation and drainage networks system in Republic of Korea.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.458-462
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• 2013

A drainage ditch is normally a component of drainage networks in farming systems to remove surplus water, but at the same time, it may act as a major conduit of agricultural nonpoint source pollutions such as sediment, nitrogen, phosphorus, and so on. The hybrid turbidity reduction system using biodegradable check dam and synthetic polymer was developed in this study to manage pollutant discharge from agricultural farmlands during rainfall events and/or irrigation periods. The performance of this hybrid system was assessed using a laboratory open channel sized in 10m-length and 0.2m-width. Various check dams using agricultural byproducts (e.g., rice straw, rice husks, coconut fiber and a mixture of rice husks and coconut fiber) were tested and additional physical factors (e.g., channel slope, flowrate, PAM dosage, turbidity level, etc.) affecting on turbidity reduction were applied to assess their performance. A series of lab experiments clearly showed that the hybrid turbidity reduction system could play a significant role as a supplementary of Best Management Practice (BMP). Moreover, the findings of this study could facilitate to develop an advanced BMP for minimizing nonpoint source pollution from agricultural farmlands and ultimately to achieve the sustainable agriculture.

• Journal of Wetlands Research
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• v.12 no.3
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• pp.79-97
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• 2010

The purpose of this research is to apply the neural networks models for the hydrologic disaggregation of the yearly pan evaporation(PE) data in Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model(MLP-NNM) and support vector machine neural networks model(SVM-NNM), respectively. And, for the evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. The application of MLP-NNM and SVM-NNM for the hydrologic disaggregation of nonlinear time series data is evaluated from results of this research. Four kinds of the statistical index for the evaluation are suggested; CC, RMSE, E, and AARE, respectively. Homogeneity test using ANOVA and Mann-Whitney U test, furthermore, is carried out for the observed and calculated monthly PE data. We can construct the credible monthly PE data from the hydrologic disaggregation of the yearly PE data, and the available data for the evaluation of irrigation and drainage networks system can be suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.399-412
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• 2010

The goal of this research is to apply the neural networks models for the temporal disaggregation of the yearly pan evaporation (PE) data, Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model (MLP-NNM) and generalized regression neural networks model (GRNNM), respectively. And, for the performances evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. From this research, we evaluate the application of MLP-NNM and GRNNM for the temporal disaggregation of nonlinear time series data. We should, furthermore, construct the credible monthly PE data from the temporal disaggregation of the yearly PE data, and can suggest the available data for the evaluation of irrigation and drainage networks system.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.73-88
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• 2007

The goal of this research is to develop and apply the generalized regression neural networks model(GRNNM) embedding genetic algorithm(GA) for the estimation and calculation of the pan evaporation(PE), which is missed or ungaged and of the alfalfa reference evapotranspiration ($ET_r$), which is not measured in South Korea. Since the observed data of the alfalfa 37. using Iysimeter have not been measured for a long time in South Korea, the Penman-Monteith(PM) method is used to estimate the observed alfalfa $ET_r$. In this research, we develop the COMBINE-GRNNM-GA(Type-1) model for the calculation of the optimal PE and the alfalfa $ET_r$. The suggested COMBINE-GRNNM-GA(Type-1) model is evaluated through training, testing, and reproduction processes. The COMBINE-GRNNM-GA(Type-1) model can evaluate the suggested climatic variables and also construct the reliable data for the PE and the alfalfa $ET_r$. We think that the constructive data could be used as the reference data for irrigation and drainage networks system in South Korea.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.89-99
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• 2007

Uncertainty analysis is used to eliminate the climatic variables of input nodes and construct the model of an optimal type from COMBINE-GRNNM-GA(Type-1), which have been developed in this issue(2007). The input variable which has the lowest smoothing factor during the training performance, is eliminated from the original COMBINE-GRNNM-GA (Type-1). And, the modified COMBINE-GRNNM-GA(Type-1) is retrained to find the new and lowest smoothing factor of the each climatic variable. The input variable which has the lowest smoothing factor, implies the least useful climatic variable for the model output. Furthermore, The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. The optimal COMBINE-GRNNM-GA(Type-1) is developed to estimate and calculate the PE which is missed or ungaged and the $ET_r$ which is not measured with the least cost and endeavor Finally, the PE and $ET_r$. maps can be constructed to give the reference data for drought and irrigation and drainage networks system analysis using the optimal COMBINE-GRNNM-GA(Type-1) in South Korea.

• Journal of Korean Society of Forest Science
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• v.90 no.1
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• pp.90-104
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• 2001

This study is aimed to analyze and to evaluate the revegetation and soil conservation technology in desertification-affected sandy land, resulting from the project of "Studies on the desertification combating and sand industry development". Main native plants for combating desertification : The general characteristics of vegetation distribution in desertified regions are partially concentrated vegetation distribution types including the a) desert plants in low zone of desert or sanddune of depressed basin, b) salt-resistant plants around saline lakes, c) grouped vegetation with Poplar and Chinese Tamarix of freshwater-lakes, saline-lakes and river-banks, d) gobi vegetation of gravel desert and e) grassland and oasis-woods around the alluvial fan of rivers, etc. Generally, Tamarix ehinensis Lour., Haloxylon ammodendron Bunge., Calligonum spp., Populus euphratica Oliver., Elaeagnus angustifolia L., Ulmus pumila L., Salix spp., Hedysarum spp., Caragana spp., Xanthoceras sorbifolia Bunge., Nitraria tangutorum Bobr., Lespedeza bicolor, Alhagi sparsifolia Shap., Capparis spinosa L., Artemisia arenaria DC., etc. are widely distributed in desertified regions. It is necessary for conducting research in the native plants in desertified regions. Analysis of intensive revegetation technology system for combating desertification : In the wind erosion region, the experimental research projects of rational farming systems (regional planning, shelterbelts system, protection system of oasis, establishment of irrigation-channel networks and management technology of enormous farmlands, etc.), rational utilization technology of plant resources (fuelwood, medicinal plants, grazing and grassland management, etc.), utilization technology of water resources (management and planning of watershed, construction of channel and technology of water saving and irrigation, etc.), establishment of sheltetbelts, control of population increase and increased production technology of agricultural forest, fuelwood and feed, etc. are preponderantly being promoted. And in water erosion region, the experimental research projects of development of rational utilization technology of land and vegetation, engineering technology and protection technology of crops, etc. are being promoted in priority. And also, the experimental researches on the methods of utilization of water (irrigation, drainage, washing and rice cultivation, etc.), agricultural methods (reclamation of land, agronomy, fertilization, seeding, crop rotation, mixed-cultivation and soil dressing works, etc.) and biological methods (cultivation of salt-resistant crops and green manure and tree plantation, etc.) for improvement of saline soil and alkaline soil in desertified-lands are actively being promoted. And the international cooperations on the revegetation technology development projects of desertified-lands are sincerely being required.