• Journal of the Korea Organic Resources Recycling Association
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• v.25 no.2
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• pp.33-40
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• 2017

In Korea, it is required to install the final cover layer immediately after the end of use of the waste landfill, and to conduct aftercare for 30 years. However, the installation of the final cover layer minimizing the penetration of the rainfall will delay the decomposition of the buried organic wastes and reduce the amount of pollutants released into the leachate. Therefore, at the end of the aftercare period, pollutants might be discharged and cause the pollution of the surrounding environment. In this study, using lab-scale lysimeters, the amount of pollutants discharged into the leachate was observed. At the initial stage, same artificial rainfall was injected, and after 7 months later, different reduced artificial rainfall was injected for 8.4 months assuming as the final soil layer was installed. From the results, it was advantageous in terms of environmental management after the end of the aftercare period to install a temporary cover layer that permits the infiltration of rainfall to some extent rather than to install the final cover layer immediately after the end of use of the waste landfill.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2B
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• pp.145-152
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• 2006

In the present study, a nonlinear model of rainfall-runoff process using Artficial Neural networks(ANNs) which have no consideration on the physical parameter for the basin was developed at Naju station which is the main stream of Yeongsan-river, and Sunam station which is the main stream of Hwangryong-river. The result from the model of ANN_NJ_9 at the Naju station revealed the best result of the rainfall-runoff process, while the model of ANN_SA_9 for the Sunam station. Also, GUI_FFS developed in the research showed the $R^2$ of more than 0.98 between the observed and predicted values using the rainfall and runoff in the respective stations. Therefore, the GUI_FFS might be expected that it can play a role for the high reliability to operate and manage the water resources and the design of river plan more efficiently in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1976-1981
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• 2011

In this study, the optimal sewer layout model(Lee, J.H., 2010)[1] was applied to verify the reduction effect of urban inundation in the optimal sewer networks, which designed by this optimal model, for various artificial rainfall events in urban areas. Then the optimal model was developed by Lee, J.H. to minimize the peak outflow at outlet in sewer network. The applied rainfall events are two types. One is the rainfall event which the double peak occurs between specific time distance continuously. The other is the continuous rainfall event with specific rainfall intensity. As the result, in two applied rainfall types, the peak outflows at outlet were reduced in the optimal sewer networks which designed the optimal sewer layout model of Lee, J.H.. Therefore, the peak outflow is reduced because the inflows at each manhole are distributed in the whole sewer networks, it's not delay of inflows by this optimal model.

• Water Engineering Research
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• v.5 no.1
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• pp.37-45
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• 2004

As one alternative to alleviate damages caused by stormwater runoff, the effects of runoff quantity reduction are analyzed when porous pavement is used. Porous pavements with various depths, general pavement and an artificial rainfall generator are installed for laboratory experiments. Runoff changes are analyzed according to the various rainfall durations. The rainfall intensity of 150 mm/hr is generated for 30 minutes, 60 minutes, and 120 minutes. For porous pavements with 80 cm thickness, 100%, 93%, 56% of discharge is infiltrated through soil, respectively. For porous pavements with 20 cm thickness, 81%, 32%, 28% of discharge is infiltrated through soil, respectively. It is found that the porous pavements are able to decrease the runoff.

• Environmental Engineering Research
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• v.20 no.3
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• pp.290-297
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• 2015

This paper presents the evaluation of the impact of climate change on water resources and yield capacity of Asa and Kampe reservoirs. Trend analysis of mean temperature, runoff, rainfall and evapotranspiration was carried out using Mann Kendall and Sen's slope, while runoff was modeled as a function of temperature, rainfall and evapotranspiration using Artificial Neural Networks (ANN). Rainfall and runoff exhibited positive trends at the two dam sites and their upstream while forecasted ten-year runoff displayed increasing positive trend which indicates high reservoir inflow. The reservoir yield capacity estimated with the ANN forecasted runoff was higher by about 38% and 17% compared to that obtained with historical runoff at Asa and Kampe respectively. This is an indication that there is tendency for water resources of the reservoir to increase and thus more water will be available for water supply and irrigation to ensure food security.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.81-94
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• 2010

Input variable selection is one of the various techniques for improving the performance of artificial neural network. In this study, mutual information is applied for input variable selection technique instead of correlation coefficient that is widely used. Among 152 variables of RDAPS (Regional Data Assimilation and Prediction System) output results, input variables for artificial neural network are chosen by computing mutual information between rainfall records and RDAPS' variables. At first the rainfall forecast variable of RDAPS result, namely APCP, is included as input variable and the other input variables are selected according to the rank of mutual information and correlation coefficient. The input variables using mutual information are usually those variables about wind velocity such as D300, U925, etc. Several statistical error estimates show that the result from mutual information is generally more accurate than those from the previous research and correlation coefficient. In addition, the artificial neural network using input variables computed by mutual information can effectively reduce the relative errors corresponding to the high rainfall events.

• Journal of Wetlands Research
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• v.16 no.1
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• pp.19-32
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• 2014

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using $Vflo^{TM}$. Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.6 no.4
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• pp.1-7
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• 2003

Artificial ground greening, which is considered as a way for the greening of cities, should be constructed easily and maintained continuously. Thus it is necessary to use light soils for keeping in flexible formation and light load. And the garden should be managed optimally taking account for the characteristics of the soil and plant. But in most landscape green area, they are not under management. Mostly they are occasionally irrigated without nutrient by hand-operating. So this study was conducted to investigate plant growth by management methods and soil depth(15cm, 30cm). As a results of the different methods of management had effect on the plant growth and on the rate of flowering. When Hosta longipes were grown in different three management methods, control(rainfall), periodical irrigation, and nutri-irrigation(fertigation), the content of chlorophyll, the plant growth and the rate of flowering were higher in nutri-irrigation (fertigation) treatment than those in control(rainfall) and periodic irrigation. And nutrient contents of leaf are also higher. Between 15cm and 30cm soil depth, the plant growth of 15cm soil depth is better than that of 30 soil depth. According to these results on artificial ground greening, determination of optimal soil depth by plant species is required, And a specialist for nutrient management is demanded.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.259-259
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• 2016

This study examined Artificial Neurons Networks model (ANNs) for forecast flash discharge at Southern part of Thailand by using rainfall data and discharge data. The Sungai Kolok River Basin has meant the border crossing between Thailand and Malaysia which watershed drains an area lies in Thailand 691.88 square kilometer from over all 2,175 square kilometer. The river originates in mountainous area of Waeng district then flow through Gulf of Thailand at Narathiwat Province, which the river length is approximately 103 kilometers. Almost every year, flooding seems to have increased in frequency and magnitude which is highly non-linear and complicated phenomena. The purpose of this study is to forecast runoff on Sungai Kolok at X.119A gauge station (Sungai Kolok district, Narathiwat province) for 3 days in advance by using Artificial Neural Networks model (ANNs). 3 daily rainfall stations and 2 daily runoff station have been measured by Royal Irrigation Department and Meteorological Department during flood period 2000-2014 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Coefficient of determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. $R^2$values for first day, second day and third day of runoff forecasting is 0.71, 0.62 and 0.49 respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and real-time operated. In conclusion, the ANNs model is suitable to runoff forecasting during flood incident of Sungai Kolok river because it is straightforward model and require with only a few parameters for simulation.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.4 s.15
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• pp.27-34
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• 2004

In this paper, in order to analyse the variation of runoff characteristics depending upon installation of the groundwater recharge facilities, the experiment basin was prepared and the ratio of infiltration and runoff volume were observed in the rainfall events. For the rainfall analysis, 4 types of rainfall events were examined during July 11${\sim}$July 17, 2004. The results show that the mean ratio of infiltration was 89.39% and the mean ratio of runoff was 10.61%. For the artificial rainfall events, which are in the range of rainfall intensities between 60mm/hr and 100mm/hr, all the rainfall volume was infiltrated through the groundwater recharging basin. However, it is necessary to be careful for the long term rainfall, the runoff can be occurred based on the groundwater table.