• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• Proceedings of the Zoological Society Korea Conference
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• 1999.10b
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• pp.150.2-150
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• 1999

To investigate the weathering soil buffering capacities of the artificial acidic precipitation, the weathering soils and their leachate solutions were sampled from the host rocks(granite;GR, rhyolite;RH, gabbro;GA, basalt;BA, two serpentinite;SE1, SE2 and limestone;LI) and analyzed for pH and chemical properties. 1n the soil pH of the GR and RH ,the acidic rocks, were 5.02 and 5.95, respectively. And the GA and BA, basic rocks, were 6.52 and 7.57. The SE1 and SE2 were 8.90 and 8.89. While the LI was 7.84. These results means the typical soil pH properties by host rocks. After the artificial acidic precipitation input 5OOml, the average changes of soil leachate solutions treated by pH levels(pH 5.0, 4.0 and 3.0), were pH 5.73, 5.00 and 4.40. in GR soil, and pH 6.19, 5.99 and 5.57 in RH. GA were pH 6.31, 6.04 and 5.86, BA were pH 7.05, 6.85 and 6.56 and SE1 were pH 8.31, 8.26 and 7.71. SE2 were pH 8.29, 8.24 and 7.96. LI were pH 7.55, 7.46 and 6.79. The soil leachate pHs from volcanic rocks were higher than those from the plutonic rocks and GR soils showed greater response than other soils. With increasing 100ml input-solution, the soil leachate pHs were mainly decreased. Cation concentrations, CEC, EC and total nitrogen concentrations of RH and BA soils, the volcanic rocks, were higher than those of GR and GA soil, the plutonic rocks. On the contrary, Al concentrations of the GR and GA soils were higher than those of RH and BA soils, partly because of high quartz content in GR and Al content in the biotite and plagioclase in GA.

• Journal of Environmental Science International
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• v.9 no.6
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• pp.455-462
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• 2000

This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.267-275
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• 2015

This study has been implemented to check the effect of roughness on the color stability according to the surface treatment of artificial teeth. 20 units of 3 groups with the different surface treatment of artificial teeth have been precipitated in the soy sauce, the red pepper paste, the coffee and the cola, measure equipment was used for Shade-eye(Chroma Meter, Shofu, U.S.A). The value of L*, a*, b* using its average value after measuring 3 times per 1 point by selecting 3 points randomly out of central parts of teeth on a cycle of each 1 day, 1 week, 2 weeks, 3 weeks, 4 weeks after precipitation in terms of precipitated specimens. As a result of Multiple regression analysis, the value of ${\Delta}E*$ of artificial teeth according to the surface treatment showed the significant negative(-) effect in the Group 2(p<0.001) and the red pepper paste had the biggest effect on the color change of artificial teeth out of pollution source.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.97-107
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• 2011

For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.586-591
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• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.877-885
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• 2016

In this study, the Standard Precipitation Index(SPI), meteorological drought index, was used to evaluate the temporal and spatial assessment of drought forecasting results for all cross Korea. For the drought forecasting, the Multi Layer Perceptron-Artificial Neural Network (MLP-ANN) was selected and the drought forecasting was performed according to different forecasting lead time for SPI (3) and SPI (6). The precipitation data observed in 59 gaging stations of Korea Meteorological Adminstration (KMA) from 1976~2015. For the performance evaluation of the drought forecasting, the binary classification confusion matrix, such as evaluating the status of drought occurrence based on threshold, was constituted. Then Receiver Operating Characteristics (ROC) score and F score according to conditional probability are computed. As a result of ROC analysis on forecasting performance, drought forecasting performance, of applying the MLP-ANN model, shows satisfactory forecasting results. Consequently, two-month and five-month leading forecasts were possible for SPI (3) and SPI (6), respectively.

• Journal of the Korean Society for Heat Treatment
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• v.34 no.5
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• pp.226-232
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• 2021

The relationship between precipitation and coefficient of thermal expansion of Al-6%Si-0.4%Mg-0.9%Cu-(Ti) alloy (in wt.%) after various heat treatments were studied by the thermodynamic analyzer (TMA) and differential scanning calorimetry (DSC). Solution heat treatment of the alloy was carried out at 535℃ for 6 h followed by water quenching, and the samples were artificially aged in the air at 180℃ and 220℃ for 5 h. The coefficient of thermal expansion (CTE) curve showed some residual strain and decreased with increasing aging temperature. The CTE curves changed sharply in the temperature range of 200℃ to 400℃, and the corresponding peak shifted for the aged samples due to the change in the precipitation behavior of the secondary phase. These transformation peaks in the aged sample are related to the volume of the precipitation of the Si phase as determined by DSC analysis. The change in CTE is mainly caused by the precipitation of the Si phase in the Al-Si alloy, and the size of the change occurs simultaneously with the size of the precipitate.

• Korean Journal of Organic Agriculture
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• v.25 no.3
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.