• The Journal of Engineering Geology
• /
• v.23 no.2
• /
• pp.137-147
• /
• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.46 no.5
• /
• pp.3-11
• /
• 2004

The objective of this study is to develop water saving irrigation method using water balance model in order to save rural water. Daily water balance components such as irrigation water, drainage water, effective rainfall, ET, and infiltration were measured in paddy fields. Model simulations were performed for different outlet heights and ponding depths. The outlet heights and the ponding depths are 2 cm, 4 cm, 6 cm, 8 cm, and 10 cm, respectively. Based on the simulation very shallow ponding depth of 2 cm with 10 cm outlet height showed the largest effective rainfall ratio and the smallest irrigation amount. Until the introduction of laser leveling dozer and automatic inlet control devices, it would be desirable to adopt 4cm ponding depth because of difficulty of land leveling and frequency of farmer's field visit. The results of this study will be applied in the paddy farming and can improve water use efficiency.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.30 no.2
• /
• pp.95-104
• /
• 1988

This study refers to the development of a hydrologic model simulating daily inflow and release rates for inigation reservoirs. A daily - based model is needed for adequate operation of an irrigation reservoir sufficing the water demand for paddy fields which is closely related to meteorological conditions. And the objective of this study is to develop a reservoir release rate model and then to calibrata the parameters. The release rates model considers daily water demands , water supply for transplanting, minmum release for maintaining canal flow, and maxirnun and regular flooding depth for determining effective rainfall on paddy fields. Each of the factors in the model was regarded as a lumped pararuter representing the average condition of a whole irrigated area. The water demand was estimated form the potential evapotranspiration by Penman method, the effective rainfall, and the infiltration on paddy fields. The release model was found to be capable of adequately simulating daily reservoir releases based on meteorological data.

• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 1999.10c
• /
• pp.41-46
• /
• 1999

In the practice , ET was mainly estimated by Blaney-Criddle or FAO Penman method. But these methods were found to frequently overestimate ET. And calculation of effective rainfall by empirical formula is hardly to explain drop property and soil texture. Since 1990, FAO recommended the adoption of Penman-Moneteith combination method as a new standard for reference ET. Purpose of this study is establish new estimate method of upland crop requirements. We asopt P-M method to estimate ET and set up soil moisture balance equation to equation to calculate effective rainfall and irrigation water requirements. We expect that this new method rise efficiency to upland water management.

• Journal of Environmental Science International
• /
• v.12 no.10
• /
• pp.1109-1116
• /
• 2003

The genetic algorithm is investigated fer parameters estimation of SED (storage - effective drainage) model from the Wi-stream watershed in Nakdong river basin. In the practical application of model, as a number of watershed parameters do not measure directly, it is desirable to make a good estimation from the known rainfall and runoff data. For the estimation of parameters of the SED model using the genetic algorithm, parameters of Green-Ampt equation(SM, K$\_$s/) for the estimation of an effective rainfall and initial storage(y$\_$in/) used in SED model are obtained a regression equation with 5, 10, 20 days antecedent precipitation. And as a consequence of computation, the parameters were obtained to satisfy the proposed objective function. From the comparison of observed and computed hydrographs, it shows a good agreement in the shape and the rising limb, peak, falling limb of hydrograph, so the SED model using the genetic algorithm shows a suitable model for runoff analysis in river basin.

• Journal of Korean Society of Water and Wastewater
• /
• v.25 no.5
• /
• pp.751-757
• /
• 2011

The monitoring technique based on electrical conductivity (EC) can provide researchers with some advantages in maintenance management and is cost-effective as compared with existing CSOs monitoring. In this study, the flow rate estimation using EC data was executed in two sites where storm overflow chamber had installed. In the result of A-site, R2 of second order multinomial between dilution ratio of EC and observed flow rate was showed the range of 0.68 ~ 0.77. And $R^{2}$ of B-site was 0.62 ~ 0.81. On the other hand, cumulative frequency of A-site was 43.4 ~ 52.2% in the relative error level of under 20%. And B-site was 10.1 ~ 46.5%. The flow rate estimation formula was improved through consideration of some parameters including antecedent dry days and rainfall duration. And difference between estimated flow rate and observed flow rate in total rainfall event was very small.

• Journal of Wetlands Research
• /
• v.25 no.2
• /
• pp.121-131
• /
• 2023

This study examines the possible problem in the rainfall-runoff analysis process using the VIC (Variable Infiltration Capacity) model caused by using the effective soil depth instead of the soil depth. The parameters of the model are determined as follows. First, parameters that can be determined using available numerical information are fixed. For parameters related to direct runoff and base runoff, the recommended values of the VIC model are applied. In the case of soil depth, four cases are considered: (1) the effective soil depth is applied as the soil depth, (2) 1.5 times of the effective soil depth is applied as the soil depth by reflecting the vertical structure of the soil layer, (3) 1.25 times of the effective soil depth, and (4) 2.0 times of the effective soil depth as alternative soil depths. This study simulates the rainfall-runoff for the period from 1983 to 2020 targeting the Chungju Dam and Soyang River Dam basins of the Han River system. As a result of the study, it is confirmed that when the effective soil depth is applied instead of the soil depth, direct runoff and baseflow have opposite effects, and direct runoff increases by more than 3% while base runoff decreases by the same scale. In addition, the most influential factor in the estimation of the effective soil depth in the Chungju Dam and Soyanggang Dam basins is found to be the proportion of rock outcrop area. The difference between the direct runoff ratio and the base runoff ratio in the two basins is conformed significantly different due to the influence of the rock outcrop area.

• Journal of the Korean earth science society
• /
• v.35 no.7
• /
• pp.518-528
• /
• 2014

In this study, a model of rainfall drop-size distribution was modified using PARSIVEL-retrieved rainfall drop-size distribution over Daegwanryeong mountainous area. A prototype model (Modified ${\Gamma}$ distribution model) applicable for this area was decided through the comparative analysis between results from models proposed by preceding research and PARSIVEL-retrieved data over Daegwanryeong mountainous area. In order to apply the prototype model for Daegwanryeong region, the parameters (${\alpha}$, A, B) were made via sensitivity experiments and models of the rainfall drop-size distributions for five cases of rainfall rate were proposed. Results from the proposed five models showed high correlations with PARSIVEL-retrieved data ($R^2=0.975$). In order to suggest a generalized form of rainfall drop-size distribution, interaction equations between rainfall rates and parameters (${\alpha}$, A, B) were investigated. The generalized model of the rainfall drop-size distribution was highly correlated with PARSIVEL-retrieved data ($R^2=0.953$), which means that the proposed model from this study was effective for simulating the rainfall drop-size distribution over Daegwanryeong region. However, the proposed model was optimized for rainfall drop-size distribution over Daegwanryeong region. Therefore, broad observations of other regions are necessary in order to develop the representative model of the Korean peninsula.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.34-34
• /
• 2012

Heavy storms rainfall has caused many landslides and slope failures especially in the mountainous area of the world. Landslides and slope failures are common geologic hazards and posed serious threats and globally cause billions in monetary losses and thousands of casualies each year so that studies on slope stability and its failure mechanism under rainfall are being increasing attention of these days. Rainfall-induced slope failures are generally caused by the rise in ground water level, and increase in pore water pressures and seepage forces during periods of intense rainfall. The effective stress in the soil will be decreased due to the increased pore pressure, which thus reduces the soil shear strength, eventually resulting in slope failure. During the rainfall, a wetting front goes downward into the slope, resulting in a gradual increase of the water content and a decrease of the negative pore-water pressure. This negative pore-water pressure is referred to as matric suction when referenced to the pore air pressure that contributes to the stability of unsaturated soil slopes. Therefore, the importance is the study of saturated unsaturated soil behaviors in evaluation of slope stability under heavy rainfall condition. In an actual field, a series of failures may occur in a slope due to a rainfall event. So, this study attempts to develop a numerical model to investigate this failure mechanism. A two-dimensional seepage flow model coupled with a one-dimensional surface flow and erosion/deposition model is used for seepage analysis. It is necessary to identify either there is surface runoff produced or not in a soil slope during a rainfall event, while analyzing the seepage and stability of such slopes. Runoff produced by rainfall may result erosion/deposition process on the surface of the slope. The depth of runoff has vital role in the seepage process within the soil domain so that surface flow and erosion/deposition model computes the surface water head of the runoff produced by the rainfall, and erosion/deposition on the surface of the model slope. Pore water pressure and moisture content data obtained by the seepage flow model are then used to analyze the stability of the slope. Spencer method of slope stability analysis is incorporated into dynamic programming to locate the critical slip surface of a general slope.

• Journal of Korea Water Resources Association
• /
• v.56 no.8
• /
• pp.471-484
• /
• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.