• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1447-1456
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• 2017

Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.543-550
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• 2009

The traditional simple extrapolation type short term quantitative rainfall forecast can not realize the evolution of rainfall generating weather system. To overcome the drawback of the linear extrapolation type rainfall forecasting model, the history of a weather system from sequential weather radar information and a polynomial regression technique were used to generate forecast fileds of x-directional, y-directional velocities and radar reflectivity which considered the nonlinear behavior related to the evolution of weather systems. Results demonstrated that test statistics of forecasts using the developed model is better than that of 2-CAPPI forecast. However there is still a large room to improve the forecast of spatial and temporal evolution of local storms since the model is not based on a fully physical approach but a statistical approach.

• Geomechanics and Engineering
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• v.22 no.3
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• pp.207-217
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• 2020

Slope reliability analysis and risk assessment for spatially variable soils under rainfall infiltration are important subjects but they have not been well addressed. This lack of study may in part be due to the multiple and diverse evaluation indexes and the low computational efficiency of Monte-Carlo simulations. To remedy this, this paper proposes a highly efficient computational method for investigating random field problems for slopes. First, the probability density evolution method (PDEM) is introduced. This method has high computational efficiency and does not need the tens of thousands of numerical simulation samples required by other methods. Second, the influence of rainfall on slope reliability is investigated, where the reliability is calculated from based on the safety factor curves during the rainfall. Finally, the uncertainty of the sliding mass for the slope random field problem is analyzed. Slope failure consequences are considered to be directly correlated with the sliding mass. Calculations showed that the mass that slides is smaller than the potential sliding mass (shallow surface sliding in rainfall). Sliding mass-based risk assessment is both needed and feasible for engineered slope design. The efficient PDEM is recommended for problems requiring lengthy calculations such as random field problems coupled with rainfall infiltration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.821-831
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• 2014

Stochastic rainfall generators or stochastic simulation have been widely employed to generate synthetic rainfall sequences which can be used in hydrologic models as inputs. The calibration of Poisson cluster stochastic rainfall generator (e.g. Modified Bartlett-Lewis Rectangular Pulse, MBLRP) is seriously affected by local minima that is usually estimated from the local optimization algorithm. In this regard, global optimization techniques such as particle swarm optimization and shuffled complex evolution algorithm have been proposed to better estimate the parameters. Although the global search algorithm is designed to avoid the local minima, reliable parameter estimation of MBLRP model is not always feasible especially in a limited parameter space. In addition, uncertainty associated with parameters in the MBLRP rainfall generator has not been properly addressed yet. In this sense, this study aims to develop and test a Bayesian model based parameter estimation method for the MBLRP rainfall generator that allow us to derive the posterior distribution of the model parameters. It was found that the HBM based MBLRP model showed better performance in terms of reproducing rainfall statistic and underlying distribution of hourly rainfall series.

• Atmosphere
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• v.27 no.2
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• pp.133-150
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• 2017

This study evaluates CMIP5 model performance on rainy season evolution in the East Asian summer monsoon. Historical (1986~2005) simulation is analyzed using ensemble mean of CMIP5 19 models. Simulated rainfall amount is underestimated than the observed and onset and termination of rainy season are earlier in the simulation. Compared with evolution timing, duration of the rainy season is uncertain with large model spread. This area-averaged analysis results mix relative differences among the models. All model show similarity in the underestimated rainfall, but there are quite large difference in dynamic and thermodynamic processes. The model difference is shown in horizontal distribution analysis. BEST and WORST group is selected based on skill score. BEST shows better performance in northward movement of the rain band, summer monsoon domain. Especially, meridional gradient of equivalent potential temperature and low-level circulation for evolving frontal system is quite well captured in BEST. According to RCP8.5, CMIP5 projects earlier onset, delayed termination and longer duration of the rainy season with increasing rainfall amount at the end of 21st century. BEST and WORST shows similar projection for the rainy season evolution timing, meanwhile there are large discrepancy in thermodynamic structure. BEST and WORST in future projection are different in moisture flux, vertical structure of equivalent potential temperature and the subsequent unstable changes in the conditional instability.

• Journal of Environmental Science International
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• v.24 no.5
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• pp.601-613
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• 2015

On the study of the characteristics and life cycle of mesoscale convective band in type of airmass that occurred in the Honam area from June to September for only 4 years in the period of 2009~2012, 10 examples based on the amount of rainfall with AWS 24 hours/60 minutes rainfalls, Mt. Osung radar 1.5 km CAPPI/X-SECT images and KLAPS data for convective band with heavy rainfall event were selected. There were analyzed and classified by using the convective band with heavy rainfall occurred along the convergence line of sea wind in the form of individual multi-cellular cell and moving direction of convective band appeared in a variety of patterns; toward southwestern (2 cases), northeastern (4 cases), congesting (2 cases), and changing its moving direction (2 cases). The case study dated of the 17th Aug. 2012 was chosen and implemented by sequentially different evolution of its shape along the convergence line of sea wind cell and moving direction of convective band as equivalent potential temperatures at the lower layer have increased to the upper layer 500 hPa, that the individual cells were developed vertically and horizontally through their merger, but owing to divergence caused by weakened rainfall and descending air current, the growth of new cell was inhibited resulting in dissipation of convective cells.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.706-714
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• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• Journal of Korea Water Resources Association
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• v.37 no.2
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• pp.121-131
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• 2004

SIMHYD and TANK model are used to predict time series of daily rainfall-runoff of Soyang Dam and Youngcheon Dam watershed. The performances of SIMHYD model with 7 parameters and TANK model with17 parameters are compared. Three optimization methods (Genetic algorithm, Pattern search multi-start and Shuffled Complex Evolution algorithm) were applied to study-areas with 3 different types of objective functions. Efficiency of TANK model is higher than that of SIMHYD. Among different types of objective function, Nash-sutcliffe coefficient is found to be the most appropriateobjective function to evaluate applicability of model.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.473-478
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• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.191-191
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• 2017

Floods have become more widespread and frequent among natural disasters and consisted significant losses of lives and properties worldwide. Flood's impacts are threatening socio-economic and people's lives in the Mekong River Basin every year. The objective of this study is to identify the flood hazard areas and inundation depth in the Mekong River Basin. A rainfall-runoff and flood inundation model is necessary to enhance understanding of characteristic of flooding. Rainfall-Runoff-Inundation (RRI) model, a two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously, was applied in this study. HydoSHEDS Topographical data, APPRODITE precipitation, MODIS land use, and river cross section were used as input data for the simulation. The Shuffled Complex Evolution (SCE-UA) global optimization method was integrated with RRI model to calibrate the sensitive parameters. In the present study, we selected flood event in 2000 which was considered as 50-year return period flood in term of discharge volume of 500 km3. The simulated results were compared with observed discharge at the stations along the mainstream and inundation map produced by Dartmouth Flood Observatory and Landsat 7. The results indicated good agreement between observed and simulated discharge with NSE = 0.86 at Stung Treng Station. The model predicted inundation extent with success rate SR = 67.50% and modified success rate MSR = 74.53%. In conclusion, the RRI model was successfully used to simulate rainfall runoff and inundation processes in the large scale Mekong River Basin with a good performance. It is recommended to improve the quality of the input data in order to increase the accuracy of the simulation result.