Changsam Jeong

Monthly Precipitation Forecasting with a Neuro-Fuzzy Model

Quantitative and qualitative monthly precipitation forecasts are produced with ANFIS. To select the proper input variable set from 30 variables, including climatological and hydrological monthly recording data, the forward selection method, which is a wrapper method for feature selection, is applied. The error analysis of the results from training and checking the data sets suggests that 3 variables can be used as a suitable number of inputs for ANFIS, and the best five 3-input-variable sets were selected. The quantitative monthly precipitation forecasts were computed using each 3-input-variable set, and the ensemble averaging method over the five forecasts was used for calculations to reduce the uncertainties in the forecasts and to remove the negative rainfall forecasts. A qualitative forecast that is computed with the quantitative forecast also produced three types of categories that describe the next month’s precipitation condition and was compared with data from the weather agency of Korea.

Assessment of modified Anderson–Darling test statistics for the generalized extreme value and generalized logistic distributions

An important problem in frequency analysis is the selection of an appropriate probability distribution for a given sample data. This selection is generally based on goodness-of-fit tests. The goodness-of-fit method is an effective means of examining how well a sample data agrees with an assumed probability distribution as its population. However, the goodness of fit test based on empirical distribution functions gives equal weight to differences between empirical and theoretical distribution functions corresponding to all observations. To overcome this drawback, the modified Anderson–Darling test was suggested by Ahmad et al. (1988b). In this study, the critical values of the modified Anderson–Darling test statistics are revised using simulation experiments with extensions of the shape parameters for the GEV and GLO distributions, and a power study is performed to test the performance of the modified Anderson–Darling test. The results of the power study show that the modified Anderson–Darling test is more powerful than traditional tests such as the χ2, Kolmogorov–Smirnov, and Cramer von Mises tests. In addition, to compare the results of these goodness-of-fit tests, the modified Anderson–Darling test is applied to the annual maximum rainfall data in Korea.

Meta-heuristic maximum likelihood parameter estimation of the mixture normal distribution for hydro-meteorological variables

In the water resources field, there are emerging problems such as temporal changes of data and new additions of water sources. Non-mixture models are not efficient in analyzing these data because these models are developed under the assumption that data do not change and come from one source. Mixture models could successfully analyze these data because mixture models contain more than one modal. The expectation maximization (EM) algorithm has been widely used to estimate parameters of the mixture normal distribution for describing the statistical characteristics of hydro meteorological data. Unfortunately, the EM algorithm has some disadvantages, such as divergence, derivation of information matrices, local maximization, and poor accuracy. To overcome these disadvantages, this study proposes a new parameter estimation approach for the mixture normal distribution. The developed model estimates parameters of the mixture normal distribution by maximizing the log likelihood function using a meta-heuristic algorithm—genetic algorithm (GA). To verify the performance of the developed model, simulation experiments and practical applications are implemented. From the results of experiments and practical applications, the developed model presents some advantages, such as (1) the proposed model more accurately estimates the parameters even with small sample sizes compared to the EM algorithm; (2) not diverging in all application; and (3) showing smaller root mean squared error and larger log likelihood than those of the EM algorithm. We conclude that the proposed model is a good alternative in estimating the parameters of the mixture normal distribution for kutotic and bimodal hydrometeorological data.

Frequency Analysis of Nonidentically Distributed Hydrometeorological Extremes Associated with Large-Scale Climate Variability Applied to South Korea

AbstractIn the frequency analyses of extreme hydrometeorological events, the restriction of statistical independence and identical distribution (iid) from year to year ensures that all observations are from the same population. In recent decades, the iid assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino–Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, the objective of the current study is to propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observati...

Application of Harmony Search to Design Storm Estimation from Probability Distribution Models

The precision of design storm estimation depends on the selection of an appropriate probability distribution model (PDM) and parameter estimation techniques. Generally, estimated parameters for PDMs are provided based on the method of moments, probability weighted moments, and maximum likelihood (ML). The results using ML are more reliable than the other methods. However, the ML is more laborious than the other methods because an iterative numerical solution must be used. In the meantime, metaheuristic approaches have been developed to solve various engineering problems. A number of studies focus on using metaheuristic approaches for estimation of hydrometeorological variables. Applied metaheuristic approaches offer reliable solutions but use more computation time than derivative-based methods. Therefore, the purpose of the current study is to enhance parameter estimation of PDMs for design storms using a recently developed metaheuristic approach known as a harmony search (HS). The HS is compared to the genetic algorithm (GA) and ML via simulation and case study. The results of this study suggested that the performance of the GA and HS was similar and showed more accurate results than that of the ML. Furthermore, the HS required less computation time than the GA.

Temporal Downscaling of Precipitation from Daily to Hourly Based on Nonparametric Approach: Assessment of the Climate Change Impacts on the Hourly Precipitation for the Gyeongnam Region

Hourly hydro-meteorological data are vital to flood control system and to assess the hydrological effects of climate change on medium and small watersheds. However, finder than daily hydro-meteorological data are not easy to obtain. A temporal downscaling method to obtain finer time series, e.g. hourly, might be very useful for assessing the hydrological effects of the variations of precipitation by climate change. In the current study, a temporal downscaling model that combines a nonparametric stochastic simulation approach with Genetic Algorithm (GA) is tested. The tested model was applied to Busan-Gyeongnam station in South Korea for a historical time period to validate the model performance. The results revealed that the applied model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. Furthermore, the RCP 4.5 and RCP 8.5 climate scenarios for the Busan-Gyeongnam area were also analyzed. The results illustrated that the mean significantly increased while the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP 8.5 than RCP 4.5. The downscaled hourly precipitation adequately reproduced the statistical behaviors of the historical hourly precipitation data for all durations considered. Overall, the results demonstrated that the applied temporal downscaling model is a good alternative method for downscaling simulated daily precipitation data to hourly especially for assessing the impacts of climate change especially from the variations of precipitation.

Accuracy Improvement of Discharge Measurement with Modification of Distance Made Good Heading

Remote control boats equipped with an Acoustic Doppler Current Profiler (ADCP) are widely accepted and have been welcomed by many hydrologists for water discharge, velocity profile, and bathymetry measurements. The advantages of this technique include high productivity, fast measurements, operator safety, and high accuracy. However, there are concerns about controlling and operating a remote boat to achieve measurement goals, especially during extreme events such as floods. When performing river discharge measurements, the main error source stems from the boat path. Due to the rapid flow in a flood condition, the boat path is not regular and this can cause errors in discharge measurements. Therefore, improvement of discharge measurements requires modification of boat path. As a result, the measurement errors in flood flow conditions are 12.3–21.8% before the modification of boat path, but 1.2–3.7% after the DMG modification of boat path. And it is considered that the modified discharges are very close to the observed discharge in the flood flow conditions. In this study, through the distance made good (DMG) modification of the boat path, a comprehensive discharge measurement with high accuracy can be achieved.