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LASSO-based predictor section in downscaling GCM data
Taesam Lee,Dorra Hammami,TTaesam Lee,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
The objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Quebec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters forcomparison.
Subdaily precipitation downscaling of climate change scenarios
Taesam Lee,Changsam Jeong 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
In the current study, a temporal downscaling model that combines a nonparametric stochastic simulation approach with a genetic algorithm is proposed. The proposed model was applied to Jinju station in South Korea for a historical time period to validate the model performance. The results revealed that the proposed model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. In addition, the occurrence and transition probabilities are well preserved in the downscaled hourly precipitation data. Furthermore, the RCP4.5 and RCP8.5 climate scenarios for the Jinju station were also analyzed, revealing that the mean and the wet-hour probability significantly increased and the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP8.5 than RCP4.5.
Independent decomposition analysis in stochastic simulation of streamflow
Taesam Lee 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
We illustrate in the current study that fitting a univariate time series model to each extracted component might end up with the underestimation of the serial dependence that the observation data might contain. A alternative for parameter estimation is suggested to preserve the serial dependence of the observation variable using the relationship between the observation variable and the decomposed variable. The case study of the Upper Colorado River basin shows that some improvement is made through the suggested alternative.
Modeling of nonstationary oscillation hydroclimatic processes employing EMD
Taesam Lee,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
Reproducing nonstationary oscillation (NSO) processes in a stochastic time series model is a difficult task because of the complexity of the nonstationary behaviors. In the current study, a novel stochastic simulation technique that reproduces the NSO processes embedded in hydroclimatic data series is presented. The proposed model reproduces NSO processes by utilizing empirical mode decomposition (EMD) and nonparametric simulation techniques (i.e., k-nearestneighbor resampling and block bootstrapping). The model was first tested with synthetic data sets from trigonometric functions and the Rossler system. The North Atlantic Oscillation (NAO) index was then examined as a real case study. This NAO index was then employed as an exogenous variable for the stochastic simulation of streamflows at the Romaine River in the province of Quebec, Canada. The results of the application to the synthetic data sets and the real-world case studies indicate that the proposed model preserves well the NSO processes along with the key statistical characteristics of the observations. It was concluded that the proposed model possesses a reasonable simulation capacity and a high potential as a stochastic model, especially for hydroclimatic data sets that embed NSO processes.
Multivariate Stochastic Simulation of Climate Indices with EMD and NSOR
Taesam Lee,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
Climate indices generally contain nonstationary oscillations (NSO). Not much study has been done in the literature to reproduce the NSO processes through a stochastic time series model. Therefore, we proposed a model that reproduces the NSO of climate indices employing EMD- NSO resampling (NSOR) technique. The proposed simulation model was tested with three climate indices (i.e. AO, ENSO, and PDO) for the annual and winter (January, February, and March - JFM) datasets. The results of the proposed model are compared with the ones of the Contemporaneous Shifting Mean and Contemporaneous Autoregressive Moving Average (CSM-CARMA) model. One set of the 5000 year records is simulated from each model. The results (ex. Figure 1) indicated that the proposed model is superior to the CSM-CARMA model for reproducing the NSO process while the other basic statistics are comparatively well preserved in both models.
Application of Total least square method to rating curves for streamflow estimation
Taesam Lee,Byungsoo Kim,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
The ordinary least square method (OLS) has been the most frequently used least square method in hydrological data analysis. Its computational algorithm is simple, and the error analysis is also simple and clear. However, the primary assumption of the OLS method, which states that the dependent variable is the only error-contaminated variable and all other variables are error free, is often violated in hydrological data analyses. Recently, a matrix algorithm using the singular value decomposition for the total least square (TLS) method has been developed and used in data analyses as errors-in-variables model where several variables could be contaminated with observational errors. In our study, the algorithm of the TLS is introduced in the evaluation of rating curves between the flow discharge and the water level. Then, the TLS algorithm is applied to real data set for rating curves. The evaluated TLS rating curves are compared with the OLS rating curves, and the result indicates that the TLS rating curve and the OLS rating curve are in good agreement. The TLS and OLS rating curves are discussed about their algorithms and error terms in the study.
Taesam Lee,Juyoung Shin,Junhaeng Heo,Changsam Jeong 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
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 expectation maximization (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.
Long-term forecasting cimate nonstationary oscillation processes using EMD
Taesam Lee,Taha B. M. J. Ouarda 한국방재학회 2014 한국방재학회 학술발표대회논문집 Vol.2014 No.-
We developed a stochastic model that captures long term nonstationary oscillations (NSOs) within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD. To evaluate the model performance, the model is tested with the synthetic data set from Rossler attractor and with global surface temperature anomalies (GSTA) data. The results of the attractor show that the proposed approach provides a good characterization of the NSOs. For GSTA data, the last 30 observations are truncated and compared to the generated data. Then the model is used to predict the evolution of GSTA data over the next 50 years. The results of the case study confirm the power of the EMD approach and the proposed NSO resampling (NSOR) method as well as their potential for the study of climate variables.