Chiayi, Taiwan
Chiayi, Taiwan

Time filter

Source Type

Chuang S.-H.,Asia University, Taiwan | Lin H.-N.,Toko University
International Journal of Information Management | Year: 2013

This study adopts both a resource-based perspective that combines technology, human, and business resources to develop an infrastructure capability, and a strategic-positioning perspective that emphasizes customer orientation to examine customer information quality in customer relationship management (CRM) systems. Specifically, this study examines how firms bundle infrastructure capability and customer orientation to enhance the quality of customer information that enhances customer relationships and firm performance. The results of data gathered from 116 financial service firms in Taiwan suggest that the impact of quality on firm performance begins with infrastructure capability and customer orientation, and that the complementarity between these factors positively influences customer information quality. The results indicate that customer information quality positively affects customer relationship performance, which consequently leads to improvements in overall firm performance. Crown Copyright © 2013.

Lee Y.-F.,Toko University | Chi Y.,Chang Jung Christian University
Engineering Geology | Year: 2011

Landslide is a frequently encountered problem in Taiwan during the heavy rainfall and typhoon seasons (July to September). Assessing the risk of landslide is a challenging task facing both the engineering communities and local authorities. In this paper we present a framework for analyzing the rainfall-induced landslide risk and demonstrate it with a case study of Lushan hot-spring district in Nantou County in central Taiwan. This framework consists of three parts: (1) an approach for determining the conditional probability of landslide under different scenarios, (2) a procedure for estimating the landslide-induced losses, and (3) a procedure for computing the total landslide risk. The results of the detailed case study of the total landslide risk in the Lushan hot-spring district show that the proposed methodology is an effective approach for landslide risk assessment. Use of the methodology for assessing the cost-benefit of an engineering mitigation work proposal is also presented. © 2011 Elsevier B.V.

Tropical cyclones, also known as typhoons or hurricanes, are among the most devastating events in nature and often strike the western North Pacific region (including the Philippines, Taiwan, Japan, Korea, China, and others). This paper focuses on addressing the rainfall retrieval problem for quantitative precipitation forecast during tropical cyclones. In this study, Special Sensor Microwave Imager (SSM/I) data and Water Resources Agency (WRA) measurements of Taiwan were used to quantitatively estimate precipitation over the Tanshui River basin in northern Taiwan. Various retrievals for the rainfall rate over land are compared by five methods/techniques. They are the single-channel regression, multichannel linear regressions (MLR), scattering index over land approach (SIL), support vector regression (SVR), and the proposed SIL-SVR. This study collected 70 typhoons affecting the studied watershed over the past 12 years (1997-2008). The measurements of the SSM/I satellite comprise the brightness temperatures at 19.35, 22.23, 37.0, and 85.5 GHz. Overall, the results showed the approaches using the SVR and conjoined SVR and SIL performed better than regression and SIL methods according to their performances of the root-mean-square error (RMSE), bias ratio, and equitable threat score (ETS). © 2012 American Meteorological Society.

Specific primers play an important role in polymerase chain reaction (PCR) experiments, and therefore it is essential to find specific primers of outstanding quality. Unfortunately, many PCR constraints must be simultaneously inspected which makes specific primer selection difficult and time-consuming. This paper introduces a novel computational intelligence-based method, Teaching-Learning-Based Optimisation, to select the specific and feasible primers. The specified PCR product lengths of 150- 300 bp and 500-800 bp with three melting temperature formulae of Wallace's formula, Bolton and McCarthy's formula and SantaLucia's formula were performed. The authors calculate optimal frequency to estimate the quality of primer selection based on a total of 500 runs for 50 random nucleotide sequences of 'Homo species' retrieved from the National Center for Biotechnology Information. The method was then fairly compared with the genetic algorithm (GA) and memetic algorithm (MA) for primer selection in the literature. The results show that the method easily found suitable primers corresponding with the setting primer constraints and had preferable performance than the GA and the MA. Furthermore, the method was also compared with the common method Primer3 according to their method type, primers presentation, parameters setting, speed and memory usage. In conclusion, it is an interesting primer selection method and a valuable tool for automatic high-throughput analysis. In the future, the usage of the primers in the wet lab needs to be validated carefully to increase the reliability of the method. © The Institution of Engineering and Technology 2014.

Wei C.-C.,Toko University
Water Resources Management | Year: 2012

This paper employed two classical, popular decision-tree algorithms (C5. 0 and CART), and traditional Regression to deal with reservoir operations regarding decision of the releases from a reservoir system during floods. The experiment site was in Shihmen Reservoir, located in northern Taiwan. In a typical single-peak typhoon, the rules derived include two operational stages, the stage before peakflow (Stage I) and the stage after peakflow (Stage II). This study collected 50 typhoons (1987-2009). Four cases are designed, that are discretized class labels (target fields) are run by C5. 0 and CART (i. e., Cases 1 and 2, respectively), while numeric class labels are run by CART and Regression (i. e., Cases 3 and 4, respectively). The criteria of root mean square error (RMSE), coefficient of efficiency (CE), and relative error of peak discharge (EQ p) were used to evaluate the forecasts. Results showed that the decision trees are skillful in the prediction of reservoir releases in the studied site. Furthermore, it was found that CART regression trees with numeric targets are more appropriate and precise than C5. 0 classification trees and Regression for the prediction of releases. © 2012 Springer Science+Business Media B.V.

Prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. This study aims to address the rainfall prediction problem for quantitative precipitation forecasts over land during typhoons. To improve the efficiency of forecasting typhoon precipitation, this study develops Bayesian network (BN) and logistic regression (LR) models using three different datasets and examines their feasibility under different rain intensities. The study area is the watershed of the Tanshui River in Taiwan. The dataset includes a total of 70 typhoon events affecting the watershed from 1997 to 2008. For practicability, the three datasets used include climatologic characteristics of typhoons issued by the Central Weather Bureau (CWB), rainfall rates measured using automatic meteorological gauges in the watershed, and microwave data originated from Special Sensor Microwave Imager (SSM/I) radiometers. Five separate BN and LR models (cases), differentiated by a unique combination of input datasets, were tested, and their predicted rainfalls are compared in terms of skill scores including mean absolute error (MAE), RMSE, bias (BIA), equitable threat score (ETS), and precision (PRE). The results show that the case where all three input datasets are used is better than the other four cases. Moreover, LR can provide better predictions than BN, especially in flash rainfall situations. However, BN might be one of the most prominent approaches when considering the ease of knowledge interpretation. In contrast, LR describes associations, not causes, and does not explain the decision. © 2013 American Meteorological Society.

This paper presents a novel algorithm, wavelet support vector machines (wavelet SVMs), for forecasting the hourly water levels at gauging stations. These stations are under strong precipitations and affected by tidal effects during typhoons. An admissible wavelet kernel SVMs implements the combination of wavelet technique with SVMs. The wavelet is a multi-dimension wavelet function that can approximate arbitrary nonlinear functions. Using both classical Gaussian and wavelet SVMs, this study constructed the channel level models for forecasting downstream water levels. The developed models were then applied to the Tanshui River Basin in Taiwan and the water levels at various lag times predicted by both Gaussian and wavelet SVMs were compared. Analysis results showed that the optimal situation occurred at the lag time of 3 h with relative mean square errors (RMSEs) of 0.205 and 0.160 m obtained by the Gaussian and wavelet SVMs, respectively at Taipei Bridge station and RMSEs of 0.154 and 0.092 m at Tudigong station, respectively. As seen in the comparison, wavelet SVMs yielded more accurate predictions than Gaussian SVMs and offered a practical solution to the problem of water-level predictions during typhoon attacks. © 2011 Elsevier Ltd. All rights reserved.

This study presents two support vectormachine (SVM) basedmodels for forecasting hourly precipitation during tropical cyclone (typhoon) events. The two SVM-based models are the traditional Gaussian kernel SVMs (GSVMs) and the advanced wavelet kernel SVMs (WSVMs). A comparison between the fifthgeneration Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model (MM5) and statistical models, including SVM-based models and linear regressions (regression), was made in terms of performance of rainfall prediction at the Shihmen Reservoir watershed in Taiwan. Data from 73 typhoons affecting the Shihmen Reservoir watershed were included in the analysis. This study designed six attribute combinations with different lag times for the forecast target. The modified RMSE, bias, and estimated threat score (ETS) results were employed to assess the predicted outcomes. Results show that better attribute combinations for typhoon climatologic characteristics and typhoon precipitation predictions occurred at 0-h lag time with modified RMSE values of 0.288, 0.257, and 0.296 in GSVM, WSVM, and the regression, respectively. Moreover, WSVM having average bias and ETS values close to 1.0 gave better predictions than did the GSVM and regression models. In addition, Typhoons Zeb (1998) and Nari (2001) were selected for comparison between the MM5 model output and the developed statistical models. Results showed that the MM5 tended to overestimate the peak and cumulative rainfall amounts while the statistical models were inclined to yield underestimations. © 2012 American Meteorological Society.

The forecast of precipitations during typhoons has received much attention in recent years. It is important in meteorology and atmospheric sciences. Hence, the study on precipitation nowcast during typhoons is of great significance to operators of a reservoir system. This study developed an improved neural network that combines the principal component analysis (PCA) technique and the radial basis function (RBF) network. The developed methodology was employed to establish the quantitative precipitation forecast model for the watershed of the Shihmen Reservoir in northern Taiwan. The results obtained from RBF, multiple linear regression (MLR), PCA-RBF, and PCA-MLR models included the forecasts of L-ahead (L51, 3, 6) hourly accumulated precipitations. The deducted prediction results were compared in terms of four measures [mean absolute error (MAE), RMSE, coefficient of correlation (CC), and coefficient of efficiency (CE)] and four skill scores [percentage error (PE), area-weighted error score (AWES), bias score (BIAS), and equitable threat score (ETS)]. The results showed that predictions obtained using RBF and PCA-RBF were better than those produced by MLR and PCA-MLR. Although both RBF and PCA-RBF can provide good results on average, the network architecture and the learning speed of the PCA-RBF network are superior to those of the simple RBF network. This is because PCA technique could greatly reduce the input parameters and simplify concurrently the network structure. Consequently, the PCA-RBF neural networks can be regarded as a reliable model for predicting precipitation during typhoons. © 2012 American Meteorological Society.

Wei C.-C.,Toko University
Applied Soft Computing Journal | Year: 2013

This study presented various soft computing techniques for forecasting the hourly precipitations during tropical cyclones. The purpose of the current study is to present a concise and synthesized documentation of the current level of skill of various models at precipitation forecasts. The techniques involve artificial neural networks (ANN) comprising the multilayer perceptron (MLP) with five training methods (denoted as ANN-1, ANN-2, ANN-3, ANN-4, and ANN-5), and decision trees including classification and regression tree (CART), Chi-squared automatic interaction detector (CHAID), and exhaustive CHAID (E-CHAID). The developed models were applied to the Shihmen Reservoir Watershed in Taiwan. The traditional statistical models including multiple linear regressions (MLR), and climatology average model (CLIM) were selected as the benchmarks and compared with these machine learning. A total of 157 typhoons affecting the watershed were collected. The measures used include numerical statistics and categorical statistics. The RMSE criterion was employed to assess the suitable scenario, while the categorical scores, bias, POD, FAR, HK, and ETS were based on the rain contingency table. Consequently, this study found that ANN and decision trees provide better prediction compared to traditional statistical models according to the various average skill scores. © 2012 Elsevier B.V. All rights reserved.

Loading TOKO University collaborators
Loading TOKO University collaborators