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Machine learning approaches are increasingly being employed to forecast the key characteristics of strong ground motions, including the challenging classification of Pulse-Like (PL) ground motions. The PL ground motions are characterized by their impulsive nature and have the potential to cause significant damage to structures. The classification of PL ground motions continues to be a significant challenge due to the absence of consensus on their definition and categorization. This paper investigates the potential benefits of several Machine Learning Classifiers (MLCs) algorithms such as decision tree, random forest, logistic regression, naive Bayes, support vector machine, K-nearest neighbor, ensemble model, and artificial neural network models for predicting PL and Non-Pulse-Like (NPL) ground motions. In this regard, a dataset comprising 200 near-fault ground motions records compiled from active tectonic regions like Taiwan, Turkey, Iran, and Japan, was divided into 2 portions, with 75% used to train the model and the remaining 25% used for testing. Plots of performance metrics, confusion matrix, and receiver operating curve indicate that the ensemble classifier outperforms the other classifier with 86.2% accuracy and the lowest misclassification of PL and NPL ground motions. Additionally, the trained MLC has been compared with the existing ground motion classification models to further assess the accuracy of the different classifiers in the present study. [ABSTRACT FROM AUTHOR]

Missing values are common in the Internet of Things (IoT) environment for various reasons, including regular maintenance or malfunction. In time-series prediction in the IoT, missing values may have a relationship with the target labels, and their missing patterns result in informative missingness. Thus, missing values can be a barrier to achieving high accuracy of prediction and analysis in data mining in the IoT. Although several methods have been proposed to estimate values that are missing, few studies have investigated the comparison of interpolation methods using conventional and deep learning models. There has thus far been relatively little research into interpolation methods in the IoT environment. To address these problems, this paper presents the use of linear regression, support vector regression, artificial neural networks, and long short-term memory to make time-series predictions for missing values. Finally, a full comparison and analysis of interpolation methods are presented. We believe that these findings can be of value to future work in IoT applications. [ABSTRACT FROM AUTHOR]

Streamflow forecasting is significantly important for planning and operating water resource systems. However, streamflow formation is a highly nonlinear, time varying, spatially distributed process and difficult to forecast. This paper proposes a nonlinear model which incorporates improved real-coded grammatical evolution (GE) with a genetic algorithm (GA) to predict the ten-day inflow of the De-Chi Reservoir in central Taiwan. The GE is a recently developed evolutionary-programming algorithm used to express complex relationships among long-term nonlinear time series. The algorithm discovers significant input variables and combines them to form mathematical equations automatically. Utilizing GA with GE optimizes an appropriate type of function and its associated coefficients. To enhance searching efficiency and genetic diversity during GA optimization, the macro-evolutionary algorithm (MA) is processed as a selection operator. The results using an example of theoretical nonlinear time series problems indicate that the proposed GEMA yields an efficient optimal solution. GEMA has the advantages of its ability to learn relationships hidden in data and express them automatically in a mathematical manner. When applied to a real world case study, the fittest equation generated through GEMA used only a single input variable in a reasonable nonlinear form. The predicting accuracies of GEMA were better than those of the traditional linear regression (LR) model and as good as those of the back-propagation neural network (BPNN). In addition, the predicting of ten-day reservoir inflows reveals the effectives of GEMA, and standardization is beneficial to model for seasonal time series. [ABSTRACT FROM AUTHOR]

In order to determine the optimal hourly releases from reservoirs under the estuary tidal effects during typhoon periods, this paper develops a generalized multipurpose multireservoir optimization model for basin-scale flood control. The model objectives include: preventing the reservoir dam and the downstream river embankment from overtopping, and meeting reservoir target storage at the end of flood. The model constraints include the reservoir operations and the neural-based linear channel level routing. The proposed channel level routing developed from the feed-forward back-propagation neural network is employed to estimate the downstream water levels. The developed optimization model has been applied to the Tanshui River Basin system in Taiwan. The results obtained by the optimization model, in contrast to historical records, demonstrate successfully the practicability in solving the problem of flood control operations. [ABSTRACT FROM AUTHOR]

Total electron content (TEC) precursors of the Chi-Chi Earthquake, which occurred in Taiwan at 01:47:15 on September 21, 1999 (Taiwan Standard Time, TST), with its epicenter at 23.85° N and 120.82° E, a Richter magnitude (ML) of 7.3 (a moment magnitude (Mw) of 7.6), and a focal depth of 8.00 km, were detected 1, 3, and 4 days before the earthquake using two back-propagation neural network (BPNN) models. These results are consistent with the analysis results of Liu et al. (2001) and Lin (2010). Another TEC precursor was detected on May 13, 2003 (TST), 2 days before the earthquake on May 15, 2003 (TST), with an ML of 5.21. Their precursors might be induced by the variations of the geomagnetic and electric fields near their hypocenter. The two BPNN models were to be verified for stability and reliability after performing cross-validation and evaluating the variance in the learning process. For this, the result of the analyzed method can serve as a real-time TEC predicted system for giving any future time as inputs of the two BPNN models. Hence, an earthquake early warning (EEW) system is performable. [ABSTRACT FROM AUTHOR]

The article presents the development of a triage prediction model using emergency department patient data at a Taiwanese Teaching Hospital. Topics discussed include the reference to the purpose of a triage to avoid treatment delays for patients in cases of crowded hospital facilities, the integration of principal component analysis (PCA) with the support vector machine (SVM) to determine anomaly detection (overestimate or underestimate) and the support vector regression (SVR) algorithm.

In Taiwan, owing to the nonuniform temporal and spatial distribution of rainfall and high mountains all over the country, hydrologic systems are very complex. Therefore, preventing and controlling flood disasters is imperative. Nevertheless, water level and flow records are essential in hydrological analysis for designing related water works of flood management. Due to the complexity of the hydrological process, reliable runoff is hardly predicted by applying linear and non-linear regression methods. Therefore, in this study, a model for estimating runoff by using rainfall data from a river basin is developed and a neural network technique is employed to recover missing data. For achieving the objectives, hourly rainfall and flow data from Nanhe, Taiwu, and Laii rainfall stations and Sinpi flow station in the Linbien basin are used. The data records were of 27 typhoons between the years 2005 and 2009. The feed forward back propagation network (FFBP) and conventional regression analysis (CRA) were employed to study their performances. From the statistical evaluation, it has been found that the performance of FFBP exceeded that of regression analysis as reflected by the determination coefficients R, which were 0.969 and 0.284 for FFBP and CRA, respectively. [ABSTRACT FROM AUTHOR]

The mobile phone manufacturers in Taiwan have made great efforts in proposing the rational quotations to the international phone companies with the ambition to win the bids by out beating other phone manufacturers. However, there are a lot of uncertainties and issues to be resolved in estimating the manufacturing costs for mobile phone manufacturers. As far as we know, there is no existing model which can be applied directly in forecasting the manufacturing costs. This research makes the first attempt to develop a hybrid system by integrated Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as a Product Unit Cost (PUC) forecasting model for Mobile Phone Company. According to the cost formula of the mobile phone and experts' opinions, a set of qualitative and quantitative factors are analyzed and determined. Qualitative factors are applied in CBR to retrieve a similar case from the case bases for a new phone product and ANN is used to find the relationship between the quantitative factors and the predicted PUC. Finally, intensive experiments are conducted to test the effectiveness of six different forecasting models. The model proposed in this research is compared with the other five models and the MAPE value of the proposed model is the smallest. This research provides a new prediction model with high accuracy for mobile phone manufacturing companies. [ABSTRACT FROM AUTHOR]

The article presents a study on the integration of adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods to forecast the association of asthma susceptibility genes on three serum IgE groups among children in Taiwan. It examines 13 single-nucleotide polymorphisms (SNPs) in seven well-known asthma susceptibility genes. It concludes that ANFIS and classified analysis have association discovery.

Objectives: To predict the nutrition and health status of staff and students in Yuan Ze University and select the influential variables from the total body composition variables, which should have similar predictive ability with the whole factors. Design: Spontaneous and voluntary physical examination. Setting: Sanitary & Health Care Section of Yuan Ze University in Taiwan. Participants: 1227 staff and students. Measurements: With the help of Inbody720TM, 139 body composition variables were measured and 60 variables were retained after data pre-processing. An ensembled artificial neural networks (EANN) prediction model was established and seven different methods for assessing variables importance were applied. Besides, classical linear and logistic regression models were developed for comparison with EANN prediction results. Results: The prediction performance of EANN model was satisfactory (RMSE (train) = 0.2686, RMSE (validation) = 0.2648, RMSE (test) = 0.3492). Since both the actual and simulation fitness score were at the range of 0 to 100, according to rounding off rule, the simulated value was almost the same with actual value. Besides, 12 important variables were obtained by seven methods for quantifying variable importance in EANN, which had similar predictive capability with 60 variables (RMSE (train) = 0.3263, RMSE (validation) = 0.322, RMSE (test) = 0.3226). The linear and logistic regression models results were both evidently worse than EANN results. Conclusion: The results confirm that EANN is appropriate to approximate such a complicated, non-invasive and highly non-linear problem as body composition analysis. It can be helpful for nutritionists to manage and improve the nutrition and health condition of staff and students, by adjusting the 12 most important variables. [ABSTRACT FROM AUTHOR]

Debris flows caused serious loss of human lives and damages to properties in Taiwan for the past decades. A number of methods for prediction of debris flows have been studied including numerical method, statistic method, experiment method and neural network method in recent years. This study proposed a genetic algorithm (GA) model for occurrence prediction of debris flows. A total of 154 potential cases of debris flows collected in eastern Taiwan were fed into the GA for training and testing. The average ratio of successful prediction reaching 90.4% demonstrates that the presented GA model can provide a stable and reliable result for prediction of debris flows in the hazard mitigation and guarding system. [ABSTRACT FROM AUTHOR]

Forecasting and monitoring of rainfall values are increasingly important for decreasing economic loss caused by flash floods. Based on statistical learning theory, support vector regression (SVR) has been used to deal with forecasting problems. Performing structural risk minimization rather than minimizing the training errors,SVR algorithms have better generalization ability than the conventional artificial neural networks. The objective of this investigation is to examine the feasibility and applicability of SVR in forecasting volumes of rainfall during typhoon seasons. In addition, Simulated Annealing (SA) algorithms are employed to choose parameters of the SVR model. Subsequently, rainfall values during typhoon periods in Taiwan's Wu-Tu watershed are used to demonstrate the forecasting performance of the proposed model. The simulation results show that the proposed SVRSA model is a promising alternative in forecasting amounts of rainfall during typhoon seasons. [ABSTRACT FROM AUTHOR]

We investigate the possibility of applying artificial intelligence to solve an audit decision problem faced by the public sector (namely, the tax auditor of the Internal Revenue Services) when targeting firms for further investigation. We propose that the neural network will overcome problems faced by a direct knowledge acquisition method in building an expert system to preserve the expertise of senior auditors of the IRS in Taiwan. An explanation of the neural network theory is provided with regard to multi- and single-layered neural networks. Statistics reveal that the neural network performs favorably, and that three-layer networks perform better than two-layer neural networks. The results strongly suggest that neural networks can be used to identify firms requiring further auditing investigation, and also suggest future implications for intelligent auditing machines. [ABSTRACT FROM AUTHOR]