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This paper explores the possibilties of having the fourth time party turnover in Taiwain's 2024 presidential election after the ruling DPP was serverely defeated in the 2022 local elections. It first reviews Taiwan's previous election cycles at both the presidential and local levels, since 1996. It then identifies theoretical elements from Taiwan's prior experiences for the purpose of further theorizing party turnover mechanisms. Finally, the theoretical framework that this paper constructs is utilized to examine the political tides of the two major political parties, the DPP and KMT, in Taiwan after the 2022 local elections, and to delineate both certain and uncertain factors that influence whether a fourth party turnover is about to occur in Taiwan. [ABSTRACT FROM AUTHOR]

Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state's electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November. [ABSTRACT FROM AUTHOR]

Bankruptcy prediction is considered one of the most important research topics in the field of finance and accounting. The rapid increase of data science, artificial intelligence, and machine learning has led researchers to build an accurate bankruptcy prediction model. Recent studies show that ensemble methods perform better than traditional machine learning models for predicting corporate failure, especially with highly imbalanced datasets. However, the black box property of these techniques remains challenging to interpret the result and generate corporate classes without any explanation. To this end, we propose to build an accurate and interpretable classification model that generates a set of prediction rules for output. Tri-eXtreme Gradient Boosting (Tri-XGBoost), a semi-supervised technique, is recommended in this paper. The proposed method combines Borderline-Smote (BLSmote) based on Edited Nearest Neighbor (ENN) sampling techniques with three different XGBoost methods as weak classifiers (gbtree, gblinear, and dart). First, the resampling techniques are used to produce more representative synthetic data and balance the distribution of the datasets. To this end, BLSmote is applied to increase the proportion of instances in the minority class (bankrupt data). Then, ENN is used to eliminate the noisy samples from both classes. In addition, the most crucial features that increase predictive accuracy are chosen using XGBoost. Finally, in order to make the model more understandable for both applicants and experts, our result is presented as "IF–THEN" rules. Our proposed model is validated using the imbalanced Polish and Taiwan bankruptcy datasets. Our obtained results demonstrate that our suggested model performs better than the existing models based on the area under the ROC curve (AUC), F1-score, and G-mean performance measures. Our proposed model significantly improves classification accuracy, which is greater than 95% for Polish datasets and more than 93% for Taiwanese dataset in terms of AUC, G-mean and F1-score. [ABSTRACT FROM AUTHOR]

Forecasting of PM2.5 concentration is a global concern. Evidence has shown that the ambient PM2.5 concentrations are harmful to human health, climate change, plant species mortality, etc. PM2.5 concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM2.5 prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM2.5 forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM2.5 forecasting in Delhi, India. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting. Design/methodology/approach: The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components. Findings: Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed. Originality/value: To the best of the authors' knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction is an important basis of intelligent transportation systems. Its accuracy directly affects the performance of traffic control and induction. To improve prediction accuracy, in this paper, a traffic flow prediction model is proposed by combining hierarchical agglomerative clustering (HAC), standardized Euclidean distance (SED), and a long short-term memory network (LSTM). The proposed model is called the CHS-LSTM model. In this model, HAC is used to carry out cluster analysis on the original traffic flow data sample set of target detection sections. Based on the results of cluster analysis, traffic flow data are divided into several categories. For different categories of traffic flow, SED is used to calculate the spatial correlation of the road network where the target sections are located, and the K sections most relevant to the target detection sections are used in the construction of an input data matrix for LSTM. The prediction result with the minimum root-mean-square error is regarded as the final prediction result. The electronic toll collection data of Taiwan are used as the fundamental data in this paper to verify the performance and effectiveness of the CHS-LSTM model. The experimental results indicate that the CHS-LSTM model can effectively improve the prediction accuracy of LSTM. Moreover, compared with well-known models generally used for predicting the intensity of traffic flow, our proposed model also shows its superiority. [ABSTRACT FROM AUTHOR]

The COVID-19 data analysis is essential for policymakers to analyze the outbreak and manage the containment. Many approaches based on traditional time series clustering and forecasting methods, such as hierarchical clustering and exponential smoothing, have been proposed to cluster and forecast the COVID-19 data. However, most of these methods do not scale up with the high volume of cases. Moreover, the interactive nature of the application demands further critically complex yet compelling clustering and forecasting techniques. In this paper, we propose a web-based interactive tool to cluster and forecast the available data of Taiwan COVID-19 confirmed infection cases. We apply the Model-based (MOB) tree and domain-relevant attributes to cluster the dataset and display forecasting results using the Ordinary Least Square (OLS) method. In this OLS model, we apply a model produced by the MOB tree to forecast all series in each cluster. Our user-friendly parametric forecasting method is computationally cheap. A web app based on R's Shiny App makes it easier for practitioners to find clustering and forecasting results while choosing different parameters such as domain-relevant attributes. These results could help in determining the spread pattern and be utilized by medical researchers. [ABSTRACT FROM AUTHOR]

The Taiwanese government altered its corporate social responsibility (CSR) report management policy from voluntary disclosure and assurance of CSR reports to partial mandatory disclosure and partial mandatory assurance. This paper examines this policy's effects on analyst forecast. The empirical results showed that the mandatory disclosure policy on CSR reports significantly increased analyst forecast accuracy and reduced analyst forecast dispersion. Furthermore, the study found that analyst forecast accuracy was further increased when CSR reports were forced to undergo accountant assurance than those without mandatory accountant assurance which means that the mandatory assurance policy on CSR reports significantly further increased analyst forecast accuracy. [ABSTRACT FROM AUTHOR]

In response to relatively little evidence on the determinants of the financial distress in cooperative financial institutions (e.g., Credit Unions), this paper proposes a distress indicator of Merton Distance to default (Merton DD), which was constructed with a z-score, possessed improved predictive capability, but reducing equity volatility. This model possesses the advantages of both hazard and modified Merton DD model, which could timely reflect market volatility and predict when distress would occur. As a demonstration, we applied this model to forecast the financial distress of credit unions in Taiwan. The results can provide more information to researchers. [ABSTRACT FROM AUTHOR]

In recent years, the development and application of artificial intelligence have both been topics of concern. In the medical field, an important direction of medical technology development is the extraction and use of applicable information from existing medical records to provide more accurate and helpful diagnosis suggestions. Therefore, this paper proposes using the development of diseases with easily discernible symptoms to predict the development of other medically related but distinct diseases that lack similar data. The aim of this study is to improve the ease of assessing the development of diseases in which symptoms are difficult to detect, and to improve the utilization of medical data. First, a time series model was used to capture the continuous manifestations of diseases with symptoms that could be easily found at different time intervals. Then, through transfer learning and attention mechanism, the general features captured were applied to the predictive model of the development of diseases with insufficient data and symptoms that are difficult to detect. Finally, we conducted a comprehensive experimental study based on a dataset collected from the National Health Insurance Research Database in Taiwan. The results demonstrate that the effectiveness of our transfer learning approach outperforms state-of-the-art deep learning prediction models for disease course prediction. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to evaluate why school-based assessment cannot be considered to compare the sequence of senior high school admissions that exceed the quota. Method: The study sample comprises 1,650 graduates whose Basic Competency Test (BCTEST) were considered for their high school admission in Taiwan during 2009--2013. The statistical forecasting methods used are fuzzy time series and self-regression. Result: The result is that the development trend of every student is considered by using the forecasting scores of fuzzy time series with the 17 stages of interval fuzzy scores. [ABSTRACT FROM AUTHOR]

A new methodology is suggested in this study to provide optimum forecasting of the future transportation energy demand in Taiwan. The paper introduces a new improved version of Emperor Penguin Optimizer (IEPO) to provide an optimal and suitable forecasting model. The forecasting was based on three different models including linear, exponential, and quadratic where their coefficients have been optimized using the suggested IEPO algorithm which is based on considering the population, the GDP growth rate, and the total annual vehicle-km. The study considers two different scenarios based on curve fitting and projection data. The results indicate that the RMS value for the TED forecasting based on the proposed IEPO algorithm applied to the linear, exponential, and Quadratic for training are 0.0452, 0.0461, and 0.0492, respectively and for testing are 0.0456, 0.0596, and 0.0642, respectively. This shows better results of the optimized exponential method's efficiency. Simulation results showed high efficiency for the proposed IEPO-based transportation energy demand forecasting based on all of the employed models for decision-making in ROC. [ABSTRACT FROM AUTHOR]

Short-term hourly reliable prediction of significant wave height is an important research topic in coastal engineering. Many researchers have carried out in-depth studies in many ocean regions. Generally, most of this work is implemented through numerical models. However, as for numerical models, with the increase of hourly prediction duration, the accumulation of wave randomness leads to the poor prediction effect. In this paper, four buoy stations in the Taiwan Strait are taken as the research objects. We propose a significant wave height prediction algorithm, which combines numerical weather prediction model WRF and deep-learning model, called WRF-CLSF(Convolution-LSTM-FC). WRF can forecast 24-h wind speed in real-time, based on a variety of interpretable physical mechanisms. CLSF aims to extract the information of historical wind-wave interaction scale and the trend of wind-wave coherence, with the help of convolution operation and the time series model(LSTM), respectively. In the experiment, the proposed model was compared with the state-of-the-art prediction model. The results show that WRF-CLSF has an outstanding prediction effect at four buoy observation stations along with the Taiwan Strait. [ABSTRACT FROM AUTHOR]

This paper examines from a multilevel perspective the contributions of initiative climate, psychological safety and knowledge sharing to team creativity in research and development (R&D) project teams. We propose and test an organizational learning contingency model of creativity, reflecting on how initiative climate interacts with employees' psychological safety in influencing knowledge sharing and teams' creative performance, by collecting data from 352 employees comprising 88 R&D teams in Taiwan at two time points and from two sources, team members and team leaders. Results indicated that knowledge sharing acts as a mediator in the relationship between initiative climate and team creativity. Results also supported the cross‐level moderation effect of initiative climate on the relationship between psychological safety and knowledge sharing behaviour. We also made the interesting but unhypothesized findings that tenure differentiation and education diversity both negatively influenced team creativity and go on to discuss both theoretical and practical implications of our research. [ABSTRACT FROM AUTHOR]

Short-term load forecast (STLF) plays an important role in power system operations. This paper proposes a spline bases-assisted Recurrent Neural Network (RNN) for STLF with a semi-parametric model being adopted to determine the suitable spline bases for constructing the RNN model. To reduce the exposure to real-time uncertainties, interpolation is achieved by an adapted mean adjustment and exponentially weighted moving average (EWMA) scheme for finer time interval forecast adjustment. To circumvent the effects of forecasted apparent temperature bias, the forecasted temperatures issued by the weather bureau are adjusted using the average of the forecast errors over the preceding 28 days. The proposed RNN model is trained using 15-min interval load data from the Taiwan Power Company (TPC) and has been used by system operators since 2019. Forecast results show that the spline bases-assisted RNN-STLF method accurately predicts the short-term variations in power demand over the studied time period. The proposed real-time short-term load calibration scheme can help accommodate unexpected changes in load patterns and shows great potential for real-time applications. [ABSTRACT FROM AUTHOR]

Education competitiveness is a key feature of national competitiveness. It is crucial for nations to develop and enhance student and teacher potential to increase national competitiveness. The decreasing population of children has caused a series of social problems in many developed countries, directly affecting education and com.petitiveness in an international environment. In Taiwan, a low birthrate has had a large impact on schools at every level because of a substantial decrease in enrollment and a surplus of teachers. Therefore, close attention must be paid to these trends. In this study, combining a whale optimization algorithm (WOA) and support vector regression (WOASVR) was proposed to determine trends of student and teacher numbers in Taiwan for higher accuracy in time-series forecasting analysis. To select the most suitable support vector kernel parameters, WOA was applied. Data collected from the Ministry of Education datasets of student and teacher numbers between 1991 and 2018 were used to examine the proposed method. Analysis revealed that the numbers of students and teachers decreased annually except in private primary schools. A comparison of the forecasting results obtained from WOASVR and other common models indicated that WOASVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) for all analyzed datasets. Forecasting performed using the WOASVR method can provide accurate data for use in developing education policies and responses., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Stephanie Yang et al.)

Dengue and influenza-like illness (ILI) are two of the leading causes of viral infection in the world and it is estimated that more than half the world's population is at risk for developing these infections. It is therefore important to develop accurate methods for forecasting dengue and ILI incidences. Since data from multiple sources (such as dengue and ILI case counts, electronic health records and frequency of multiple internet search terms from Google Trends) can improve forecasts, standard time series analysis methods are inadequate to estimate all the parameter values from the limited amount of data available if we use multiple sources. In this paper, we use a computationally efficient implementation of the known variable selection method that we call the Autoregressive Likelihood Ratio (ARLR) method. This method combines sparse representation of time series data, electronic health records data (for ILI) and Google Trends data to forecast dengue and ILI incidences. This sparse representation method uses an algorithm that maximizes an appropriate likelihood ratio at every step. Using numerical experiments, we demonstrate that our method recovers the underlying sparse model much more accurately than the lasso method. We apply our method to dengue case count data from five countries/states: Brazil, Mexico, Singapore, Taiwan, and Thailand and to ILI case count data from the United States. Numerical experiments show that our method outperforms existing time series forecasting methods in forecasting the dengue and ILI case counts. In particular, our method gives a 18 percent forecast error reduction over a leading method that also uses data from multiple sources. It also performs better than other methods in predicting the peak value of the case count and the peak time., Competing Interests: The authors have declared that no competing interests exist.

Background: The province of Formosa, Argentina, is endemic for leprosy. In the present paper, we assessed the trend (T, 2002–2016 time series) and the forecast for 2022 of new case detection rate (NCDR) and determined the spatial distribution of new cases detected (NCD) of leprosy. Methodology/Principal findings: This is a descriptive observational study of 713 NCD of leprosy from provincial medical records between January 2002 and December 2016. The whole dataset from the provincial medical record was used to independently estimate the NCDR trends of the general population, age groups, sexes and Departments. This same database was used to estimate the NCDR forecast of the general population for 2022, applying a dynamic linear model with a local linear trend, using the MCMC algorithm. The NCDR was higher in men (p<0.05), increased with age (0.20, 8.17, 21.04, and 29.49 for the 0–14, 15–44, 45–64 and over 65-year-old age groups, respectively; p<0.05) and showed a downward trend (negative values) of estimated slopes for the whole province and each Department. Bermejo Department showed the highest (T:-1.02, 95%CI: [-1.42, -0.66]) and Patiño the lowest decreasing trend (T:-0.45, 95%CI: [-0.74, -0.11]). The NCDR trend for both sexes was similar (T:-0.55, 95%CI: [-0.64, -0.46]), and age groups showed a decreasing trend (S15-44:-103, S45-64:-81, S>65:-61, p<0.05), except for the 0–14 age group (S:-3, p>0.05), which showed no trend. Forecasts predicted that leprosy will not be eliminated by 2022 (3.64, 95%CI: [1.22, 10.25]). Conclusions/Significance: Our results highlight the status of leprosy in Formosa and provide information to the provincial public health authorities on high-risk populations, stressing the importance of timely detection of new cases for further elimination of the disease in the province. Author summary: Leprosy, a neglected tropical disease, is a public health problem in northern Argentina, causing permanent damage, stigmatization and discrimination. The Program for the Control of Leishmaniasis and Leprosy in Formosa province (PCLyLF) is responsible for the diagnosis and treatment of the disease and the active search for household contacts. The present study aimed to determine the spatial distribution of new cases of leprosy and the status of the disease using new case detection rate (NCDR) as the main epidemiological indicator. We determined the NCDR trends (2002–2016 time series) of the general population, age groups, sexes and Departments, and the NCDR forecast for the province by 2022. Our results indicated that leprosy was spread throughout Formosa, with higher rates in men and in patients over 65 years old. The NCDR showed a downward trend in the whole province and in each Department. The Departments of Bermejo and Matacos had the highest decreasing trend and the Department of Patiño the lowest one. Future projections show that leprosy will not be eliminated by 2022. [ABSTRACT FROM AUTHOR]

This paper constructs a financial distress prediction model that includes not only traditional financial variables, but also several important corporate governance variables. Using data from Taiwan, the empirical results show that the best in‐sample and out‐of‐sample prediction models should combine the financial variables with the corporate governance variables. Moreover, the prediction accuracy is higher for the models using dynamic distress threshold values than those with tradition threshold values. Most financial ratios, except for the debt ratio, are higher in financially sound companies than in financial distressed ones. With regard to the corporate governance variables, we find that the CEO/Chairman duality may not result in the outbreak of financial distress, but higher equity pledge ratios of managers (shareholding ratios by board members and insiders) positively (negatively) correlate with financial distress. [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]

• A critical analysis of existing review papers are provided. • Geographical research distribution and gaps on End of life PV modules are depicted. • The scientific landscape of EoL PV panels by publication is mapped. • Bibliometric details of the papers in EoL PV management are tabulated. • Patents trend analysis on the treatment procedures of EoL PV modules is presented. PV modules which are installed worldwide have a defined lifetime for useful service after which the panels become End-of-Life (EoL) products. An enormous amount of obsolete solar PV modules will be added to the waste stream in the near future. Hence, the EoL photovoltaic waste stream could cause an appalling problem in the future if a holistic management strategy is not considered. Despite the vast research on photovoltaic technology, little is known about the perspective of how the EoL PV modules will be handled. The current study systematically investigates global research on EoL PV modules to identify gaps for further exploration. The review reveals that most of the research concentrates on the recovery and recycling of PV panels. Also, the vast majority of the research is mostly carried out in laboratory-scale. The geographical distribution of the studies was concentrated on 15 countries including the USA, Italy, and Taiwan, the latter of which has produced the most publications. Life-cycle-assessment and reverse logistics (RL) are two critical aspects of PV waste management and have only recently received attention from researchers, with 11 and six papers respectively. There are still many countries which have not attempted to forecast their EoL solar-panel waste stream and develop recycling infrastructure. Based on review findings, the future research must be focused on forecasting the PV waste streams, development of recycling technologies, reverse logistics and the policies of individual PV consumer countries. Finally, this study develops a foundation for future research on Photovoltaic waste management to build upon. [ABSTRACT FROM AUTHOR]

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This paper investigates the relationship between candidates’ online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan’s 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates’ Facebook posts will be positively related to their election results, I calculated each candidate’s Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate’s Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election. [ABSTRACT FROM AUTHOR]

Daily peak load forecasting is crucial for the operation of bulk power systems, including economic dispatch and unit commitment. It is also essential for peak load shaving and load management in distribution systems. In power markets, peak load forecasting helps participants develop bidding strategies. This paper proposes a new method, using a hybrid convolutional neural network (CNN) that is cascaded with a fully-connected network, for making week-ahead daily peak load forecasts. The proposed method uses three loops to obtain the optimal CNN: The outer loop performs crossover/mutation operations and tournament selection to produce chromosomes to optimize the network topology and hyperparameters (such as kernel size) of the hybrid CNN by genetic algorithms; the middle loop deals with the order of chromosomes; the inner loop optimizes the synaptic weights and parameters (e.g. values of a kernel) using Adam optimizer. Daily peak load data and corresponding meteorological data for Taiwan are explored. Simulation results show that the proposed method outperforms the traditional CNN, multi-layer neural network, recurrent neural network, support vector regression and vector autoregressive moving average model. [ABSTRACT FROM AUTHOR]

Corporate misconduct may lead to severe economic loss and even fatal injuries to workers and residents in the construction industry. Previous studies have proven that board composition in organizations can be related to illegal business behaviors. By analyzing board composition data from 45 publicly listed construction companies in Taiwan, this paper provides a tool for predicting corporate misconduct (CM). A support vector machine (SVM) was used to construct such a prediction model, and a logistic regression model was used as a benchmark to assess the performance of the established SVM model. The established SVM model achieved an accuracy rate of 72.22% for predicting the occurrence of CM when applied to all observations in the sample, with a rate of 90% accuracy in predicting misconduct by companies found guilty of doing so in the sample, thus performing better than the logistic regression model. The developed model yields new insights on previous research and can guide stakeholders to reduce the risk of illegal business acts occurring in the construction industry. [ABSTRACT FROM AUTHOR]

This study develops a novel periodic regime-switching model (the PRS model) to improve the forecasting of stock market volatility by accounting for the information from non-trading and trading periods, including regular trading and after-hour trading. Empirical analysis of the Taiwan Futures Exchange (TAIFEX) demonstrates the significant improvements of the PRS model in both in-sample and out-of-sample periods. Our results also show that the introduction of after-hour trading sessions has provided valuable information for volatility forecasting in subsequent regular trading sessions, emphasizing the importance of considering diverse information flows across different trading and non-trading times. The PRS model effectively captures the dynamics of non-trading and trading sessions and the influence of unusual news arrivals and jumps on market volatility, contributing to investment and risk management strategies. [ABSTRACT FROM AUTHOR]

A hypothesis testing approach, based on the theorem of probability integral transformation and the Kolmogorov–Smirnov one-sample test, for performance evaluation of probabilistic seasonal rainfall forecasts is proposed in this study. By considering the probability distribution of monthly rainfalls, the approach transforms the tercile forecast probabilities into a forecast distribution and tests whether the observed data truly come from the forecast distribution. The proposed approach provides not only a quantitative measure for performance evaluation but also a cumulative probability plot for insightful interpretations of forecast characteristics such as overconfident, underconfident, mean-overestimated, and mean-underestimated. The approach has been applied for the performance evaluation of probabilistic season rainfall forecasts in northern Taiwan, and it was found that the forecast performance is seasonal dependent. Probabilistic seasonal rainfall forecasts of the Meiyu season are likely to be overconfident and mean-underestimated, while forecasts of the winter-to-spring season are overconfident. A relatively good forecast performance is observed for the summer season. [ABSTRACT FROM AUTHOR]

Due to the impact of the COVID-19 pandemic, people have reduced eating out, resulting in a severe drop in the revenue of the catering industry. Health risks have become a major factor affecting the revenue of this industry. Predicting the revenue of the catering industry during the COVID-19 pandemic will not only allow practitioners to adjust their business strategies, but also provide a reference for governments to formulate relief measures. To this end, this study proposes a fuzzy big data analytics approach in which random forests, recursive feature elimination, fuzzy c-means, and deep neural networks are jointly applied. First, random forests and recursive feature elimination are used to select the most influential factors. The data is then divided into clusters by fuzzy c-means. Subsequently, a deep neural network is built for each cluster to make predictions. The prediction results of individual clusters are then aggregated to improve prediction accuracy. The proposed methodology has been applied to forecast the revenue of the catering industry in Taiwan. The results of the experiment showed that the impact of new deaths on the revenue of the catering industry was far greater than the number of newly diagnosed COVID-19 cases. [ABSTRACT FROM AUTHOR]

This paper presents a prediction model based on an improved fuzzy time series (IFTS) and a modified group search optimizer to effectively solve forecasting problems. IFTS can accurately predict whether subsequent predicted data will increase or decrease according to ratio value in the fuzzy logical relationship. In addition, the modified group search optimizer is used to adjust the length of an interval. The proposed prediction model is also used to forecast the enrollments of the University of Alabama the enrollments of a university of technology in central Taiwan, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Experimental results show that the proposed model obtains the smallest prediction error than those of other methods. [ABSTRACT FROM AUTHOR]

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest’s location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF. [ABSTRACT FROM AUTHOR]

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy. [ABSTRACT FROM AUTHOR]

This research models and forecasts bounded ordinal time series data that can appear in various contexts, such as air quality index (AQI) levels, economic situations, and credit ratings. This class of time series data is characterized by being bounded and exhibiting a concentration of large probabilities on a few categories, such as states 0 and 1. We propose using Bayesian methods for modeling and forecasting in zero-one-inflated bounded Poisson autoregressive (ZOBPAR) models, which are specifically designed to capture the dynamic changes in such ordinal time series data. We innovatively extend models to incorporate exogenous variables, marking a new direction in Bayesian inferences and forecasting. Simulation studies demonstrate that the proposed methods accurately estimate all unknown parameters, and the posterior means of parameter estimates are robustly close to the actual values as the sample size increases. In the empirical study we investigate three datasets of daily AQI levels from three stations in Taiwan and consider five competing models for the real examples. The results exhibit that the proposed method reasonably predicts the AQI levels in the testing period, especially for the Miaoli station. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) and zenith total delay (ZTD) are highly correlated indicators used for forecasting rainfall effectively. These two factors are widely used when establishing qualitative and quantitative rainfall forecast models. Another indicator, however, convective available potential energy (CAPE), is also highly correlated with the occurrence of rainfall, but has not been combined with PWV or ZTD in previous studies. Therefore, a novel rainfall forecast model based on support vector regression that combines CAPE and Global Navigation Satellite System (GNSS)-derived PWV is proposed in this paper. Moreover, annual, seasonal scales, and hourly autocorrelation for predictors were also considered, and wavelet coherence (WTC) was introduced to further reveal the correlation relationships between CAPE, PWV and rainfall in both time and frequency domains. Hourly PWV, CAPE, temperature (T) and rainfall over a time span of three years were collected from four stations in Singapore and Taiwan to evaluate the performance of the proposed model. The root mean square error (RMSE) and mean absolute error (MAE) between the measured and forecasted rainfall were calculated for each year and averaged. Subsequently, the average RMSE and MAE were determined for the annual seasonal rainfall. The annual average RMSE value was 0.71 mm and the average annual seasonal RMSE value was 0.69 mm. The annual average MAE value was 0.21 mm and the annual seasonal was 0.22 mm. The average correlation coefficient (R2) for the annual value was 0.94 and the annual seasonal R2 value was 0.93. These results indicate that the forecast accuracy at the seasonal scale is basically consistent with annual scale. Additionally, a measure called comparable RMSE (CRMS) was introduced to evaluate the forecasting accuracy across all grades of rainfall. This analysis showed that the accuracy of moderate rainfall (MR) and heavy rainfall (HR) forecasts is similar, but slight rainfall (SR) forecasts are the most accurate among seasons and schemes. • A short-term rainfall forecast method is proposed using convective available potential energy (CAPE) and GNSS-derived PWV. • The forecast accuracy of seasonal scales is consistent with annual scale. • Results showed that the forecast accuracy of moderate rainfall and heavy rainfall were comparable while that of slight rainfall was highest. [ABSTRACT FROM AUTHOR]

One of the most important sources of energy is the sun. Taiwan is located at a 22–25° north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long–short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model. [ABSTRACT FROM AUTHOR]

Taiwan is highly susceptible to global warming, experiencing a 1.4 °C increase in air temperature from 1911 to 2005, which is twice the average for the Northern Hemisphere. This has potentially led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To enhance the reliability of a forecast, post-processing techniques are essential for addressing systematic errors. In September 2020, the NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and quantile mapping (QQ) approaches to predict daily maximum temperatures (Tmax) and their extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias and RMSE and the highest correlation coefficient and Index of Agreement. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method proved to be more effective in predicting daily Tmax, including extreme Tmax during summer, on extended-range time-scale deterministic and ensemble probabilistic forecasts over Taiwan. [ABSTRACT FROM AUTHOR]

This paper presents an observing system simulation experiment (OSSE) study to examine the impact of dropsonde data assimilation (DA) on rainfall forecasts for a heavy rain event in Taiwan. The rain event was associated with strong southwesterly flows over the northern South China Sea (SCS) after a weakening tropical cyclone (TC) made landfall over southeastern China. With DA of synthetic dropsonde data over the northern SCS, the model reproduces more realistic initial fields and a better simulated TC track that can help in producing improved low-level southwesterly flows and rainfall forecasts in Taiwan. Dropsonde DA can also aid the model in reducing the ensemble spread, thereby producing more converged ensemble forecasts. The sensitivity studies suggest that dropsonde DA with a 12-h cycling interval is the best strategy for deriving skillful rainfall forecasts in Taiwan. Increasing the DA interval to 6 h is not beneficial. However, if the flight time is limited, a 24-h interval of DA cycling is acceptable, because rainfall forecasts in Taiwan appear to be satisfactory. It is also suggested that 12 dropsondes with a 225-km separation distance over the northern SCS set a minimum requirement for enhancing the model regarding rainfall forecasts. Although more dropsonde data can help the model to obtain better initial fields over the northern SCS, they do not provide more assistance to the forecasts of the TC track and rainfall in Taiwan. These findings can be applied to the future field campaigns and model simulations in the nearby regions. [ABSTRACT FROM AUTHOR]

Combination forecasting is an important in the literature on tourism. This study considers three important issues to develop a more accurate combination forecasting method for tourism forecasting. We address the unrealistic requirement related to the statistical properties of the collected data for model fitting, assign changing rather than fixed weights to single models, and incorporate nonlinear relationships among single-model forecasts into forecast combinations. This leads us to develop a three-stage procedure for combination forecasting that consists of generating single-model forecasts with grey prediction models, detecting significant changes in the time series to determine when to update the weights for combining the forecasts, and nonlinearly combining individual forecasts based on a dynamic weighting strategy. In contrast with commonly used fixed weighting, the dynamic weighting used here involves the use of change points to identify the period for which the weights need to be re-estimated. The inbound demand for tourism in Taiwan was used as an empirical case to assess the performance of the proposed framework for forecast combinations. The results show that the nonlinear fuzzy integral with the proposed dynamic weighting strategy significantly outperforms that with the fixed weighting strategy, and has a superior forecasting accuracy than other combined models. [ABSTRACT FROM AUTHOR]

• Nursing manpower needed for the cancer disease healthcare will increase continuously in the future. • The incoordination between teaching and examination has impact on the nursing manpower supply. • By applying traditional fuzzy set to system dynamic, it replaces the definite values with interval values, making the prediction results more flexible. • Manpower for cancer medical care demand is calculated. • Manpower for cancer medical care supply is estimated through data analysis. This paper presents a dynamic model aimed at predicting nursing manpower requirements for cancer care over the next ten years. The proposed model, based on the Taiwan Health Insurance Database (2000 to 2010), is meant to serve as a reference in establishing policy for government health units. The proposed prediction model uses fuzzy sets to replace definite values with interval values in order to account for uncertainties in real-world data and enhance the flexibility of prediction results. Our results suggest that the demand for nursing manpower for cancer care will grow steadily in the foreseeable future. The gap between the demand for nursing staff and the supply is expected to peak in 2027. By then, the number of oncologists is expected to reach 7,083 (54.32% of the total number of in-hospital physicians), but the number of oncology nurses will be less than 26,297 (56.5% of the total healthcare manpower). It is also expected that there will be fewer than 1,613 outpatient physicians (71.81% of the total number of physicians) and fewer than 4,967 outpatient nurses (68.46% of the total nursing manpower). This paper provides a valuable reference for government agencies involved in the nursing manpower planning to improve the quality of nursing care. [ABSTRACT FROM AUTHOR]

This paper examines the usefulness of statistical methods in forecasting the financial energy of households. The study's objective is to create the innovative ratios that combine both financial and demographic information of households and implement them in the forecasting models. To conduct this objective, six forecasting models are developed using three different methods—discriminant analysis, logit analysis, and decision trees separately for households in Poland and Taiwan. Such a research approach will answer the question whether the implementation of constructed ratios can increase effectiveness of the forecasting model and its' versatility between different economic regions. The research relies on four samples of households—two learning samples and two testing samples (one for each country) consisting of 2400 households from both countries. This study is one of the first attempts in the literature globally to develop forecasting models based on ratios constructed with a combination of two different types of variables—one financial with one demographic variable. Findings confirm the high usability of the proposed innovative approach for forecasting the financial energy of households by taking into account a wide spectrum of diagnostic variables representing the financial strength of consumers. [ABSTRACT FROM AUTHOR]

Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon quantitative precipitation forecasts (QPFs) from numerical models is often controlled by track errors, with better quality from those with smaller track errors. However, the present work demonstrates that in daily QPFs over Taiwan made by a cloud-resolving model during five seasons of 2012–2016, targeted for 84 days during 27 typhoons and at ranges of day one (0–24 h) to day eight (168–192 h), the control of track errors on QPF quality is reduced for typhoons associated with southwesterly flow, compared to those without, and decent QPFs could still be obtained with large track errors in some cases. Subsequently, the circumstances and reasons for good (or bad) QPFs in selected examples are further investigated to deepen our understanding of typhoon QPFs in Taiwan. Some common ingredients are found in three cases where good QPFs were produced at a longer range (day 7 or 8) without a good track: these typhoons passed near northern Taiwan and the southwesterly flow prevailed over much of the island during the accumulation period. Responsible for much of the rainfall in Taiwan, the southwesterly flow was reasonably captured, resulting in good QPFs. In another example where the typhoon moved across southern Taiwan, on the contrary, the rainfall was produced by the storm's circulation, and the QPF was degraded without a good enough track prediction. [ABSTRACT FROM AUTHOR]

A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses. [ABSTRACT FROM AUTHOR]

The shear wave velocity profile and dynamic soil properties are known to be affected by aleatory uncertainty. This paper aims to investigate the effect of a statistical variation in the initial stiffness profile, stiffness degradation and damping curves on ground response predictions by conducting stochastic analysis. The Large Scale Seismic Test site in Lotung, Taiwan, is back-analysed with a fully-coupled finite element procedure using an advanced kinematic hardening soil model. Two ground motions recorded at the site, one strong and one weak, are applied at bedrock level. The results reveal that the site response prediction is sensitive to the seismic intensity of the input motion. When the level of induced shear strain is higher, i.e. in the case of the stronger motion, the spatial variability of the stiffness degradation and damping curves has a pronounced effect on the predicted site response. In contrast, when the weaker motion is considered the prediction is particularly sensitive to the statistical variation in the initial stiffness profile. This is mainly due to the stiffness degradation at very small strains shown by the laboratory data on LSST soils, which is captured in the paper by assuming an appropriate elastic domain in the constitutive model calibration. [ABSTRACT FROM AUTHOR]

The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day's weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods. [ABSTRACT FROM AUTHOR]

The progress of industrial development naturally leads to the demand for more electrical power. Unfortunately, due to the fear of the safety of nuclear power plants, many countries have relied on thermal power plants, which will cause more air pollutants during the process of coal burning. This phenomenon as well as increased vehicle emissions around us, have constituted the primary factors of serious air pollution. Inhaling too much particulate air pollution may lead to respiratory diseases and even death, especially PM $_{2.5}$ 2. 5 . By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM $_{2.5}$ 2. 5 prediction becomes more important. In this study, we propose a PM $_{2.5}$ 2. 5 prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Autoencoder and Linear interpolation are adopted for solving the missing value problem. Spearman’s correlation coefficient is used to identify the most relevant features for PM $_{2.5}$ 2. 5 . Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM $_{2.5}$ 2. 5 value for each Airbox device. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K-means has the best performance among all methods. Therefore, LSTM based on K-means is chosen to provide real-time PM $_{2.5}$ 2. 5 prediction through the Linebot. [ABSTRACT FROM AUTHOR]

Accurate rainfall forecasting is essential in planning and managing water resource systems efficiently. However, intermittent rainfall patterns increase the difficulty of accurately forecasting rainfall values. Deep learning techniques have recently been popular and powerful in forecasting. Thus, this study employed deep belief networks with a simple exponential smoothing procedure (DBNSES) to forecast hourly intermittent rainfall values in Taiwan. Weather factors were used as independent variables to forecast rainfall volume. The simple exponential smoothing data preprocessing procedure was used to deal with the intermittent data patterns. The other three forecasting models, namely the least squares support vector regression (LSSVR), the generalized regression neural network (GRNN), and the backpropagation neural network (BPNN), were employed to forecast rainfall using the same data sets. In addition, genetic algorithms were utilized to determine the parameters of four forecasting models. The empirical results indicate that the developed DBNSES models are superior to the other forecasting models in terms of forecasting accuracy. In addition, the DBNSES can obtain smaller values of RMSE than those in the previous studies. Therefore, the DBNSES model is a suitable and effective way of forecasting rainfall with intermittent data patterns. [ABSTRACT FROM AUTHOR]

Accurate real-time forecasts of inundation depth and extent during typhoon flooding are crucial to disaster emergency response. To manage disaster risk, the development of a flood inundation forecasting model has been recognized as essential. In this paper, a forecasting model by integrating a hydrodynamic model, k-means clustering algorithm and support vector machines (SVM) is proposed. The task of this study is divided into four parts. First, the SOBEK model is used in simulating inundation hydrodynamics. Second, the k-means clustering algorithm classifies flood inundation data and identifies the dominant clusters of flood gauging stations. Third, SVM yields water level forecasts with 1–3 h lead time. Finally, a spatial expansion module produces flood inundation maps, based on forecasted information from flood gauging stations and consideration of flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we present an application to the Yilan River basin, Taiwan. The forecasting results indicate that the simulated water level forecasts from the point forecasting module are in good agreement with the observed data, and the proposed model yields the accurate flood inundation maps for 1–3 h lead time. These results indicate that the proposed model accurately forecasts not only flood inundation depth but also inundation extent. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response. [ABSTRACT FROM AUTHOR]

Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentrations is a challenging prediction task but Internet of Things (IoT) can help in developing economical and agile ways to design such systems. In this paper, we use a historical data-based approach to perform PM2.5 forecasting. A forecasting method is developed which uses exponential smoothing with drift. Experiments and evaluation were performed using the real-time PM2.5 data obtained from large scale deployment of IoT devices in Taichung region in Taiwan. We used the data from 132 monitoring stations to evaluate our model's performance. A comparison of prediction accuracy and computation time between the proposed model and three widely used forecasting models was done. The results suggest that our method can perform PM2.5 forecast for 132 monitoring stations with error as low as 0.16 μ g/ m 3 and also with an acceptable computation time of 30 s. Further evaluation was done by forecasting PM2.5 for next 3 h. The results show that 90 % of the monitoring stations have error under 1.5 μ g/ m 3 which is significantly low. [ABSTRACT FROM AUTHOR]

Microphysical perturbation experiments were conducted to investigate the sensitivity of convective heavy rain simulation to cloud microphysical parameterization and its feasibility for ensemble forecasts. An ensemble of 20 perturbation members differing in either the microphysics package or process treatments within a single scheme was applied to simulate 10 summer-afternoon heavy-rain convection cases. The simulations revealed substantial disagreements in the location and amplitude of peak rainfall among the microphysics-package and single-scheme members, with an overall spread of 57%–161%, 66%–161%, and 65%–149% of the observed average rainfall, maximum rainfall, and maximum intensity, respectively. The single-scheme members revealed that the simulation of heavy convective precipitation is quite sensitive to factors including ice-particle fall speed parameterization, aerosol type, ice particle shape, and size distribution representation. The microphysical ensemble can derive reasonable probability of occurrence for a location-specific heavy-rain forecast. Spatial-forecast performance indices up to 0.6 were attained by applying an optimal fuzzy radius of about 8 km for the warning-area coverage. The forecasts tend to be more successful for more organized convection. Spectral mapping methods were further applied to provide ensemble forecasts for the 10 heavy rainfall cases. For most cases, realistic spatial patterns were derived with spatial correlation up to 0.8. The quantitative performance in average rainfall, maximum rainfall, and maximum intensity from the ensembles reached correlations of 0.83, 0.84, and 0.51, respectively, with the observed values. Significance Statement: Heavy rainfall from summer convections is stochastic in terms of intensity and location; therefore, an accurate deterministic forecast is often challenging. We designed perturbation experiments to explore weather forecasting models' sensitivity to cloud microphysical parameterizations and the feasibility of application to ensemble forecast. Promising results were obtained from simulations of 10 real cases. The cloud microphysical ensemble approach may provide reasonable forecasts of heavy rainfall probability and convincing rainfall spatial distribution, particularly for more organized convection. [ABSTRACT FROM AUTHOR]

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This study aimed to assess the effective spatial characteristics of input features by using physics-informed, machine learning (ML)-based inundation forecasting models. To achieve this aim, inundation depth data were simulated using a numerical hydrodynamic model to obtain training and testing data for these ML-based models. Effective spatial information was identified using a back-propagation neural network, an adaptive neuro-fuzzy inference system, support vector machine, and a hybrid model combining support vector machine and a multi-objective genetic algorithm. The conventional average rainfall determined using the Thiessen polygon method, raingauge observations, radar-based rainfall data, and typhoon characteristics were used as the inputs of the aforementioned ML models. These models were applied in inundation forecasting for Yilan County, Taiwan, and the hybrid model had the best forecasting performance. The results show that the hybrid model with crucial features and appropriate lag lengths gave the best performance. [ABSTRACT FROM AUTHOR]