Jamaludin Suhaila

Copulas are a vital statistical tool, particularly in hydrology, for understanding complex relationships among flood characteristics. This study focuses on three key flood features: peak discharge, flood volume, and flood duration, using trivariate copulas to capture their interdependencies. This is crucial because bivariate and univariate analyses fall short in considering all three factors simultaneously. To handle extreme flood values, L-moment is proposed over maximum likelihood estimation and inference function margin due to its enhanced reliability and susceptibility to outliers and extreme values. Akaike information criterion was employed to identify the best-fit marginal distribution and copula. The Lognormal distribution effectively models peak discharge, while Weibull and generalized extreme value distributions fit flood volume and duration best, respectively. Various copula families, including elliptical and Archimedean, are assessed, where Clayton copula emerge as the most suitable. This analysis demonstrates that when more flood features are considered together, the return period increases, indicating the reduced likelihood of occurrence. The trivariate case of the AND-joint return period surpasses the trivariate case of the OR-joint return period where the TP, V, DAND=5, 405.93 years, while TP, V, DOR=500.46 years. This comprehensive approach enhances hydrological modeling and decision-making for water resource management and flood mitigation projects.

The four-dimensional flood variables namely peak flow, volume, duration, and peak time are modeled using a copula-based method in this study using an array of c The four-dimensional flood variables namely peak flow, volume, duration, and peak time are modeled using a copula-based method in this study using an array of c

Copulas have gained significant prominence as a powerful statistical tool with diverse applications, particularly in the field of hydrology, where they facilitate the measurement of complex relationships among various flood characteristics. Three primary flood features are considered which are peak discharge, flood volume, and flood duration, and their interdependencies are examined using copula functions. Trivariate copula is employed to capture the interrelation between these flood variables since bivariate and univariate flood frequency analyses have several shortcomings where they are unable to consider all three crucial flood factors simultaneously. In light of the presence of extreme values in flood variables, the L-Moment is proposed to estimate the parameters of the marginal distributions. This is due to its enhanced reliability and susceptibility to outliers and extreme values, unlike the commonly used parameter estimation in flood frequency analysis, Maximum Likelihood Estimation (MLE) and Inference Function Margin (IFM). Akaike Information Criterion (AIC) was employed to identify the best fit marginal distribution and copula. The Lognormal distribution performs well in modeling peak discharge, while the Weibull and Generalized Extreme Value (GEV) distributions provide the best fits for flood volume and duration characteristics, respectively. Several widely known copula including Elliptical and Archimedean copula families are analyzed. After assessing the dependence structure between flood variables, the Clayton copula emerged as the most suitable choice. It is expected that if more flood features are combined, the return period would be higher means the event is less likely to occur if all flood factors considered simultaneously, and it was proven that theAND-joint return period has higher return periods compared to theOR-joint return period. This comprehensive analysis facilitates improved hydrological modelling and flood risk assessment in Johor River Basin, Malaysia, by employing the L-Moment method for estimating flood distribution parameters.

This study aims to evaluate the suitability of the Tweedie generalised linear model for characterising monthly rainfall patterns across 18 meteorological stations in Peninsular Malaysia. It incorporates harmonic functions consisting of sine and cosine functions as seasonal predictors and El Niño Southern Oscillation (ENSO) indices as climatic predictors. Results indicate that three harmonic functions are essential to accurately portray rainfall dynamics in the southwestern and northwestern regions, while two suffice for the inland and western regions. However, incorporating four harmonic functions is the most optimal representation of the eastern region. An additional 1-month lag in ENSO indices is introduced to the optimal seasonal predictor model. Based on the findings, the southern oscillation index notably impacts monthly rainfall significantly in eastern and inland areas, while meteorological stations in the western and northwestern areas fit better with the multivariate ENSO index. Strikingly, no substantial impact of climate predictors is observed on the monthly rainfall within the southwestern region. Thus, the influence of climate indices is very much influenced by the geographical locations of the regions. Importantly, generating simulated data through the Tweedie model contributes to a more accurate representation of the statistical properties inherent in rainfall analysis.

Functional data analysis (FDA) has received significant attention from researchers due to its flexibility and diverse applications in various fields. FDA provides a comprehensive framework for analysing and extracting information from complex and high-dimensional datasets, enabling researchers to obtain insights into the underlying processes, improve modelling, and make accurate predictions. Therefore, understanding the FDA topic and its features and tools, as well as identifying the collaborative networks, are crucial for the development of its research areas. The objective of the present bibliometric study is to analyse the global research trend in FDA areas based on publication outputs, authorships, co-authorships, affiliated countries, and the co-occurrence of author keywords, which will enable researchers to assess the existing knowledge environment, future trends, potential research gaps, and collaboration opportunities. The publications from the year 1989 to 2021 were retrieved from the Scopus database, resulting in 1712 articles in journals after screening. Results have shown that articles published in the Journal of the American Statistical Association received the highest citations. Nearly 43% of the published articles were contributed by the leading authors from the USA, followed by China (11.5%) and Spain (9.4%). According to the QS World University Ranking 2021, eight of the top 20 productive institutions were ranked among the top 100 best universities. The findings indicated that researchers had intensively developed and applied FDA tools and features, such as smoothing, principal component analysis, regression, and clustering, in various domains. In addition, the expansion of FDA tools could be seen based on the recent progress in author keywords. New keywords, including function-on-function regression, function-on-scalar regression, scalar-on-function regression, outlier detection, structural health monitoring, and COVID-19, have arisen recently. Due to public concern about emerging diseases, future FDA work is expected to rise, particularly in the health sciences and biomedical fields.

The rise of COVID-19 disease has brought the world to a very worrisome stage. It started in Wuhan, China and numerous export cases had been verified in other provinces in China, as well as other countries. Considering the global threat, WHO has declared COVID-19 a public health emergency of international concern (PHEIC). The number of positive cases rose beyond 553 cases on 16^th March 2020 and the Prime Minister of Malaysia declared a lockdown. By the end of 2020, Malaysia had 113 010 cases and 471 deaths. The aim of the present study is to employ a logistic regression model in handling COVID-19 cases and to find the association between COVID-19 cases and climate factors. Minimum temperature (C), maximum temperature (C), temperature (C), wind speed (m/s) are all climate components. The number of the population also will be included in this study. This study will only be conducted in Peninsular Malaysia, and all climate data will be collected from July to December 2020. The logistic regression model is employed in this study since the predictor variable is binary, with redzone (over 40 active cases) as 1 and non-red zone (under 40 active cases) as 0. Based on the results, only the population variables is denoted the growth of COVID-19 cases since the variables was significance almost every week. However, none of the climate variable were found to be related to the COVID-19 incidence.

Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents, including malaria, cholera, dengue fever, hand, foot, and mouth disease (HFMD), and the recent Corona-virus pandemic. HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s. The outbreaks may result from a combination of rapid population growth, climate change, socioeconomic changes, and other lifestyle changes. However, the modeling of climate variability and HFMD remains unclear, particularly in statistical theory development. The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling. When dealing with time-series data with clustered variables such as HFMD with clustered states, the independence principle of both modeling approaches may be violated. Thus, a Generalized Additive Mixed Model (GAMM) is used to investigate the relationship between HFMD and climate factors in Malaysia. The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect. This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation. The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia. The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm. Besides, a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances. The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon. The study's outcomes can be used by public health officials and the general public to raise awareness, and thus, implement effective preventive measures.