Student copula method in rainfall distribution

Copulas are tools for modelling dependence of several random variables. The term copula was first used in the work of Sklar (1959) and is derived from the latin word copulare, to connect or to join. The main purpose of copulas is to describe the interrelation of several random variables. (Thorsten Schmidt, 2006). Copula is a function that joins the two distributions and known as dependence functions. Copula connect multivariate distribution function to its univariate marginal distribution. When we have two models having the problems relating to dependence, we can join that models becoming one model using marginal function. So, the dependency is taken care. It means that copula played an important role to join multivariate distributions to their one dimensional marginal distribution function

Spatial pattern of rainfall events:a background study to modeling and forecasting rainfall

The study of extreme rainfall events and their spatial coverage is important in identifying areas with high and low extreme events. It has been widely known that extreme rainfall is responsible for major flash flood and landslide events that have caused significant loss of life and economic losses. Unfortunately, the dynamics of extreme rainfall events still received less concern. This study scrutinized the characteristics of extreme rainfall and their spatial coverage in Peninsular Malaysia using rain gauge data. Eight indices of climate extremes based on daily precipitation data defined and adopted by the Joint Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated. The selected indices captured the precipitation intensity, the frequency and length of heavy rainfall events. The geostatistical method of Ordinary Kriging (OK) is applied to the indices calculated. The results from OK method give a pictorial representation of the structure of extreme rainfall spatial variability which helps in deriving rainfall patterns, quantifying rainfall amounts or help in identifying areas with high risk of extreme rainfall event. This result could provide to researchers and decision makers a case study area that needs adequate attention

Multiple regression analysis using climate variables

Regression analysis is very useful when it comes to study the relationship between variables. Regression analysis can identify the cause and effect of one variable to another variable. Variables is the main part in regression analysis. There are dependent variable (or criterion variable) and independent variable (or predictor variable). In multiple regression, the independent variables can be added more in the model then explain the cause and effect of dependent variable in more variations. Hence, dependent variable can be predicted by building better models using multiple regression analysis. The objective of this study comprises of (i) to determine correlation between temperature, humidity, wind, solar radiation and evaporation; and (ii) to build relationships between predictand with predictors using multiple linear regression

parametric estimation methods for bivariate copula in rainfall application

This study focuses on the parametric methods: maximum likelihood (ML), inference function of margins (IFM), and adaptive maximization by parts (AMBP) in estimating copula dependence parameter. Their performance is compared through simulation and empirical studies. For the empirical study, 44 years of daily rainfall data of Station Kuala Krai and Station Ulu Sekor were used. The correlation of the two stations is statistically significant at 0.4137. The results from the simulation study show that when the sample size is small (n <1000) for correlation level less than 0.80, IFM has the best performance. While, when the sample size is large (n ≥ 1000) for any correlation level, AMBP has the best performance. The results from the empirical study also show that AMBP has the best performance when the sample size is large. Thus, in order to estimate a precise Copula dependence parameter, it can be concluded that for parametric approaches, IFM is preferred for small sample size and has correlation level less than 0.80 and AMBP is preferred for larger sample size and for any correlation level. The results obtained in this study highlight the importance of estimating the dependence structure of the hydrological data. By using the fitted copula, the Malaysian Meteorological Department will be able to generate hydrological events for a system performance analysis such as flood and drought control system

Review on indoor environmental quality parameters towards healthier green buildings in Malaysia / Fadhilah Che Aziz and Md. Yusof Hamid

This paper presents the review of indoor environmental quality parameters in various guidelines and green rating tools namely; Guidelines on Indoor Environmental Quality for Government Office, Guideline for Performance Evaluation and Office Building‟s Rating, Green Building Index (GBI), Green Real Estate (GreenRE), Green Mark and Building Research Establishment Environment Assessment Method(BREEAM). The review process was done by comparing theparameters existing in these guidelines and green rating tools. Further review has been done on the selected green rating tools across several green policies on indoor environmental quality. Standard references for the parameters isalso identified. Previous studies that have been conducted regarding indoor environmental quality level in several buildings are also been reviewed.With the insights drawn from the comparative review, suggestions on ways to improve indoor environmental quality parameters and assessments are made that may facilitate its implementation. The outcome of the study provides a deep understanding in indoor environment quality parameters and assessments. With the outcome of this study, it may helpsinimprovingassessment in indoor environmental qualitytowards healthier green buildings in Malaysia

Identification of rainfall temporal patterns

Temporal pattern for rainfall events is required in the design and evaluation of hydrologic safety for hydraulic structures. In this paper, a method of statistical cluster analysis is applied to identify event-based representative temporal rainfall pattern in fourteen stations in Johor. The 8 hour separation time of no rain is used to divide the 5 years rainfall data into individual rainfall event according to the monsoons. The analysis is implemented from the dimensionless mass curve ordinates as the attributes in statistical cluster analysis of the cumulative storm depth over the non-dimensionalized time. As a result, three representative rainstorm patterns are identified and classified under the two basic types of advanced-type (A) and central-peak type (C) for northeast and southwest monsoon. Meanwhile, only one basic type is identified to represent rainstorm pattern during the inter-monsoon that is advanced type (A). In addition, the rainfall pattern is dependent on rainfall depth and duration, season and geographical location by contingency table test. The rainfall information presently used for design in Malaysia is very dissimilar to the representative curves derived in this study. The identification of three representative rainfall temporal patterns according to the monsoon seasons in Johor can be used as a basis to stochastically generate the plausible rainfall hyetographs of the specified pattern in Johor

Modelling asthma cases using count analysis approach: poisson INGARCH and negative binomial INGARCH

Pollution in Johor Bahru is an issue that needs adequate attention because it has contributed to a number of asthma cases in the area. Therefore, the goal of this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approaches namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH) and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. The estimation of the parameter was done by quasi-maximum likelihood estimation. Model assessment was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT) histogram, log-likelihood value, Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Our result shows that NB-INGARCH with identity and log link function is adequate in representing the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality yet the lowest AIC and BIC. However, in terms of forecasting accuracy, NB-INGARCH with identity link function performed better with the smaller RMSE (8.54) for the sample data. Therefore, NB- INGARCH with identity link function can be applied as the prediction model for asthma disease in Johor Bahru. Ideally, this outcome can assist the Department of Health in executing counteractive action and early planning to curb asthma diseases in Johor Bahru

Development of generalized feed forward network for predicting annual flood (depth) of a tropical river

The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable

Comparison of stochastic mortality model for wider age range

The incorporation of non-linear pattern of early ages has opened new research directions on improving the existing stochastic mortality model structure. Several authors have outlined the importance of encompassing the full age range in dealing with longevity risk exposure by not to ignore the dependence between young and old age. In this study, we consider the two extensions of Cairns, Blake and Dowd model that incorporate the irregularity profile seen at the mortality of lower ages which are Plat and O’Hare and Li. The models’ performances in terms of in-sample fitting and out-sample forecasts were examined and compared. The results indicated that O’Hare and Li model performs better as compared to Plat model

Multi-Population mortality model: A practical approach

The growing number of multi-population mortality models in the recent years signifies the mortality improvement in developed countries. In this case, there exists a narrowing gap of sex-differential in life expectancy between populations; hence multi-population mortality models are designed to assimilate the correlation between populations. The present study considers two extensions of the single-population Lee-Carter model, namely the independent model and augmented common factor model. The independent model incorporates the information between male and female separately whereas the augmented common factor model incorporates the information between male and female simultaneously. The methods are demonstrated in two perspectives: First is by applying them to Malaysian mortality data and second is by comparing the significance of the methods to the annuity pricing. The performances of the two methods are then compared in which has been found that the augmented common factor model is more superior in terms of historical fit, forecast performance, and annuity pricing