scholarly journals Neyman Scott Rectangular Pulse Modeling for Storm Rainfall Analysis in Peninsular Malaysia

2015 ◽  
Vol 11 (8) ◽  
pp. 841-846
Author(s):  
Rado Yendra ◽  
Ari Pani Desvina ◽  
Rahmadeni Rahmadeni ◽  
Wan Zawiah Wan Zin ◽  
Abdul Aziz Jemain ◽  
...  
Keyword(s):  
Rectangular Pulse ◽  
Peninsular Malaysia ◽  
Rainfall Analysis ◽  
Storm Rainfall

On the potential of Bartlett Lewis rectangular pulse models for simulating rainfall in Peninsular Malaysia

2013 ◽  
Vol 58 (8) ◽  
pp. 1690-1703
Author(s):  
Ibrahim Suliman Hanaish ◽  
Kamarulzaman Ibrahim ◽  
Abdul Aziz Jemain
Keyword(s):  
Rectangular Pulse ◽  
Peninsular Malaysia

scholarly journals A New Technique for the Analysis of Extreme Rainfall with Application to Lagos Metropolis, Nigeria

1983 ◽  
Vol 14 (3) ◽  
pp. 127-138 ◽  
Author(s):  
E. S. Oyegoke ◽  
J. O. Sonuga
Keyword(s):  
Parameter Estimation ◽  
Maximum Entropy ◽  
Extreme Events ◽  
Gumbel Distribution ◽  
Extreme Rainfall ◽  
Statistical Simulation ◽  
Entropy Method ◽  
Likelihood Method ◽  
Rainfall Analysis ◽  
Storm Rainfall

This paper focuses on the use of the principle of maximum entropy as an alternative technique for the parameter estimation of the Extreme Value Type – 1 (EV1) distribution or Gumbel distribution often used for the analysis and forecast of extreme events. A case study is made of storm rainfall analysis for Lagos metropolis using the available rainfall data for Ikeja, Oshodi and Lagos Mainland as obtained from Akanbi (1982). For comparison purposes, the parameters of the EV1 distribution is also obtained using the Maximum Likelihood Method. The later being one of the most reliable techniques and perhaps the most widely used for parameter estimation of the EV1 distribution. This exercise has made it possible to demonstrate in some ways the superiority of the maximum entropy method over existing methods used for statistical simulation of extreme events.

A Regionalized Stochastic Rainfall Model for the Generation of High Resolution Data in Peninsular Malaysia

2016 ◽  
Vol 10 (5) ◽  
pp. 77 ◽  
Author(s):  
Zalina Mohd Daud ◽  
Siti Musliha Mat Rasid ◽  
Norzaida Abas
Keyword(s):  
High Resolution ◽  
Synthetic Data ◽  
Rectangular Pulse ◽  
Peninsular Malaysia ◽  
Rainfall Series ◽  
Statistical Characteristics ◽  
Model Parameters ◽  
High Resolution Data ◽  
Ungauged Sites ◽  
Hourly Rainfall

<p>High resolution rainfall data is an important input for studies on hydrological systems. Often time synthetic data has to be generated in the absence of historical data.  The stochastic Neyman Scott Rectangular Pulse (NSRP) model has been developed to produce synthetic data of high resolution. To take into account the spatial and temporal nature of rainfall, an extra domain had been added producing the Spatial Temporal NSRP (ST-NSRP) model. However these models require the repeat estimation of all the model parameters for each location. This work develops the Regionalized ST-NSRP model which produces a single model for a region. The regionalization approach is carried out by the station-year method using ten years (2001-2010) of records from sixteen stations. The statistical characteristics used in estimating the model’s parameters are hourly and daily coefficient of variation, hourly and daily lag one auto correlation, hourly cross correlation between sites and hourly skewness of rainfall series. Simulation process is conducted at two independent stations with distinctly different rainfall profile. The Regionalized ST-NSRP model produced statistical characteristics of the ungauged sites which matched those of the observed series fairly well even though there is a tendency to underestimate hourly covariance and lag one autocorrelation. Considering that spatial variability of rainfall is high within the studied region, this model is sufficiently robust in producing reasonable synthetic hourly rainfall series which captured the characteristics and pattern of observed rainfall series. Therefore the model has shown a promising potential in generating high resolution synthetic data for any ungauged site within the region.</p>

Floristic Variation of Tree Communities and Their Association with Soil Properties in Pulau Jerejak, Penang, Peninsular Malaysia

2020 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Faezah Pardi
Keyword(s):  
Tree Species ◽  
Diversity Index ◽  
Basal Area ◽  
Peninsular Malaysia ◽  
Importance Value ◽  
Conservation Action ◽  
Endangered Tree ◽  
Endangered Tree Species ◽  
Tree Communities ◽  
Floristic Variation

This study was conducted at Pulau Jerejak, Penang to determine the floristic variation of its tree communities. A 0.5-hectare study plot was established and divided into 11 subplots. A total of 587 trees with diameter at breast height (DBH) of 5 cm and above were measured, identified and recorded. The tree communities comprised of 84 species, 63 genera and 32 families. The Myrtaceae was the most speciose family with 10 recorded species while Syzgium glaucum (Myrtaceae) was the most frequent species. The Myrtaceae recorded the highest density of 306 individuals while Syzgium glaucum (Myrtaceae) had the highest species density of 182 individuals. Total tree basal area (BA) was 21.47 m2/ha and family with the highest BA was Myrtaceae with 5.81 m2/ha while at species level, Syzgium glaucum (Myrtaceae) was the species with the highest total BA in the plot with value of 4.95 m2/ha. The Shannon˗Weiner Diversity Index of tree communities showed a value of 3.60 (H'max = 4.43) and Evenness Index of 0.81 which indicates high uniformity of tree species. The Margalef Richness Index (R') revealed that the tree species richness was 13.02. Myrtaceae had the highest Importance Value of 20.4%. The Canonical Correspondence Analysis (CCA) showed that Diospyros buxifolia (Ebenaceae) and Pouteria malaccensis (Sapotaceae) were strongly correlated to low pH. Dysoxylum cauliflorum (Meliaceae) and Eriobotrya bengalensis (Rosaceae) were correlated to phosphorus (P) and calcium ion (Ca2+), respectively. Therefore, the trees species composition at Pulau Jerejak showed that the biodiversity is high and conservation action should be implemented to protect endangered tree species. Keywords: Floristic variation; Tree communities; Trees composition; Pulau Jerejak; Species diversity

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Sign in / Sign up

Export Citation Format

Share Document