NORMAL RATIO IN MULTIPLE IMPUTATION BASED ON BOOTSTRAPPED SAMPLE FOR RAINFALL DATA WITH MIS SINGNESS

2017 ◽  
Vol 13 (36) ◽  
Author(s):  
Siti Nur Zahrah Amin Burhanuddin
Keyword(s):  
Multiple Imputation ◽  
Rainfall Data ◽  
Normal Ratio

scholarly journals Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults

2017 ◽  
Vol 32 (4) ◽  
pp. 575-583 ◽  
Author(s):  
José Ruy Porto de Carvalho ◽  
José Eduardo Boffinho Almeida Monteiro ◽  
Alan Massaru Nakai ◽  
Eduardo Delgado Assad
Keyword(s):  
Multiple Imputation ◽  
Daily Precipitation ◽  
Daily Rainfall ◽  
Auxiliary Variable ◽  
Rainfall Data ◽  
Imputation Model ◽  
Climatic Zones ◽  
Daily Rainfall Data ◽  
Versatile Technique ◽  
Precipitation Records

Abstract Modeling by multiple enchained imputation is an area of growing importance. However, its models and methods are frequently developed for specific applications. In this study the model for multiple imputation was used to estimate daily rainfall data. Daily precipitation records from several meteorological stations were used, obtained from system AGRITEMPO for two homogenous climatic zones. The precipitation values obtained for two dates (Jan. 20th 2005 and May 2nd 2005) using the multiple imputation model were compared with geo-statistics techniques ordinary Kriging and Co-kriging with the altitude as an auxiliary variable. The multiple imputation model was 16% better for the first zone and over 23% for the second one, compared to the rainfall estimation obtained by geo-statistical techniques. The model proved to be a versatile technique, presenting coherent results with the conditions of different zones and times.

Revised Normal Ratio Methods for Imputation of Missing Rainfall Data

2016 ◽  
Vol 13 (1) ◽  
pp. 83
Author(s):  
Siti Nur Zahrah Amin Burhanuddin ◽  
Sayang Mohd Deni ◽  
Norazan Mohamed Ramli
Keyword(s):  
Missing Data ◽  
Daily Rainfall ◽  
Peninsular Malaysia ◽  
Rainfall Data ◽  
Ratio Method ◽  
Accurate Estimation ◽  
Trimmed Mean ◽  
Coordinate Method ◽  
Target Station ◽  
Normal Ratio

A good quality of rainfall data is highly necessary in hydrological and meteorological analyses. Lack of quality in rainfall data will influence the process of analyses and subsequently, produce misleading results. Thus, this study is aimed to propose modified missing rainfall data treatment methods that produced more accurate estimation results. In this study, the old normal ratio method and the modified normal ratio based on trimmed mean are combined with geographical coordinate method. The performances of these modified methods were tested on various levels of the missing data of 36 years complete daily rainfall records from eighteen meteorology stations in Peninsular Malaysia. The results indicated that both modified methods improved the estimation of missing rainfall values at the target station based on the least error measurements. Modified normal ratio based on trimmed mean with geographical coordinate method is found to be the most appropriate method for station Batu Kurau and Sg. Bernam while modified old normal ratio with geographical coordinate is the most accurate in estimating the missing data at station Genting Klang.

scholarly journals Controlled Sampling Approach in Improving Multiple Imputation for Missing Seasonal Rainfall Data

2021 ◽  
Author(s):  
Siti Nur Zahrah Amin Burhanuddin ◽  
Sayang Mohd Deni ◽  
Norshahida Shaadan
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Missing Values ◽  
Rainfall Data ◽  
Original Structure ◽  
Seasonal Patterns ◽  
Block Bootstrap ◽  
Data Imputation ◽  
Bootstrap Approach ◽  
Sampling Approach

Abstract Missingness in rainfall data is one of the well-known and challenging issues faced by meteorologists and researchers from all over the world. The problem would affect the quality of the data which is very important in representing the actual meteorological characteristics of a particular location. Therefore, the missing data should be properly treated in order to provide good quality dataset for the public domain. In furtherance of ensuring the accuracy of imputed missing data, the original structure of the rainfall data series must be specifically preserved when the data are having seasonal patterns. Most of the environmental datasets are generally characterized by outliers and seasonal patterns. These characteristics have certainly affected the performance of missing data imputation methods. The problem of missing data can be treated, but a specific structured approach must be employed when involving dataset that contains outliers and seasonal patterns. This study has highlighted and discussed the structured and comprehensive procedures on how to tackle the problem of missing data by emphasizing on controlled sampling approach for their implementation. The missing values were estimated by using multiple imputation based on block bootstrap approach associated with normal ratio methods compared to the conventional sampling (i.e. general bootstrap approach). The analysis and experimentation are illustrated using several datasets obtained for several locations in Peninsular Malaysia. The block bootstrap approach has revealed its advantage of preserving time series structure in its process and successfully improved the estimates of missing rainfall data imputation.

Imputation of Missing Rainfall Data Using Revised Normal Ratio Method

2017 ◽  
Vol 23 (11) ◽  
pp. 10981-10985 ◽  
Author(s):  
Siti Nur Zahrah Amin Burhanuddin ◽  
Sayang Mohd Deni ◽  
Norazan Mohamed Ramli
Keyword(s):  
Rainfall Data ◽  
Ratio Method ◽  
Normal Ratio

scholarly journals Comparison of Normal Ratio Method and Distance Power Method for Estimating Missing Rainfall Data with Three Neighboring Stations

2021 ◽  
pp. 1-9
Author(s):  
Celeste A. De Asis
Keyword(s):  
Correlation Coefficient ◽  
Daily Rainfall ◽  
T Test ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Ratio Method ◽  
Power Method ◽  
Daily Rainfall Data ◽  
Normal Ratio ◽  
Better Than

This study compared the performances of Normal Ratio Method and Distance Power Method as a tool for estimating missing rainfall data. The data utilized are the rainfall data of the three neighboring station of Catarman, Northern Samar, Philippines. These stations are Catbalogan Station (Samar Province), Legazpi (Bicol Province) and Masbate (Masbate Province). The observed daily rainfall data for the Catarman (Northern Samar), Catbalogan, Legazpi, and Masbate were obtained from the Philippine Atmospheric Geographical Astronomical Services Administration. The monthly rainfall were computed for the three (3) neighboring stations (Catbalogan, Legazpi, Masbate). The evaluation used the T-test for correlated samples and the Pearson’s Correlation Coefficient for the monthly rainfall data computed of the three neighboring Station of Catarman, Northern Samar with the three neighboring stations. Based from the results, Normal Ratio Method performs better than Distance Power Method as applied to three neighboring stations.

Revised Normal Ratio Methods for Imputation of Missing Rainfall Data

2016 ◽  
Vol 13 (1) ◽  
pp. 83 ◽  
Author(s):  
Siti Nur Zahrah Amin Burhanuddin ◽  
Sayang Mohd Deni ◽  
Norazan Mohamed Ramli
Keyword(s):  
Missing Data ◽  
Daily Rainfall ◽  
Peninsular Malaysia ◽  
Rainfall Data ◽  
Ratio Method ◽  
Accurate Estimation ◽  
Trimmed Mean ◽  
Coordinate Method ◽  
Target Station ◽  
Normal Ratio

A good quality of rainfall data is highly necessary in hydrological and meteorological analyses. Lack of quality in rainfall data will influence the process of analyses and subsequently, produce misleading results. Thus, this study is aimed to propose modified missing rainfall data treatment methods that produced more accurate estimation results. In this study, the old normal ratio method and the modified normal ratio based on trimmed mean are combined with geographical coordinate method. The performances of these modified methods were tested on various levels of the missing data of 36 years complete daily rainfall records from eighteen meteorology stations in Peninsular Malaysia. The results indicated that both modified methods improved the estimation of missing rainfall values at the target station based on the least error measurements. Modified normal ratio based on trimmed mean with geographical coordinate method is found to be the most appropriate method for station Batu Kurau and Sg. Bernam while modified old normal ratio with geographical coordinate is the most accurate in estimating the missing data at station Genting Klang.

scholarly journals Simulation Study of Adjusted Spatial Weighting Method to Estimate Missing Rainfall Data

2020 ◽  
pp. 1-7
Author(s):  
Muhammad Az-zuhri Azman ◽  
Roslinazairimah Zakaria ◽  
Siti Zanariah Satari
Keyword(s):  
Simulation Study ◽  
Absolute Error ◽  
Simulation Method ◽  
Rainfall Data ◽  
Environmental Study ◽  
Weighting Method ◽  
Missing Value ◽  
Target Station ◽  
Normal Ratio ◽  
Weighting Methods

Missing value especially in environmental study is a common problem including in rainfall modelling. Incomplete data will affect the accuracy and efficiency in any modelling process. In this study, simulation method is used to demonstrate the efficiency of the old normal ratio inverse distance correlation weighting method (ONRIDCWM) in solving missing rainfall data. The simulation study is used to identify the best parameters for correlation power of p, percentage of missing value and sample size, n of the ONRIDCWM through simulating for 10,000 times by varying the value of the parameters systematically. The results of the simulation are compared with other available weighting methods. The estimated complete rainfall data of the target station are compared and assessed with the observed data from the neighbouring station using mean, estimated bias (EB) and estimated root mean square error (ERMSE). The results show that ONRIDCWM is better than the other weighting methods for the correlation power of p at least four. For illustration of the weighting method, monthly rainfall data from Pahang is used to demonstrate the efficiency of the method using three error indices: S-Index, mean absolute error (MAE) and correlation, R.

The Effect of Several Tissue Thromboplastins on the Activation of the Abnormal Factor X (Factor X Friuli)

1972 ◽  
Vol 27 (03) ◽  
pp. 535-542 ◽  
Author(s):  
A Girolami ◽  
M Lazzarin ◽  
G Molaro
Keyword(s):  
Prothrombin Time ◽  
Rabbit Brain ◽  
Factor X ◽  
Dilution Curve ◽  
Human Origin ◽  
Rabbit Lung ◽  
Normal Ratio

SummaryThe effect of several tissue thromboplastins on the abnormal factor X (factor X Friuli) has been investigated.The prothrombin time varied between 33.6 and 69 sec. The prothrombin time percentile values (saline dilution curve and Quick’s formula for citrated plasma) varied between 6.6 and 22% and between 10.9 and 32.8%, respectively. The prothrombin time patient/normal ratio varied between 2.24 and 4.43.The factor X level varied between 3.5 and 20% of normal.Significant correlations were found to exist between the percentile factor X level and the prothrombin time in seconds, the percentile prothrombin time values and the prothrombin time patient/normal ratio. Thromboplastins of human origin yielded the lowest factor X values namely 5% thereby appearing to be practically “inert” with regard to the abnormal factor X. Thromboplastins obtained from rabbit lung on the contrary yielded the highest values, namely 15.3%. Thromboplastins obtained from simian or rabbit brain gave values intermediate between these two extremes.

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