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.

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 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.

scholarly journals A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Siti Mariana Che Mat Nor ◽  
Shazlyn Milleana Shaharudin ◽  
Shuhaida Ismail ◽  
Nurul Hila Zainuddin ◽  
Mou Leong Tan
Keyword(s):  
Missing Values ◽  
Nearest Neighbor ◽  
East Coast ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Peninsular Malaysia ◽  
Partial Least Square ◽  
Least Square ◽  
Rainfall Data ◽  
Efficiency Coefficient

Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.

ANALYSIS OF EXTREME RAINFALL INDICES IN PENINSULAR MALAYSIA

2016 ◽  
Vol 78 (9-4) ◽  
Author(s):  
Nur Shazwani Muhammad ◽  
Amieroul Iefwat Akashah ◽  
Jazuri Abdullah
Keyword(s):  
East Coast ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Peninsular Malaysia ◽  
Rainfall Data ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Daily Rainfall Data ◽  
Extreme Rain ◽  
Increasing Trends

Extreme rainfall events are the main cause of flooding. This study aimed to examine seven extreme rainfall indices, i.e. extreme rain sum (XRS), very wet day intensity (I95), extremely wet day intensity (I99), very wet day proportion (R95), extremely wet day proportion (R99), very wet days (N95) and extremely wet days (N99) using Mann-Kendall (MK) and the normalized statistic Z tests. The analyses are based on the daily rainfall data gathered from Bayan Lepas, Subang, Senai, Kuantan and Kota Bharu. The east coast states received more rainfall than any other parts in Peninsular Malaysia. Kota Bharu station recorded the highest XRS, i.e. 648 mm. The analyses also indicate that the stations in the eastern part of Peninsular Malaysia experienced higher XRS, I95, I99, R95 and R99 as compared to the stations located in the western and northern part of Peninsular Malaysia. Subang and Senai show the highest number of days for wet and very wet (N95) as compared to other stations. Other than that, all stations except for Kota Bharu show increasing trends for most of the extreme rainfall indices. Upward trends indicate that the extreme rainfall events were becoming more severe over the period of 1960 to 2014. 

scholarly journals A comparison of method for treating missing daily rainfall data in Peninsular Malaysia

2017 ◽  
Vol 13 (4-1) ◽  
pp. 375-380 ◽  
Author(s):  
Izzat Fakharuddin Kamaruzaman ◽  
Wan Zawiah Wan Zin ◽  
Noratiqah Mohd Ariff
Keyword(s):  
Traditional Method ◽  
Missing Values ◽  
Model Performance ◽  
Daily Rainfall ◽  
Peninsular Malaysia ◽  
Imputation Method ◽  
Rainfall Data ◽  
Data Series ◽  
Daily Rainfall Data ◽  
Weighting Methods

This study modified a method for treating missing values in daily rainfall data from 104 selected rainfall stations. The daily rainfall data were obtained from the Department of Irrigation and Drainage Malaysia (DID) for the periods of 1965 to 2015. The missing values throughout the 51 years period were estimated using the various types of weighting methods. In determining the best imputation method, three test for evaluating model performance has been used. The findings of this study indicate that the proposed method is more efficient than the traditional method. The homogeneity of the data series was checked using the homogeneity tests recommended by the existing literatures. The results indicated that more than 40% of the rainfall stations were homogenous based on the proposed method.

A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy)

2008 ◽  
Vol 160 (1-4) ◽  
pp. 1-22 ◽  
Author(s):  
Rossella Lo Presti ◽  
Emanuele Barca ◽  
Giuseppe Passarella
Keyword(s):  
Missing Data ◽  
River Basin ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Daily Rainfall Data

scholarly journals Estimation of rainfall and stream flow missing data for Terengganu, Malaysia by using interpolation technique methods

2017 ◽  
Vol 13 (3) ◽  
Author(s):  
Wan Norliyana Wan Ismail ◽  
Wan Zawiah Wan Zin @ Wan Ibrahim
Keyword(s):  
Missing Data ◽  
Stream Flow ◽  
Missing Values ◽  
Estimation Error ◽  
Daily Rainfall ◽  
Efficient Estimation ◽  
Estimation Methods ◽  
Flow Data ◽  
Target Station ◽  
Interpolation Techniques

Missing data is a serious problem in many climatological time series. Daily rainfall and stream flow datasets with no missing values are required for efficient estimation for application purposes. In order to estimate any missing observations in data, interpolation techniques are often used. This study focuses on comparing a few selected methods in the estimation of missing rainfall and stream flow data. The interpolation techniques studied were the Arithmetic Average (AA) method, Normal Ratio (NR) method, Inverse Distance (ID) method and Coefficient of Correlation (CC) method. However, in the case when there is no information from neighboring stations, the mean on the same day and month but at different years is taken as estimation of the missing value on that particular date. Twenty years of daily rainfall and stream flow data at 12 stations located at Terengganu were used for this study. In testing to verify which method is the best in evaluating missing values at the target station using information from the nearby stations (in the radius range of 10 km to 50 km), several percentages of missing values were considered. The validation of the best estimation methods is done based on the estimation error; with tests such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient (R) tests.

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 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.

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