Multiple Imputation for Hydrological Missing Data by Using a Regression Method (Klang River Basin)

Abstract

Rainfall amounts and water surface elevation are considered as one of the most important climatic parameters. Because these two parameters will have a direct impact on water resources management decisions such as meet the water needs and prevent flooding. But in some cases, for some reason all time series data are not fully recorded. To fill the gaps in the data, several interpolation methods currently used. One of these methods is regression analysis as a statistical method. By using regression, we can determine the mathematical relationship coefficients between inputs and outputs. By achieving the equation, we can obtain the unknown quantities. In this research, the daily data between 2005 to 2015 for 5 Rain-gauge stations and 3 elevation measurement of water surface stations in the Klang River Basin were used. The main goal was to find the missing value of the water level in the mentioned three stations by rainfall and water level data. To evaluate the obtained results, Multiple R, R 2 , and Standard Error were used. The results indicate that the standard error in normalized data was less than the regular data.

Holy Places, Crosses Water, Light and Plant

“…In many papers, the important role of water is expressed in great detail. These include topics such as Reservoir operation [5][6][7][8][9][10][11], Flood [12][13][14][15][16], Drought [17], Meteorology and Climate Change [18-21], Forecasting and Predicting [22][23][24][25][26], Water quality [27,28], systematic and integrated approach in water resources [29] and Environmental Impact [30].…”

Methodological approaches for imputing missing data into monthly flows series

“…Because missing data imputation is a useful tool in water-resource management studies (Barnetche and Kobiyama, 2006), several authors have worked on the application of techniques for imputing missing data in hydrological studies resulting in a variety of methods ranging from simple imputation by mean or median to widely used statistical methods such as Regional Weighting (Ely et al, 2021); interpolations (linear, quadratic and cubic) (Gyau-Boakye andSchultz, 1994, Hamzah et al, 2020); methods based on linear regressions (single and multiple) (Kamwaga et al, 2018;Khalifeloo et al, 2015); Self Organizing Map (SOM) and Soil and Water Assessment Tool (SWAT) (Kim et al, 2015); to more advanced and robust methods, such as different Artificial Neural Network approaches (Canchala-Nastar et al, 2019;Elshorbagy et al, 2000;Nkiaka et al, 2016;Starrett et al, 2010;Vega-Garcia et al, 2019); machine learning methods (Heras and Matovelle, 2021;Rado et al, 2019); satellite radar altimetry and multiple imputation (Ekeu-Wei et al, 2018); combination of regression and autoregressive integrated moving average (ARIMA) models called dynamic regression (Tencaliec et al, 2015); Singular Spectrum Analysis (SSA) and Multichannel Singular Spectrum Analysis (MSSA) (Semiromi and Koch, 2019); among many others. The many methods that can be used for hydrological missing data imputation resulted in literature reviews as can be seen in Ben Aissia et al (2017) and Hamzah et al (2020).…”

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

“…In the last few decades, MI approach was frequently applied by several researchers (Khalifeloo et al (2015), De Carvalho et al (2017, Miró et al (2017), Sattari et al (2017), Jakhar et al (2018), andMilo et al (2019)) in the imputation of missing rainfall data. Due to its efficiency, there are various MI approach packages that have been introduced for missing data problems.…”