COMPARATIVE ANALYSIS OF MISSING DATA IMPUTATION METHODS FOR FLOOD FEATURES FROM LANGAT RIVER IN SELANGOR, MALAYSIA

Abstract

Flooding poses serious risks to lives, infrastructure, and ecosystems, underscoring the need for accurate forecasting. However, missing values in hydrological datasets—often caused by equipment failure or extreme weather—can compromise forecast reliability. This study evaluates five imputation techniques: Last Observation Carried Forward, Next Observation Carried Backward, Linear Interpolation, Spline Interpolation, and K-Nearest Neighbours, to identify the most effective method for reconstructing missing flood-related data. Using temperature, humidity, and water level records from the Langat River, Selangor, Malaysia, each method’s performance was assessed via Root Mean Square Error. Results show that Linear Interpolation generally yields the lowest error, while Next Observation Carried Backward performs best when missing data is minimal (1.20%).