Prediction on Flood Risk in Batu Pahat using Spatial Analysis Approach

Abstract

Floods are among the most frequent and destructive natural disasters in Malaysia, particularly affecting regions such as Batu Pahat, Johor, due to its low-lying terrain, rapid urbanization, and climate variability. The conventional flood prediction methods often fail to capture the spatial variability of flood risks, limiting their accuracy for localized assessments. This research aims to determine flood prediction in Batu Pahat hydrology station by using geospatial tools, implement the clustering analysis in categorizing the flood risk levels based on the rainfall intensity across multiple hydrology stations, and to predict the rainfall intensity in 2025 by using simple exponential smoothing (SES). This research employs geospatial tools like Local Moran’s I and k-means clustering to analyse normalized rainfall data from 11 hydrology stations. Clustering methods were used to identify spatial patterns and categorize flood risks. SES was applied for predictive modelling of rainfall intensity. The findings reveal significant spatial clustering of rainfall intensity in urban areas, highlighting them as high-risk zones due to impermeable surfaces and poor drainage systems. Stations were categorized into low, medium, and high-risk zones, with SES predictions indicating the smoothing constant (α=0.0915) for Station 1 and the smoothing constant (α=0.2183) for Station 9 produce more efficient and better forecast in 2025 where the predicting rainfall intensities of 75.4 mm and 78.6 mm, respectively. The insights provide a foundation for improved urban planning, resource allocation, and disaster preparedness.