Abstract
Scattered data interpolation is an essential technique used in environmental, geospatial and meteorological analysis. It enables the estimation of unknown values within a dataset at the domain by utilising known data points. The utilisation of this method is crucial to generate smooth and continuous surfaces from discrete data, facilitating the creation of more precise models and visualisations of environmental events. The Malaysian rainfall and terrain data introduced in this paper, are essential datasets for these analyses. This paper provides detailed rainfall data from various meteorological stations across Malaysia, sourced from the Department of Irrigation and Drainage Malaysia. Additionally, terrain data for Kalumpang was obtained, which was based on the Agricultural Station in Selangor. These primary datasets are essential for understanding local climatic and topographic variations. However, challenges such as the size of the dataset, time constraints, weather conditions, and difficulties in obtaining data due to sensitivity and confidentiality issues limit the scope of the data collection. Although there are certain limits, the datasets have been carefully processed and prepared to be easily accessible in Microsoft Excel, MATLAB, and Python. This allows for additional research and applications in machine learning. This paper presents raw and pre-processed datasets, enabling comprehensive analyses and advancing scattered data interpolation techniques.