ABSTRACT
No human can survive without water. Water holds numerous benefits for human life, encompassing essential needs such as drinking and bathing, as well as playing a crucial role in electricity generation. Rain serves as one of the primary sources of water. While rain brings various advantages to human existence, it is not uncommon for it to also bring about disasters. This is further compounded by climate change, which can lead to alterations in rainfall patterns and increases in air temperatures. Changes in rainfall patterns pose a potential risk of undesirable consequences. In urban areas, these consequences may manifest as floods, disruptions in transportation, and the outbreak of diseases. Therefore, it is imperative to meticulously record rainfall data for a given region. This recording serves as a foundation for implementing preventative measures to mitigate potential unwanted outcomes. Unfortunately, in some instances, rainfall data goes unrecorded, resulting in data gaps. This paper focuses on predicting rainfall data through the application of cubic spline interpolation. Cubic spline interpolation is a method employed to determine an interpolating function that accurately passes through a defined set of data points. In this research, the cubic spline interpolation method successfully predicted rainfall data with a minimal relative error.
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