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RAINFALL PREDICTION MODEL USING MACHINE LEARNING ALGORITHM
RAINFALL PREDICTION MODEL USING MACHINE LEARNING ALGORITHM Muhammad Azizan1 , Prihastuti Harsani2 , Boldson H.Situmorang3 1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan University, Bogor, West Java, 16143, Indonesia Abstract Indonesia is a country where most regions experience two seasons: the dry season and the rainy season. The significant difference between these seasons lies in the average monthly rainfall. Rainfall is a key indicator in measuring weather conditions, as the amount of measured rainfall can indicate the intensity of rain in a specific area. Rainfall is measured in millimeters (mm) at monthly intervals. Rainfall has a major impact on human life, both in excess and in shortage. Excessive rainfall, such as heavy or extreme rain, often leads to flooding, especially in the city of Bogor, which is known as a flood and landslide-prone area due to its high rainfall intensity. Therefore, an effective rainfall prediction method is needed to help reduce the negative impacts of extreme weather. In this study, a rainfall prediction model was developed for the Bogor area using the Multilayer Perceptron algorithm, with 90% of the data for training and 10% for testing. The results show that the model can predict rainfall accurately, with an RMSE value of 17.8763, indicating a good level of accuracy. RMSE is used as the main indicator in evaluating model performance, where the lower the value, the more accurate the prediction. This MLP�based rainfall prediction model has great potential for application in various sectors, including disaster mitigation. With high accuracy, this model can support decision-making in areas vulnerable to floods and droughts, thus minimizing the negative impacts of weather variability. Keywords: Prediction; Rainfall; Multilayer Perceptron 1. Introduction Indonesia is a country where most of its regions experience two seasons in a year: the dry season and the rainy season. The main difference between these two seasons is the average monthly rainfall. Rainfall refers to the height of water collected in a rain gauge placed on a flat surface that does not absorb, drain, or evaporate [1]. Rainfall plays a significant role in human life on this planet. Both high and low rainfall levels greatly impact the climate conditions on Earth’s surface. Excessive rainfall can cause various problems, such as floods, crop failures, and other events. Heavy rain with high intensity, commonly referred to as extreme rain, can trigger floods. Bogor, one of the cities in Indonesia, often falls victim to flooding due to high rainfall, and this sometimes leads to landslides and building damage, causing significant losses to the local population. Therefore, a specific method is required to obtain optimal rainfall predictions [2]. Artificial Neural Networks, specifically the Multilayer Perceptron (ANN-MLP) model, can be used to predict weather changes more accurately, enabling the application of this forecasting system in daily life [3]. The variables used in the MLP method include rainfall, average temperature, average humidity, and sunshine duration. The advantage of using the MLP method lies in its ability to determine better weight values compared to other methods [4]. In this study, the selection of the Multilayer Perceptron method is expected to improve rainfall prediction accuracy by modeling complex relationships between variables in the context of weather data. The goal of this research is to predict rainfall in Bogor in 2023 using data downloaded from the BMKG website. The information generated from this research is expected to serve as a basis for the Meteorology, Climatology, and Geophysics Agency (BMKG) of Bogor in anticipating the future impacts of rainfall.
Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pakuan
Fakultas Matematika dan Ilmu Pengetahuan Alam
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