Rainfall prediction model in Semarang City using machine learning

Abstract

The erratic distribution of rainfall greatly affects people's daily activities, especially in Semarang City, so it is necessary to predict rainfall. Correct prediction of rainfall can improve community preparedness in dealing with natural disasters. Algorithms for machine learning and data mining have been extensively utilized in research involving rainfall data from various regions. The primary objectives of this study are to find the best regression algorithm and use machine learning algorithms to predict rainfall in Semarang. The dataset used is daily rainfall data for the City of Semarang from the meteorological, climatological, and geophysical agency (BMKG). Machine learning algorithms such as multiple linear regression, random forest regression, and replicated neural networks will be used to conduct regression analysis on this dataset. The mean absolute error and Root mean squared error techniques are utilized to evaluate the performance of machine learning algorithms. With an error rate of 13.055 for root mean squared error (RMSE) and 6.621 for mean absolute error (MAE), the results of the research indicate that the performance of the neural network algorithm is superior to that of other algorithms.