Rainfall Trend Analysis in Tangerang City Using Linear Regression and Random Forest

Rizaldi Wisnu Wardana

doi:10.63581/JoCPES.v5i1.07

Authors

Rizaldi Wisnu Wardana College of Meteorology Climatology and Geophysics Author

DOI:

https://doi.org/10.63581/JoCPES.v5i1.07

Keywords:

Rainfall, Rainfall, Linear Regression, Linear Regression, Random Forest, Random Forest, Machine Learning, Machine Learning

Abstract

Rainfall is an important element in the hydrological cycle that has a significant impact on the environment and human life, especially in tropical areas such as Tangerang City. This study aims to analyze annual and monthly rainfall trends and compare the performance of Linear Regression and Random Forest methods in predicting daily rainfall. Daily rainfall data from the Soekarno-Hatta Meteorological Station during the period 2019–2024 are used as model input. The results show that Random Forest has superior performance in capturing complex and extreme rainfall fluctuation patterns, with lower Mean Squared Error (MSE) and higher R-squared (R²) compared to Linear Regression. Linear Regression is only able to predict linear trends simply but is less accurate in handling non-linear variations. This study provides practical contributions to flood risk mitigation, water resource management, and urban infrastructure planning. The development of more accurate prediction models, such as Random Forest, is an important step in supporting climate change adaptation and environmental management in urban areas. Further research is recommended to include additional atmospheric variables and more complex validation techniques to improve prediction accuracy.

Author Biography

Rizaldi Wisnu Wardana, College of Meteorology Climatology and Geophysics

Student

Undergraduate Program in Applied of Instrumentation Meteorology, Climatology Geophysics

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