Analysis and Prediction of Relative Humidity Level using Generalized Linear Model

Abstract

The significance of humidity as a critical climate parameter impacts various sectors, including agriculture, health, and energy, necessitating a comprehensive understanding of its influencing factors. This study investigates the influence of climatic variables such as temperature, rainfall, sunshine duration, wind speed, and wind direction on the humidity levels in DKI Jakarta from 2019 to 2022. The objective is to develop a time-independent predictive model for humidity based on historical climate data. The methodology includes data pre-processing to impute missing values and replace outliers, followed by exploratory data analysis to ascertain variable distribution and inter-relationships. A regression model was initially employed for analysis, with subsequent application of regularization via a generalized linear model to enhance prediction accuracy. Results indicate that temperature, rainfall, sunshine duration, and wind direction significantly impact humidity levels in the investigated period. High inter-variable correlation posed challenges of multicollinearity and overfitting in the initial model. However, the application of regularization, trained with 75% of the historical dataset, mitigated these issues and improved model accuracy. This is evident in the improved Mean Squared Error (MSE) performance metrics of the Elastic-Net Regression Model (12.2), compared to the initial Multiple Regression Model (12.5). These findings hold potential implications for weather forecasting and climate change studies