Relative Humidity Prediction using XGBoost Machine Learning Model, Case Study: Bajgah Climatological Station, Iran

Abstract

given the prevalence of available data for only these two parameters in many parts of the country, various scenarios involving these parameters were studied. The best scenario for predicting relative humidity was obtained using the XGBoost model. To assess the accuracy of the model, the Bajgah region in Fars Province was chosen as a case study, and the accuracy of different scenarios was compared using data from the past 30 years (1993 to 2023). In this regard, missing data were estimated using the KNN Imputer model. The correlation between mean relative humidity of one to ten days before and the target variable (predicted relative humidity on day t) was calculated using Pearson correlation. Based on the results indicating the insignificance of data from the fourth day and earlier, data from one to three days before were utilized.Findings and Conclusion: Finally, by comparing the results based on six statistical criteria (RMSE, MAE, MARE, MXARE, NSE, and R2), it was determined the scenario based on relative humidity and the maximum and minimum temperatures of the preceding 3 days provides the best estimation.

Keywords

• bajgah
• machine learning
• relative humidity
• xgboost