Prediksi Hujan Bulanan di Bali Selatan Menggunakan Regresi Berganda Berdasarkan Indikator Suhu dan Kelembaban Udara - Forisman Daba, Irenius; Dewi, Krisna; Alghifari Sunaddin Nia, Muhammad Ridho; Akbar, Dzikrullah (Stasiun Pemantau Atmosfer Global Lore Lindu Bariri - Palu, 2025)
Southern Bali, characterized by intensive development and aviation activities, requires accurate rainfall information due to the influence of tropical atmospheric dynamics. This study employs a regression model based on air temperature and humidity using climatological data from 2000–2023 to analyze rainfall patterns. The results show that air temperature has the highest Pearson correlation with rainfall (r = 0.75), followed by air humidity (r = 0.67). However, RMSE evaluation indicates that humidity provides more accurate predictions (183.09 mm) than temperature (190.40 mm). These findings emphasize that correlation does not always reflect prediction quality, as it only represents linear relationships, whereas RMSE directly assesses model accuracy. Physically, humidity plays a direct role in cloud formation and rainfall, while temperature only regulates the atmosphere’s capacity to hold water vapor. Despite limitations during seasonal transition periods and extreme events, this model demonstrates potential to support data-driven rainfall prediction for risk mitigation, aviation meteorological services, and development planning in Southern Bali.