Leveraging Ensemble Time-series Forecasting Model to Predict the amount of Rainfall in Andhra Pradesh

• Published in: 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP) pp. 1 - 7
• Main Authors: Basha, Shaik Johny, Prasad, G. Lakshmi Vara, Vivek, Kolla, Kumar, Eedupalli Sai, Ammannamma, Tamminina
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.02.2022
• Subjects: Annual rainfall; Atmospheric modeling; Intensity; Moisture; Monsoons; Predictive models; Rain; Statistical analysis; Temperature; Time series forecasting models
• ISSN: 2640-5768
• DOI: 10.1109/AISP53593.2022.9760553

Rainfall in India is becoming more unpredictable, leaving it harder to forecast. When it comes to predicting Indian summer monsoon rainfall, the Indian Meteorological Department (IMD) now uses Ensemble methods and Statistical methods. As a result of this, strategists are unable to foresee the socioeconomic consequences of floods (too many rains) or droughts (fewer rains). Precisely how much rain falls depends on several variables such as a measure of the warmth or coldness in the air, moisture, breeze, movement, and direction of the wind. This article will use Ensemble time-series forecasting model ARIMA (Autoregressive Integrated Moving Average)+ GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) to forecast the intensity of rainfall by considering various meteorological factors like sea-level pressure, moisture, dew point, min-max temperature, snowfall, geopotential height, speed and direction of the wind, humidity, and atmospheric pressure. The suggested Ensemble ARIMA+GARCH model has given good results when compared with individual models and state-of-the-art ensemble approaches in terms of RMSE, MAE, and MSE.