Rainfall Forecasting: A Comparative Analysis of Deep Learning and Machine Learning Models with Application to Environmental Data
Keywords:
Deep learning, linear regression, Arima model, LSTM, Rainfall predictionAbstract
Accurate daily rainfall prediction is essential for enhancing agricultural productivity and ensuring the availability of food and water resources. This research explores the field of data mining and deep learning techniques, specifically focusing on the utilization of LSTM (long short-term memory) and ARIMA (auto-regressive integrated moving average) models utilizing environmental datasets from diverse regions. This study offers an exhaustive investigation of these two models to improve the precision of daily rainfall forecasting. The research outcomes underscore a comparative assessment of LSTM and ARIMA models in the field of precipitation prediction. LSTM demonstrates remarkable results with minimal RMSE during both the training and testing phases, achieving a high R2 score, which signifies its efficacy in capturing rainfall patterns. Conversely, the ARIMA model exhibits competitive performance, characterised by low MSE, MAE, and RMSE values, underscoring its dependability in predicting rainfall. The study draws attention to the unexplored Vidarbha region, which includes 11 districts, using Nagpur district as a representative instance. This study offers valuable insights into the realm of climate prediction, particularly concerning rainfall forecasting. These insights carry substantial implications for strategic decision-making in agriculture and water resource management, ultimately promoting food and water security and safeguarding the well-being of the populace.
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