Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches

• Main Authors: Latif S.D., Alyaa Binti Hazrin N., Hoon Koo C., Lin Ng J., Chaplot B., Feng Huang Y., El-Shafie A., Najah Ahmed A.
• Other Authors: 57216081524
• Format: Review
• Published: Elsevier B.V. 2024
• Subjects: Hybrid models; Machine learning; Prediction; Rainfall; Remote sensing; Climate models; Forecasting; Learning algorithms; Learning systems; Long short-term memory; Rain; Regression analysis; Satellite imagery; Hybrid model; Learn+; Machine-learning; Model learning; Prediction modelling; Predictive models; Rainfall prediction; Remote sensing approaches; Remote-sensing; Statistic modeling

Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. Deep learning uses neural networks to learn complex data patterns and relationships. A combination of satellite imagery, radar data, and ground-based observations are used and using aircraft or satellites, and remote sensing (RS) collects data on distant objects or locations. Satellites and radar are used to gather regional precipitation data for hybrid models. An algorithm trained on historical rainfall measurements would then process the data. Using remote monitoring instrument input features, the machine-learning model can predict precipitation. Evaluation of machine learning regression methods is based on the degree of agreement between predicted and observed values. The RMSE, R2, and MAE statistical measures check on the precision of a prediction or forecasting model. Machine learning excels at rainfall prediction regardless of climate or timescale. As one of the more popular models for predicting rainfall, the LSTM models demonstrate their superiority. Remote sensing and hybrid predictive models should be investigated further due to their scarcity. � 2023 THE AUTHORS