Application of Hybrid Machine Learning Approaches for Effective Rainfall Prediction

Proper rainfall estimation is very crucial in improving disaster preparedness and managing effects of other natural disasters like floods and droughts amongst others. In many conventional observational approaches, difficulties can be encountered in terms of getting necessary resolution and accuracy for measuring such characteristics of the precipitation as its intensity and duration at the time of storming. To overcome these drawbacks, this work suggests a new dual machine learning system that integrates XGBoost and BiLSTM for the improved rainfall prognostic model. XGBoost is used for its capability to handle structured, tabular data and prevent overfitting, BiLSTM is used since it can learn temporal relationships and non-linear patterns associated with meteorological time series. The hybrid model takes advantages of these complementary abilities to form accurate and reliable forecast. XGBoost mainly considers finding the importance of useful features from static data where as BiLSTM considers the weather data as a sequence and tries to find temporal pattern. The integration of these models improves both the reliability and efficiency of predictions as well as offering a detailed appreciation of rainfall patterns. In this research work, rainfall forecast accuracy has been enhanced which creates positive implications on agriculture management, flood preparedness.