Rainfall Prediction Using Machine Learning Based Ensemble Model
Veera Ankalu Vuyyuru, G. Apparao, S. Anuradha
Abstract
For dealing with complex systems, machine learning (ML) is an effective method. Based on machine learning techniques, this work provides a method for forecasting rainfall. Meteorological data relating to rainfall variables are used in the proposed ensemble model here. IMD data is being used in this project (Indian Meteorological department). (RF-SRF) Random Forest models and Support Vector Regression are used for validation and verification here. In order to accurately predict the likelihood of rain, the validation is carried out using data from the meteorological department in a specific area. In order to improve the system's reliability, a novel approach is devised to take advantage of the potential of SVR-RF in enhancing the quality of rainfall prediction. Rainfall behaviour is noticed for a particular area in order to make predictions by analysing the data. For the SVR-RF model, the proposed model helps to establish a relationship between rainfall and other common variables. Mean Absolute Error (MAE), RMS (Root Mean Square), and accuracy in classification based on months and days are the performance metrics used in the Python simulation. The proposed model has the potential to outperform the current prediction models in terms of performance. Based on data such as temperature, precipitation, and so on, the proposed model is able to accurately predict rainfall. Performance and error rates are improved by using this model.