An Efficient Hybrid Machine Learning Classifier for Rainfall Prediction

Abstract

The most leading applications of Artificial Intelligence that seems to witness an immense Progression in the digital era are the Machine Learning (ML) Techniques. It learns itself from the past experiences and attempts at the best prediction of future instances or trends. Such progressive learning does not demand any explicit programming structures. Machine learning finds a wide range of application areas, and out of which accurate real time weather prediction gains importance. An interactive neural network based classification model for better prediction of rainfall has been put forth in this paper; we have proposed an interactive model for predicting rainfall using neural classification. The model is premeditated in a way, that it fetches feature extraction from a database including information about previous rainfalls in a specific area. The features were then pre-processed and further segmented by employing the random forest. The segmented outputs are then classified using neural networks. A comparison of spatial interpolation scheme is done with existing systems by deploying the hybrid classifier. The efficiency of the proposed model is evaluated and is compared with the traditional Deep Learning process and it is observed that the Random forest based interactive model provides better performance. Results of the model seem to be more accurate as the model uses an iterative approach for feature extraction.

Short-Term Rainfall Prediction Using Supervised Machine Learning

“…Researchers like Shah et al [3] have developed a simple polynomial regression-based model to predict the rain to benefit agricultural products. Asha et al [4] have proposed a hybrid machine learning classification model for predicting rainfall, and it has shown better performance than the ordinary ml-based model. Sakthivel and Thailambal [5] have also demonstrated such a hybrid approach for rain prediction, predicting continuous long period rainfall.…”