Leveraging Radial Basis Function Neural Networks for Rainfall Prediction in Andhra Pradesh

The amount of water in dams and the availability of water for farming both depend on rainfall. As a result of global warming and other factors, it is difficult to make reliable predictions about when and how much rain will fall. Characteristics like as the quantity of rainfall in past years, climatic change, climatic circumstances, and so on may be utilized by Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) to predict the rainfall in a certain location. The unexpected precipitation might lead to floods and the destruction of man-made infrastructure. To find rains that will aid governments in preventing human fatalities, agricultural failures, and animal fatalities, comprehensive techniques for data classification and examination, review, inquiry, and interpretations are required. In order to effectively analyze and manage vital rainfall data, ML and DL solutions are constantly being developed. To do so, we used RBFNN, a kind of Artificial Neural Network (ANN) to forecast precipitation annually in Andhra Pradesh which is a state in India. We examined RBFNN with many other Neural Network (NN) models. When compared to other models, RBFNN was shown to be the most accurate option with 83% accuracy.