ANN in forecasting Missing Rainfall Data

Abstract

ANN has been used to estimate rainfall data by analysing patterns and mapping the correlation between historical data and weather patterns. ANNs are a type of machine learning algorithm that is modelled on the structure of the human brain, which makes them particularly effective for solving complex problems that involve large amounts of data. Overall, the use of ANNs for estimating rainfall data is a promising area of research that has the potential to provide valuable insights into weather patterns and their impacts on the environment and human society. The potential of ANN in the estimation of missing precipitation values has been investigated in detail. This study proposes to compare the performance of conventional methods as well as different algorithms of the Artificial Neural Network method to predict missing rainfall in the Upper Tapi catchment area in the West region of India. It was found that the ANN method has an edge over the conventional methods and proved to be a better method of finding the missing rainfall data values.