Bibliometric Review of Monsoon Rainfall Prediction Models: With Special Reference to Use of Artificial Intelligence in Rainfall Prediction

Authors

Shilpa Manoj Hudnurkar, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Follow
Neela Rayavarapu Dr., PhD Supervisor, Symbiosis Institute of Technology, Symbiosis International (Deemed University)

Date of this Version

3-6-2021

Document Type

Article

Abstract

Rainfall is a result of several complex atmospheric processes making it challenging to predict. For countries whose economy is dominated by agricultural sector, accurate rainfall prediction is highly essential. A huge network of weather stations is spread across the globe for the observation of meteorological parameters. These generate vast amounts of data which can be used to accurately predict the weather. This necessitates the use better tools such as various artificially intelligent algorithms. This study aims to explore global research trends in monsoon rainfall prediction techniques using Artificial Intelligence (AI) and Artificial Neural Networks (ANN). Scopus database has been used for carrying out bibliometric analysis for the period 1979 to 2021. The Scopus database has been analyzed for a number of publications, sources, languages, countries, affiliations etc. The analysis revealed that monsoon rainfall is sensitive to various factors such as sea surface temperature, El Nino, Southern Oscillation and many more. Statistical and dynamic models were used for monsoon rainfall forecasting and AI tools have been used for monsoon rainfall forecasting since 2000. Publications are mainly in the form of research articles and 99.7% of the literature is in the English language. Of the total publications, contributions from India are 55% while the United States and China contributed 18.67% and 14.3%, respectively.