Libraries at University of Nebraska-Lincoln

 

Date of this Version

3-6-2021

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.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.