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

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

A Bibliometric Survey on the Use of Long Short-Term Memory Networks for Multivariate Time series forecasting

In this paper, we aim to review and analyze the publications related to the utilization of Long Short-Term Memory (LSTM) networks for multivariate time series forecasting. The purpose of this bibliometric survey was to study how technology in the field of LSTM has evolved over the years. There were 242 research papers published, by over 50 researchers, over 6 years, on the topic of “Multivariate time series forecasting using LSTM”. The majority of these papers were published between the years 2018 and 2020. The Scopus database was utilized for analyzing recent trends in this area and to determine the model that would be best suited for weather forecasting applications. Through this study, we aim to shortlist various models that have shown consistent reliability and accuracy while utilizing multivariate time series data for prediction. These models can then be employed for other forecasting applications