Bibliometric Analysis of Sediment Concentration Prediction Using Artificial Neural Network
Keywords:sediment, prediction, bibliometric review, subject area, ANN, water quality
This bibliometric review is aimed at documenting, synthesizing research trends, and suggest future directions in the applications of artificial neural network (ANN) in the prediction of sediment concentration over the past 21 years (1998 to 2020). Through bibliometric analysis, we analyzed 158 documents extracted from the Scopus-indexed database. We analyzed meaningful information on the document type, research trends, publishing activity by authors and journals, subject area, most active countries, the language of documents, and keywords. Microsoft excel and related figures extracted from the Scopus website was used in computing the data aggregate. Documents, author, and journal source analysis was used to identify the key authors and most active sources that contributed to the knowledge pool in this research field. Our review found that the highest number of publications in this area were from Asia, Europe, and North America with Iran taking the lead. Also, we found that the prediction of sediment concentration using artificial neural networks requires more research data. This research literature has significant contributions from both economically developed and developing countries. This study is limited to data extracted from the Scopus database in October 2020.