Bibliometric review on the impact of artificial neural networks on reservoir sedimentation (2008 – august 2024)
Year: 2024
Type: article
Abstract: This article addresses a literature review on the application of artificial neural networks (ANN) in reservoir sedimentation focusing on their main concepts towards underscoring the importance of data integration and technological application in water resource management. Despite its significance, the topic is still relatively scarce in academic literature. Bibliometric analysis was a fundamental step in this research, since data from Scopus and Web of Science (WoS) databases were examined, focusing on the period from 2008 to August 2024 and employing specific keywords for the analysis. The choice between Scopus and WoS depends on the researcher's needs. The former would be more suitable for a broad literature coverage, whereas WoS would be ideal for impact analyses and citations in high-quality journals. Scopus is better for broad literature coverage, while WoS excels in impact analysis and high-quality citations, making them complementary resources. The review highlighted practical applications of bibliometric concepts, facilitating the visualization of collaborations among countries, journals, and authors. Zotero software was employed for its free, open-source nature, which simplifies access and integrates well with Microsoft Word and Google Docs for managing citations and bibliographies. Countries such as Iran, China, and India dominated almost 50% of the publications analyzed, reflecting the geological, topographical, and climatic diversity of those regions. Investigations on reservoir sedimentation with the use of ANNs have been gaining relevance in the scientific scenario, becoming crucial in the national context, considering the scarcity of in-depth studies on the topic, and representing a significant challenge for the management and conservation of our water resources.
Source: Caderno Pedagógico
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404883173