This repository contains the code implementation for the paper titled "Bibliometric Analysis of Artificial Intelligence Techniques for Predicting Soil Liquefaction: Insights and MCDM Evaluation," published in the Natural Hazards journal in 2024. The paper explores the application of artificial intelligence (AI) techniques for predicting soil liquefaction, a critical geotechnical issue affecting infrastructure and human safety.

Soil liquefaction, a critical geotechnical issue affecting infrastructure and human safety, necessitates effective prediction and mitigation strategies. The application of artificial intelligence (AI) techniques has gained significant attention for addressing soil liquefaction complexities. This study conducts a bibliometric analysis of AI research in soil liquefaction prediction. Through a systematic search of the Web of Science database, 258 relevant articles published between 1994 and 2023 were identified. Bibliometric indicators were applied to analyze publication trends, authorship patterns, affiliated institutions, publication venues, and citation patterns. Additionally, the study introduces a novel approach for evaluating bibliometric analysis results. The MULTIMOORA method, a Multi-Criteria Decision Making (MCDM) technique, was employed to further analyze journals contributing to the academic knowledge inventory on AI techniques in soil liquefaction. This study showcases the utility of MCDM techniques in aggregating bibliometric analysis results and facilitating decision-making. It emphasizes the interdisciplinary nature of this field, which merges geotechnical engineering, computer science, and machine learning. The analysis indicates a consistent increase in publications on AI in liquefaction, with notable peaks in 2011 and 2019. Journals such as Soil Dynamics and Earthquake Engineering, Bulletin of Engineering Geology and the Environment, and Environmental Earth Sciences are identified as significant contributors to studies on soil liquefaction prediction using AI techniques.

@article{kokcam_bibliometric_2024,
	title = {Bibliometric analysis of artificial intelligence techniques for predicting soil liquefaction: insights and {MCDM} evaluation},
	doi = {10.1007/s11069-024-06630-0},
	journal = {Natural Hazards},
	author = {Kökçam, Abdullah Hulusi and Erden, Caner and Demir, Alparslan Serhat and Kurnaz, Talas Fikret},
	year = {2024},
}