Machine Learning in Disaster Management: Recent Developments in Methods and Applications
- Journal
- SHILAP Revista de lepidopterología
In The Last Decade
doi.org/10.3390/make4020020 →Countries where authors are citing Machine Learning in Disaster Management: Recent Developments in Methods and Applications
This map shows the geographic impact of Machine Learning in Disaster Management: Recent Developments in Methods and Applications. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Machine Learning in Disaster Management: Recent Developments in Methods and Applications with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Machine Learning in Disaster Management: Recent Developments in Methods and Applications more than expected).
Fields of papers citing Machine Learning in Disaster Management: Recent Developments in Methods and Applications
This network shows the impact of Machine Learning in Disaster Management: Recent Developments in Methods and Applications. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Machine Learning in Disaster Management: Recent Developments in Methods and Applications.
About Machine Learning in Disaster Management: Recent Developments in Methods and Applications
This paper, published in 2022, received 130 indexed citations . Written by Maria Drakaki, Panagiotis Tzionas and Yannis L. Karnavas covering the research area of Artificial Intelligence and Sociology and Political Science. It is primarily cited by scholars working on Artificial Intelligence (40 citations), Global and Planetary Change (32 citations) and Sociology and Political Science (23 citations). Published in SHILAP Revista de lepidopterología.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.3390/make4020020.