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The Climate Change and Flooding: Bibliometric Analysis to Identify Fu-ture Research

Davronjon AllayorovTashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent; Tashkent State Transport University, 100167, TashkentKudrat RakhimovTashkent State Transport University, 100167, TashkentUchqun UmarovTashkent State Transport University, 100167, TashkentDinislam AtakulovTashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, TashkentDilbar AllayorovaTashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent; Research Institute of Irrigation and Water Problems, 100187, TashkentSamandar ShaymardonovTashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent
Forum Geografijournal2026
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Among the various hydroclimatic phenomena, floods occupy a special place due to their environmental and socio-economic impact. However, identifying the main focus of research related to climate change and floods requires examining large number of publications. The article presents a bibliometric analysis of publications on climate change and floods published in the Web of Science database between 2015 and 2024. Analysis indicate that climate change-induced flood research is a rapidly expanding and increasingly popular field. The majority of publications are scientific articles (94%), and over the last 5 years there has been an improvement in their quality. Much of the research is conducted in China, the USA, and European union. The Chinese Academy of Sciences is a leader in this field, and journals such as Science of Total Environment, Water, and the Journal of Hydrology are active in disseminating research results. Bibliometric analysis via VOSviewer and Bibliometrix finds that under the climate change context, the major institutions are studying precipitation changes and water management in catchments. Flood susceptibility and forecast streamflow are the central topics that must be deeply studied for developing highly accurate early warning systems. And in this process, physically based hydrological modeling must be incorporated by advanced soft computing techniques, particularly machine learning, artificial neural networks, and geospatial tools like geographic information systems and remote sensing.

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