[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117127-en":3,"doc-seo-117127-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117127,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Flash Flood Forecasting Using Machine Learning Models - A Scientometric Analysis","Flash flood forecasting addresses a critical hydro-meteorological challenge affecting many regions, including Romania. Flash floods occur frequently and cause substantial socio-economic and environmental damage, making reliable, timely warnings essential. This study conducts a scientometric analysis of literature using open-source tools, ScientoPyGui and VOSviewer, querying Web of Science and Scopus and analyzing 112 retained publications after deduplication. Results show a 60% growth after 2021, with momentum toward deep learning and China leading by share of publications, highlighting machine-learning models’ rising role in mitigation.","How to cite: Bîlbîe F., Zaharia L. (2024) Flash Flood Forecasting Using Machine Learning Models: A Scientometric Analysis. 2024 ”Air and Water – Components of the Environment” Conference Proceedings, ClujNapoca, Romania, p. 1-10, DOI: 10.24193/AWC2024_01.  \nFLASH FLOOD FORECASTING USING MACHINE LEARNING MODELS: A SCIENTOMETRIC ANALYSIS  \nFlorin BÎLBÎE 1,2, Liliana ZAHARIA 1  \nDOI: 10.24193/AWC2024_01  \nABSTRACT. Flash Flood Forecasting Using Machine Learning Models: A Scientometric Analysis. Hydro-meteorological hazards are a major issue in many regions of the world, including Romania. Among these, flash floods are the most frequent phenomena, generating significant annual socio-economic and environmental damages. In recent years, flash flood forecasting using machine learning algorithms has become an useful tool for data-based hydrologic modeling. Machine learning allows to create mathematical relationships between the river discharge and other climatic and physico-geographic parameters from the training dataset. This paper aims to perform a scientometric analysis using open-source programs, namely ScientoPyGui and VOSviewer. The expression ‘flash flood forecasting AND machine learning’ was searched in the Web of Science and Scopus databases. After merging and removing duplicates, 112 publications were retained for analysis. Their number has increased by 60% in the past three years (after 2021) with a trend towards a sub-branch of machine learning, namely deep learning. The spatial distribution of the papers showed that China is a global leader with 25% of the total. These findings highlight the increasing role of machine learning based models (particularly deep learning) in enhancing flash flood forecasting, anonstructural measure for the flash flood risk mitigation.  \nKeywords: flash flood forecasting, scientometric analysis, machine learning, deep learning  \n1. INTRODUCTION  \nHydrological disasters, particularly flash floods, have emerged as a major global concern, causing extensive damage and loss of life. According to report by Centre for Research on the Epidemiology of Disasters (CRED), hydrological disasters had the highest occurrence rate (50%) among all natural disasters between 2006-2015 (Guha-Sapir et al., 2016) . By 2050, damages from hydro-meteorological hazards are expected to reach one trillion dollars annually (Hartnett and Nash, 2017; Bubeck and Thieken, 2018; Tien Bui et al., 2019) . These alarming statistics underscore the urgent need for effective forecasting tools to mitigate the devasting impact of flash floods.  \n1 Faculty of Geography, University of Bucharest, 1 Nicolae Bălcescu Blvd., 010041, Bucharest, Romania e-mail: [florin.bilbie@s.unibuc.ro](florin.bilbie@s.unibuc.ro), [liliana.zaharia@geo.unibuc.ro](liliana.zaharia@geo.unibuc.ro)  \n2 National Institute of Hydrology and Water Management, 97E București-Ploiești Road, 013686, Bucharest, Romania  \nThe term ‘flash flood’ is often used interchangeably, but its precise definition remains a matter of debate within the scientific community (Gruntfest and Handmer, 2001a, 2001b; Kaiser et al., 2020) . While torrential rains are a primary trigger, other factors such as rapid appearance of a large volume of water, or cyclones can also contribute to flooding. In general, flash floods are characterized by rapid increases in river discharge over a short period of time (2-6 hours) and typically occur in watersheds smaller than 250 km2 (Stănescu and Drobot, 2002; WMO, 2011) .  \nHydrological forecasting models play a crucial role in mitigating the impacts of flash floods by providing timely warnings to vulnerable communities. These models can be broadly classified into three categories: ‘black-box’, conceptual and distributed models (Kan et al., 2019) . Machine learning models are part of the ‘blackbox’ models which are also called ‘data-driven’ models. They are based only on historical data and mathematical relationships that they develop themselves (Dazzi et al.","cbCaivpHKa2baz5k","https://ap.wps.com/l/cbCaivpHKa2baz5k","pdf",751112,1,10,"English","en",105,"# Introduction\n## Flash flood definition and characteristics\n## Forecasting models and machine learning\n## Purpose of the scientometric study\n# Data and Methods\n## Literature search strategy and databases","[{\"question\":\"Why is flash flood forecasting considered important in this work?\",\"answer\":\"Flash floods are described as frequent hydro-meteorological hazards that generate significant socio-economic and environmental damage, so effective forecasting tools are needed for mitigation and timely warnings.\"},{\"question\":\"What method and tools were used to perform the scientometric analysis?\",\"answer\":\"The study used open-source tools ScientoPyGui and VOSviewer, searching the Web of Science and Scopus databases with the query “flash flood forecasting AND machine learning,” then merging and removing duplicates.\"},{\"question\":\"How did the research output change over time, and what trend was observed?\",\"answer\":\"The number of publications increased by about 60% in the past three years after 2021, with a shift toward a deep learning sub-branch within machine learning.\"}]","Flash Flood Forecasting Using Machine Learning Models - 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