# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 2 Methodology  
### Methods and Data Sources  
The data were extracted from the Web of Science (WOS) database [12] between 2000 and 2023. This study considered only journal articles, conference papers, and review papers where literature of low relevance to the topic of this study was excluded by analyzing the titles, abstracts, keywords, and other information of the papers for manual screening. The following keywords were used: \"flooding disaster/hazard\", \"flood monitoring\", \"flood risk assessment\", \"flood mapping\", \"flood forecast\", \"flood warning\", \"flood recovery\", and \"flood disaster/hazard\" in the WOS database. The keywords \"flood recovery\" and \"sensor\" were used in the Web of Science Core Collection database, yielding 2429 results covering research areas such as remote sensing, telecommunications, oceanography, meteorology, and atmospheric science. After filtering the preliminary search results and excluding irrelevant research topics (e.g., linguistics, stomatology, paleontology), 791 valid literature data on flood sensors were obtained, of which 122 articles were selected for this study. According to the report of the World Resources Institute (WRI) [13], the top fifteen countries in terms of the number of people affected by floods are mostly developing countries or underdeveloped countries, including India, Bangladesh, and China.  
Some of the remarkable flood events from 2003 to 2023 show the global distribution of flooding scenarios and the driving role of innovations in sensor technology (Table 1). Overall, the emergence of hardware and software technologies such as wireless sensor networks, satellites, radar, drones, social media platforms, and artificial intelligence is crucial.  
\begin{table}
\begin{tabular}{c c c} \hline
**Year of Occurrence** & **Flood Event** & **Flood Sensor Research Directions** \\ \hline
2000 & Mozambique Floods & Remote sensors respond to floods in remote areas [14] \\ \hline
2002 & Elbe and Danube Floods & Emerging technologies for flood monitoring and early flood warning systems [15; 16] \\ \hline
2005 & Hurricane Katrina Floods & Satellite radar and airborne remote sensors for tracking and monitoring floods [17; 18; 19] \\  & & Advanced sensor data processing techniques [20] \\ \hline
2007 & Summer Floods in the United Kingdom & Weather radar and wireless sensor networks help to extend flood warning time [21; 22] \\ \hline
2010 & Pakistan floods & Integration of multi-source sensor data, including in situ and remote sensing, to improve flood monitoring systems [23; 24] \\  & & Low-cost wireless sensor networks and communication technologies [25] \\ \hline
2011 & Thailand floods & Sensor networks integrating satellite data to provide real-time or near-real-time flood monitoring [26; 27] \\ \hline
2011 & Tohoku earthquake and tsunami, Japan & Social media sensors for flood warning and response [28; 29] \\ \hline
2013 & North India floods & High-resolution satellite data for flood warning and emergency management [30; 31] \\ \hline
2013 & Elbe and Danube Floods & Improved sensor accuracy and speed for flood response [32; 33; 34] \\ \hline
2017 & Houston Floods & Multi-radar multi-sensor system for urban flooding [35] \\ \hline
2018 & Kerala floods, India & High-time and high-resolution meteorological remote sensing data [36; 37] \\ \hline
2019 & Mumbai floods, India & Smart flood sensors [38; 39] \\ \hline
2022 & Pakistan floods & Integrating artificial intelligence and machine learning for flood data processing [40; 41] \\ \hline \end{tabular}
\end{table}
Table 1: Flood events drive flood sensor development.