# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 5 Conclusions  
The monitoring and damage assessment of floods by using different kinds of remote sensing and ground-based sensors highlighted the influence of climate change, flood events, and technological advancements on the evolution of flood sensor technologies and policies. Since 2000, global warming has driven a rise in extreme weather events, increasing the demand for effective flood management systems. It is found that a strong correlation exists between the advancement of flood sensor technology and the effectiveness of flood management. In situ sensors, evolving with IoT support, remain fundamental, while remote sensing and social sensors show promise but require further development in cost-efficiency and data accuracy. Due to the high cost of precision sensors and platforms, even developed countries are constrained from deploying them extensively. AI and multi-source data integration offer significant potential, particularly in the area of flood monitoring, forecasting, and management. AI can help collect, analyze, and publish data from a variety of sources provided by sensors, such as remote sensing, smart sensors, and social media, and machine learning and deep learning models, such as support vector machines (SVMS), random forests (RF), convolutional neural networks (CNN), long short-term memory networks (LSTM), U-Net, and Transformer can play a different role when combined with historical or real-time data and can be used to improve the flood warning and forecasting system and various evaluation systems to improve the efficiency of flood disaster management. Satellites have become an effective tool to acquire global data sets, so extensive research on artificial intelligence algorithms to process integrated spatial data has become an inevitable trend, especially in the more complex, image-based flood monitoring, in which the remote sensing platform reflects the economy and efficiency. Recent flood monitoring studies indicate that developing countries face significant challenges in managing floods. In addition to environmental factors such as complex terrain, topography, vegetation cover, and lighting conditions, their inadequate sensor deployment, emergency response mechanisms, and experience in flood management further hinder effective disaster management. The most direct solution would be to invest in flood detection infrastructure and technology, develop multi-source data integration, enhance data analysis capacity, and update flood policies and laws. Also, urban flooding is different from other types of flooding, requiring more advanced sensor technology and data processing technology. However, if developing countries struggle in the short term to deploy high-precision and high-density sensors or train skilled personnel in advanced remote sensing technologies, they can seek assistance through international cooperation programs or organizations such as Sentinel Asia, IFI, GFDRR, etc. For example, during the 2010 Pakistan floods, the country, as a member of the program, accessed satellite imagery from ALOS, Resources-1, and FORMOSAT-2 to support disaster management. Based on this, we propose a framework to facilitate global collaboration and resource-sharing in flood disaster management (Figure 6). In light of the current situation, developing countries should prioritize structural measures, emphasize the training and recruitment of technical personnel, and avoid the scenario of possessing basic data without the capacity to utilize it effectively. Additionally, enhancing disaster awareness and skills is crucial to strengthen the self-rescue capabilities of residents. Developed countries should consider prioritizing the development of non-structural measures for a safer and more resilient future for everyone exposed to the threat of flooding. **Author Contributions:** [PERSON] and [PERSON] designed the research; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] collected, collated, and summarized the literature and wrote the first draft of the article.