# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 4 Discussion
### Data Management and Data Quality  
Risk knowledge plays a key role in EWSs. According to the IPCC [143], risk is \"the potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives associated with such systems\". Hazard, vulnerability, and exposure are the primary components of risk, and their relevance is constantly evolving. A major challenge remains the collection of relevant data on these elements, as well as ensuring access for decision-making authorities [9]. Figure 3 illustrates a framework for data-driven risk assessment. In this context, data management becomes crucial. It is one of the recommendations in the National Disaster Risk Assessment document. Risk assessment is a process heavily dependent on the availability and quality of data. Experts recommend the creation of a common data repository to facilitate regular updates and risk assessments. The best way to ensure data availability, based on experience, is through legal regulations. Therefore, in-depth analyses and gap assessments should be conducted between available and required data [144]. As mentioned earlier, automation and digitisation of data are necessary. Data from satellite imagery and sensors can be used for developing vulnerability maps to plan how to prevent certain hazards or to predict damages. It is necessary to count with data and assess the capacities in place to monitor risks, as part of EWSs. Machine learning and artificial intelligence has a huge potential to classify and evaluate information from ongoing events [135]. Through sophisticated pattern recognition applied to vast datasets, AI can more accurately predict natural hazard occurrences, including time, location, and scale. This enhanced prediction enables faster and more effective early warning systems, bolstered by the integration of varied data from sources such as sensors and satellites [145]. Despite improvements in the availability of meteorological and hydrological data, gaps remain in exposure, vulnerability, and past crisis event data. Such data are typically available at regional or national levels; however, for MHEWSs and risk assessment, data from the local or community level are essential. The Global Status of Multi-Hazard Early Warning Systems 2024 outlines key areas and recommendations for data management and risk knowledge:  
* Centralisation of data;
* Data integrability;
* Focus on local-level data;
* Data accessibility for decision-makers;
* Ensuring data quality;
* Focus on data security [9]. An important step towards effective data management is the establishment of a national data governance framework and the development of operational procedures to ensure data reusability and accessibility. Investments in data infrastructure and the design of interoperable systems are also crucial. These efforts should aim to improve field data collection and streamline online reporting, ultimately enhancing efficiency and accuracy in disaster response [11]. Figure 3: Data-driven risk assessment. The loss data sector and its information systems and database architecture have been studied by [PERSON] et al. (2022) [146]. In terms of information communication, it is essential to prevent errors. In EWS communication processes, errors can occur at any stage, whether due to the sender, receiver, or transmission process. The sender may misinterpret the situation, misidentify data, or incomplete information may be sent under stress. Transmission errors may result from technical failures, network issues, or other unforeseen circumstances. The receiver might misunderstand the message or alter it based on assumptions. Such errors can lead to incomplete or incorrect decisions, resulting in further damages and losses [47]. The use of artificial intelligence and machine learning in these systems can significantly reduce errors, particularly if algorithms for data evaluation are properly configured. This paper identified in Table 4 various types of sensors that can be universally applied.