Climate change is exacerbating sea-level rise and the frequency/severity of coastal flooding, significantly impacting vulnerable communities. Real-time water level sensor networks offer detailed, accurate information for flood risk mitigation and emergency response, surpassing the capabilities of traditional monitoring methods like single offshore tide gauges. Optimal sensor placement is crucial to maximizing the network's benefits, given resource limitations. Existing approaches, however, have significant shortcomings. Traditional quantitative methods focus primarily on coverage and uncertainty, neglecting flood-specific factors and social vulnerability. Qualitative approaches, relying on local expertise, lack a quantitative basis and can be subject to bias. This study addresses these gaps by developing a novel methodology for optimal sensor placement that considers multiple objectives, including sensor-related, flood-specific, and social parameters. This comprehensive approach ensures that sensor networks provide the most relevant and beneficial data for effective flood risk management.

Previous research on sensor placement primarily focused on optimizing network coverage and minimizing uncertainty. While some studies combined network utility parameters, none integrated flood risk, social vulnerability, and infrastructure exposure into sensor network assessments. Existing methodologies often prioritize minimizing uncertainty in unmonitored regions, assuming equal importance across all areas. This ignores the inherent diversity of coastal communities in terms of population density, housing characteristics, critical infrastructure, and flood risk levels. Qualitative approaches, while incorporating local knowledge, lack quantitative rigor and may overlook critical areas due to historical biases. This study bridges this gap by proposing a novel methodology that systematically integrates multiple objectives into the decision-making process, improving upon the limitations of previous quantitative and qualitative approaches.

This study employs a multi-objective optimization approach, combining it with geographic information system (GIS) visualization to facilitate both quantitative analysis and effective communication with community stakeholders. The methodology incorporates five key network parameters: 1. **Network Coverage:** Quantifies the increase in the area monitored with the addition of a new sensor, using an inundation mapping algorithm with a 20% error threshold and a 5km decay distance. 2. **Network Uncertainty:** Measures the mean error across the inundation map, reflecting the confidence level in interpolated water levels. 3. **Critical Infrastructure Facilities Density:** Uses kernel density estimation to assess the proximity of potential sensor locations to critical facilities (hospitals, police stations, power facilities, schools), prioritizing data collection near essential assets. 4. **Flood Zone:** Leverages FEMA flood zone data to prioritize locations with higher flood likelihoods (VE, A, AE, AH, A99, X_500, X zones). 5. **Damage Assessment Priority Index (DAPI):** Integrates socioeconomic indicators, vulnerable residential factors, and housing unit characteristics to prioritize locations with higher social vulnerability. The methodology is applied sequentially. Initially, the full solution space of potential sensor locations is defined based on the study area, resolution, and feasible waterways. Then, the five parameters are calculated for each potential location. A multi-objective optimization algorithm (Pareto frontier method) identifies a prioritized subset of non-dominated solutions, significantly reducing the number of potential locations for community consideration. GIS visualization facilitates communication and decision-making by highlighting solution clusters. The process iteratively updates the network after each sensor installation, allowing for sequential network expansion and continuous optimization.

Applying the methodology to Chatham County, Georgia, reduced the initial 28,890 potential sensor locations to a prioritized set of 381 non-dominated solutions (1.3% of the original set). This significant reduction aids community decision-making. The optimized locations were not solely determined by maximizing coverage and minimizing uncertainty (traditional metrics), but also by considering flood risk, infrastructure exposure, and social vulnerability. The analysis showed that the resulting locations were significantly different from those identified using only traditional network expansion parameters. Most of the identified optimal locations were not near existing sensors, contributing to increased network coverage. GIS visualization identified clusters of solutions, further simplifying decision-making. The sequential expansion capability of the method was demonstrated, allowing for continuous optimization as the network grows and resources become available. Collaboration with Chatham County officials demonstrated the practical application of the methodology, involving real-world considerations in selecting final sensor locations. This collaboration highlighted the practical implementation and benefits of integrating quantitative analysis with local expertise.

The study highlights the critical need to move beyond traditional coverage and uncertainty metrics in sensor placement decisions. The integrated approach presented here provides a more comprehensive assessment of flood risk, accounting for the complexities of coastal communities and the multifaceted impacts of flooding. Including flood-specific and social parameters ensures that sensor data is relevant for both mitigation efforts and emergency response, particularly benefiting vulnerable populations. The significant reduction in potential sensor locations achieved by the multi-objective optimization demonstrates the method’s effectiveness as a decision support tool. The sequential expansion capability enhances the long-term value and adaptability of sensor networks. The integration of GIS visualization ensures effective communication and empowers community stakeholders to make informed decisions based on both quantitative analysis and local knowledge.

This study presents a novel multi-objective optimization methodology for strategically placing real-time water level sensors in coastal communities. The method effectively incorporates sensor-related, flood-specific, and social vulnerability parameters, significantly reducing the number of potential sensor locations and facilitating community decision-making. The sequential expansion capability supports the long-term development of comprehensive, effective sensor networks. Future research could explore the integration of additional parameters, such as economic impacts or specific community resilience goals, and the development of more sophisticated inundation mapping algorithms.

The accuracy of the results depends on the quality and availability of input data (e.g., LiDAR, FEMA flood zones, socioeconomic data). The DAPI, while effective for Chatham County, may require adaptation for other communities. The 5km decay distance in the inundation mapping algorithm is a simplification and could be further refined. The study focuses on sequential sensor placement rather than simultaneous placement of multiple sensors, which could be investigated in future work. The focus was on water level sensors, which are valuable in coastal environments; for other environments, other sensors and modelling approaches may be necessary.