NOE is a module of the multi-risk DRR platform that we have developed at the Spanish company TESICNOR, a leader in prevention and safety in the renewable energy sector. Its activities currently extend to the real-time detection and monitoring of natural hazards such as floods. NOE aims to fill the gap in information exchange among multiple stakeholders when managing an emergency, through a early warning module and an effective digitalization of emergency protocols.
A key to municipal response involves data collection, the generation of high-value products, decision-making, and coordination of measures. A successful solution will consider this overall vision and its effective implementation.
NOE addresses the impacts of riverine and pluvial floods using cutting-edge technologies such as AI/ML and EO-based products. It consists of two parts: the first is an Early Warning System (EWS) that uses real-time information from hydrological and gauge networks, as well as remote sensing observations to predict increases in water flow that could lead to river flooding or extreme rainfall causing severe impacts in high-risk areas such as cities, infrastructures, or transportation routes. The second part is a digital emergency manager designed to organize the sequence of actions to be carried out by a coordination center. This manager keeps an order and record of measures taken, which in the case of floods usually include access and route closures, removal of elements like cars susceptible to being swept away by the water current, or, in the worst case, forced evacuations of people from flooded zones.
An adage in the practice of emergency management is “all disasters are local”, although, depending on the severity of the situation, the response may be coordinated at a supra-municipal level. That is why preparation and immediate response involve an exhaustive understanding of the physical risk and its immediate consequences in the environment where the event occurrs.
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NOE is an application developed by Tesicnor that alerts about floods and prevents negative consequences on the safety of people and property, as well as on the environment, cultural heritage, economic activity, and infrastructure.
The main objective focuses on providing high-value information to municipal agencies and first responders in relation to the detection and monitoring of the physical risk, and analysis of actions concerning riverine or flash flood evolution. In both cases, it is essential to carry out prior plans and studies, as well as to collect and analyze data in order to implement an effective early warning system. This information allows for issuing alerts, monitoring the situation, and coordinating various key elements involved in the response phase to flood effects that could threaten human lives and assets.
Furthermore, NOE can contribute to the improvement of climate change adaptation strategies or local, comprehensive protection measures plans. It is important to emphasize that investing in preparation and adaptation phases can lead to significant cost savings in disaster response. Additionally, minimizing damages can have substantial benefits in water management, biodiversity, health, and well-being. Therefore, adaptation is cost-effective and offers collateral benefits, with the protection of human lives being the most important.
The case of the flood event in Valencia in October 2024, along with other similar episodes, clearly demonstrates the need for better products focused on early warning and the coordination of actions at the municipal level. In the case of the Valencia episode, the regional emergency coordination center was clearly overwhelmed. A tool like NOE could free up resources in a center that is currently handling calls and coordinating actions at a supra-municipal level, as it aims precisely to facilitate a more organized and effective municipal response.
We list a series of items and functionalities that show the innovative nature of the situation:
Integration with open public data, river sensors, weather radars, remote sensing data, rain gauges, as well as with IT technologies and sources such as IoT sensors or crowdsourcing.
Information layers that are generated automatically with high-refresh updates and in interoperable formats.
Digitization of plans and automatic activation of protocols.
Integration of new Earth Observation technologies and application of Artificial Intelligence/Machine Learning techniques to monitor and predict the behavior of floods, both riverine and pluvial. For flash floods, data fusion is employed using Deep Learning techniques with the application of deep neural networks that learn from a historical database of millions of weather radar images. Predictions are made over a time horizon of one to two hours, which is critical for most flash-flood events. Since radar structures repeat during similar atmospheric events, machine learning is effective. For river floods, recurrent neural networks are applied to data, as an archetype of networks that possess some form of temporal memory.
Another innovation relates to the fact that the application is aimed both at first responders (such as those coordinated in an emergency control center) and at the general public (connecting to information delivery through various access channels). This allows citizens to have real-time information and improve their self-protection, which contributes to a better general response to the emergency by easing the activity of first responders.
NOE is currently available as an app and mobile version, although one future goal is to evolve the tool toward an agent-based system that employs modern artificial intelligence interaction techniques.
Overall, the solution increases preventive and resilient capacity, reducing the potential impacts derived from flood risks.
The solution enables continuous detection and monitoring of the phenomenon to be observed, in this case floods or heavy rainfall, allowing for follow-up on the measures adopted at the municipal level or even for individual assets such as infrastructure or production plants. It also forecasts possible scenarios in the very short-term horizon of nowcasting.
It is also important the possibility of performing practices or drills to train with the tool in various user modes. The solution aims to be an easy-to-use and accessible tool, intuitive, complemented by a geospatial viewer that facilitates the identification of critical points when organizing the response to an emergency at the municipal level.
It is not only useful for emergency managers at the municipal level but also for regional or national agencies to monitor and track the emergency, verify that municipal authorities are acting properly, and coordinate with them.
As mentioned, NOE is a digital tool that provides critical information during a flood emergency. An practical example follows: if it is predicted that a river flow is going to reach a value that requires closing a specific access point the tool will enable monitoring of the phenomenon, issuing alerts with sufficient lead time (based on very short-term predictions within a horizon of 1 to 12 hours), and organizing the responsible personnel for the required action. All this process is recorded for future analysis.
The system contributes to greater resilience and preparedness for the emergency, reducing the final impact, so that first responders can focus their efforts on other objectives during the response phase.
We have a powerful multidisciplinary team of data scientists, software engineers, AI developers, and experts in GIS and remote sensing who have contributed to the development of NOE. Additionally, a marketing expert helps us enhancing the product's dissemination.
Furthermore, we possess expertise and know-how in the fields of meteorology, hydrology, geography, and land management, which are closely linked to climate risks that are increasingly posing greater threats to people and property.
We have a powerful multidisciplinary team of data scientists, software engineers, AI developers, and experts in GIS and remote sensing who have contributed to the development of NOE. Additionally, a marketing expert helps us enhancing the product's dissemination.
Furthermore, we possess expertise and know-how in the fields of meteorology, hydrology, geography, and land management, which are closely linked to climate risks that are increasingly posing greater threats to people and property.