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Why you should use camera-based flood disaster monitoring system

Why you should use camera-based flood disaster monitoring system
The collateral damage of COVID-19 has been that it eclipsed some of the other major disasters that the world has been witnessing every year. For instance, early in 2020, the Indonesian capital of Jakarta saw flash floods that resulted in widespread damage. Similar incidents have occurred in other parts of the world too. Although COVID-19 has emerged as the biggest catastrophe in recent history and the security industry is mostly focussed on dealing with it at at the moment, issues like floods are no less dangerous.

Fortunately, there is a solution to detect the possibility of the flood before it happens so that people can take precautions and avoid a disaster. Some companies have come up with video analytic solutions that make use of footage from security surveillance cameras to detect the rise of water levels in rivers and other water bodies.

What makes video-based water-level monitoring better

Traditionally, two kinds of sensors have been used to monitor rising or receding water levels – pressure-based and ultrasound/radar-based. The pressure-based solution is inexpensive but is intrusive and hence prone to damage. Ultrasound or radar-based solutions are expensive and difficult to install. This is why a camera-based solution becomes relevant.

A France-based company that provides a camera-based water level monitoring solution is Tenevia. Speaking to asmag.com recently, Leo Estrade, Business Developer for the company, explained how their solution works.

“We have two main solutions based on image processing algorithms is that we developed based on CCTV cameras,” Estrade said. “One is for water level measurement, and the other is to detect surface velocity and discharge. So usually when we talk about detecting flood risks, the first step would be to have a vision of the watercourse and have water-level sensors in order to send alerts.”

The hardware that’s needed

Tenevia’s solution can be run on the edge or the cloud and is camera agnostic. But Estrade points out that certain factors need to be prioritized when setting up the camera. Selecting a camera that is cyber-secure is also important. 

“The accuracy of the sensor is going to depend on the resolution of the camera and the distance between the camera and the target,” Estrade explained. “Basically, we recommend three kinds of hardware, for 20 meters site, 40 meters site, and sites beyond 40 meters.”

Demand in the market

Naturally, one of the major customers for such a solution would be governments. However, as the solution becomes more popular, verticals that are hydro-dependent also find this solution attractive. For instance, wastewater management, hydroelectric power, agriculture, and irrigation are all sectors that could make use of this solution.

“We plan to work with institutions that deal with natural disaster risk management,” Estrade said. “For early warning systems, we work from government agencies. We are even targeting industries that are not directly hydro-dependent but have rivers or water bodies that pose flood threats to their premises.”

Smart city demand

The benefits of the solution are evident, but the real demand would come in as smart city projects gather further momentum globally. A smart city relies heavily on sensors that would enable the authorities to prepare themselves for any kind of natural disaster. Being able to use sensors that are based on surveillance cameras would save costs and enable easier set up.

“We work a lot with smart cities that are looking for monitoring solutions,” Estrade said. “We work with municipalities that have some issues with water.”

The solution may find more takers in markets that are more prone to floods and water-related issues. For instance, some regions are particularly prone to typhoons and hurricane-related challenges. Customers in such areas might find this solution more attractive. However, as Estrade explains, video-based water level monitoring has potential applications across verticals.


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