Monitoring Floods with SAP Cloud Platform

The region was on high alert all week; NASA, the Weather Channel, and all major news agencies warned of a huge category 4 tropical storm forming at top of the Gulf of Mexico and prepared citizens for an impending catastrophe.


Imagine having only 10 minutes to flee your home. Here, SAP employee Shibaji Chandra shares the story of how he built an app so he would know when his family was safe from floods.


It started raining the morning of Friday, August 25, and continued relentlessly as the storm neared landfall. Anticipating the calamity, people began stockpiling water, basic food, and so on from Wednesday onward. As a result, local market had already run out of water and other necessities by Thursday night.

Harvey is Here

It rained hard for three days, non-stop. We watched the news reports of flooding all over Houston, but to our surprise, the area of Katy where I live was not hit until Monday, August 28. That morning, the rain seemed to have slowed down, but before we could breathe a sigh of relief, a team from the Federal Emergency Management Agency (FEMA) came to our apartment and warned us that our apartment complex would soon be under water. Initially we thought it would be a temporary issue and that the water would recede in a few hours, so we didn’t consider evacuating.

The Great Escape

But it rained steadily all night, and to our worst nightmare, the reservoir near our apartment spilled over and flooded our entire apartment complex. On Tuesday, August 29, we were forced to evacuate our home, moving out on a rescue boat under evacuation orders from local authorities. Luckily, we have a good friend near downtown Houston whose apartment was not flooded, so we decided to take shelter at his place. Soon thereafter, three of my friends along with their respective families had to evacuate their apartments, too, and joined us at our friend’s apartment.

In Safety

There were four families (12 members) who had found refuge in my friend’s apartment. My good Samaritan friend had no dearth of effort to make the place comfortable, but the worry started building within us. There was little we could do at that moment apart from watch the news, pray for the best, and wait for the floods to recede.

Ray of Hope

Doing some research, I learned that the U.S. Army Corps has installed many IoT sensors, such as radar, press transducer, across the United States that monitor vital statistics such as water level, water body areas, and so on in near real time. Looking more deeply, I discovered some APIs from the U.S. Geological Survey that could feed me the data of these IoT sensors. Bingo! We started monitoring the data.

Checking JSON payload every 15 minutes is not very relaxing, so I decided to build an app based on SAP Cloud Platform. After all, when you have free Internet and three friends are snoring beside you, what else better to do at night? I started designing my app to monitor the reservoir near my apartment. And the next morning, I developed it.

This was very handy, as I could see the water level from my cellphone. Later I decided to transfer the same data to SAP Cloud Platform cloud portal.

This portal is currently set for the Barker Reservoir near my apartment only but can easily be configured for any other water body where the U.S. Army Corps has installed high-end sensors.

Hurricane Irma

Less than two weeks later, while we were slowly recovering from the aftermath of Hurricane Harvey in Texas, another threat of a category 5 storm loomed over Florida. The phenomenon of hurricane and flood in its aftermath seems to be inevitable in certain parts of the country. So I decided to develop the app further and make the flood monitoring site more dynamic. Now you can enter your address and monitor the water levels of areas across the nation. Just follow these three simple steps:

  • Enter your address. The site will find and list the water bodies near you.
  • Select from the hit list.
  • The location is shown in Google Maps and the water level data is shown as a graph in real time.