World Metrological Organization reports that out of majority of all disasters in the world, flooding is one of the most severe disasters affecting the people across the globe.
India is one of the worst flood affected country in the world and total Indian rainfall is concentrated over a short monsoon season of four months. As a result, the rivers witness a heavy discharge during these months, leading to widespread floods.
Several states suffer from severe annual flooding. Unfortunately, they do not have an early warning mechanism that would alert the affected regions from the occurrence of a disaster. The existing disaster management mechanism is primarily focused on strengthening rescue and relief arrangements during and after disasters. As a result:
It is desired to have a technological solution that could
SAP HANA platform with its high performance, advanced analytical capabilities (Predictive, Geospatial, Text analytics) and UI5 technology front end is suited well to address these technical challenges.
A solution, built on SAP HANA, would enable the state irrigation department to centrally monitor, analyse and forecast flood situations. It can automatically alert the administration and the population about probable occurrence of a flood event. This would help take necessary measures to minimize the loss of human lives and mitigate the damage to properties.
Arteria Flood Real-time Monitoring (FoRM), uses sensor data from various sites such as reservoirs, dams, canals and consolidates it using the SAP HANA platform for monitoring in real time via a central dashboard using maps. Currently, sensor data from 4 major river basins across roughly 1000 sensor points is collected into SCADA servers and pulled into HANA platform every 5 minutes. Automatic alert notifications are calculated in case of a threshold breach and predictive algorithms are used to forecast a flood situation based parameters such as rainfall, water level and discharge levels.
FoRM has the potential to save the loss of property and lives of citizens by accurate forecasts of flood situations and the impacted geographic zones.
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