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India modernises flood forecasting through AI/ML

The Central Water Commission (CWC), under the Ministry of Water Resources, is primarily responsible for predicting and preventing floods. It issues flood forecasts as a non-structural measure of flood management to concerned state governments at their request. CWC also issues inflow forecasts to identified reservoirs to maintain proper regulations.

According to a press release, the dissemination of flood forecasts has been modernised through a dedicated website. Flood-related information is also shared via various social media platforms under the CWC Flood Forecast Dissemination system. Daily flood situation reports and advisories are shared with stakeholders and the general public. CWC has signed a memorandum of understanding with Google, which allows them access to its vast repository of high-resolution digital elevation models. The government is able to send alerts regarding inundation through Google platforms using the flood forecasts issued by CWC.

The release noted that CWC uses all the latest technology including remote-sensing, geographical information systems (GIS), artificial intelligence (AI), and machine learning (ML). The technologies are applied in the development, formulation, implementation, and calibration of mathematical models and to provide inundation alerts, which are at par with international standards.

At the end of the flood season, CWC prepares an appraisal of flood forecasting activity in which the accuracy/performance of the forecasts is compiled. In the conventional methodology of forecasting, a prediction is deemed to be accurate if the forecasted level is within 0.15m from the actual river water level attained at the forecasted time. Similarly, for inflow forecasts, if the estimate is within 20% of the actual inflow into the reservoir, it is accurate.

CWC also employs the mathematical modeling of a river basin based on the rainfall-runoff methodology. Input taken is rainfall provided by the Indian Metrological Department (IMD) through its automatic weather station (AWS), automatic rain gauge (ARG) stations, and CWC telemetry stations.

The three-day advance forecast is generated using various available rainfall data products as a major input into the system. These include the IMD Gridded Rainfall product, as well as other global rainfall products like the global satellite mapping of precipitation (GSMaP), global precipitation measurement (GPM), and IMD forecasted rainfall data.

Numerical Weather Prediction (NWP) model products, namely the weather research and forecast (WRF) model and the global forecasting system (GFS) model product are seamlessly shared by IMD for CWC’s mathematical models. Mathematical models have been used for the formulation of advisories, which are shared with stakeholders on the government website.

IMD supports the CWC flood warning services by providing observed and predicted rainfall. To meet the specific requirements of flood warnings, IMD operates flood meteorological offices (FMOs) at 13 locations across the country. FMOs provide meteorological support to the CWC to issue flood warnings well in advance in respect of 153 river basins.

IMD has also developed tools for cyclone warning services. It had collaborated with the Indian National Centre for Ocean Information Services (INCOIS), which set up a Storm Surge Early Warning System (SSEWS). The prime objective of this service is to save the lives of coastal communities by forecasting cyclone-induced storm surges.

Further, the government initiated the National Cyclone Risk Mitigation Project (NCRMP), which aims to undertake suitable structural and non-structural measures to mitigate the effects of cyclones in the coastal states. Under the second phase of the project, a web-based Dynamic Composite Risk Analysis (Web-DCRA) & Decision Support System (DSS) tool was developed to forecast the expected damage associated with the landfalling cyclones over coastal districts. This will lead to better assessments of vulnerable areas and help mobilise mitigation action.

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