Thiruvananthapuram: A scientific team comprising experts from IIT Roorkee, IIT Palakkad and IIIT Kottayam appointed by the state govt has recommended using artificial intelligence, machine learning, remote sensing, hydrodynamic modelling and high-precision rainfall forecasting to identify flood-prone areas in Kerala and issue accurate early warnings.The recommendations are part of a preliminary report on the proposed Kerala flood resilience initiative, which seeks to strengthen the state’s preparedness against extreme rainfall, urban waterlogging, riverine floods, landslides and mudslides. The project envisages a comprehensive flood-resilience system using advanced technology to mitigate rain-related disasters aggravated by climate change and other natural hazards.The team also cautioned against removing silt and sediment from rivers, lakes and backwaters without first conducting scientific studies. The environmental impact of desilting and its effect on the natural flow of water should be assessed before such work is undertaken, the experts said.The proposed system would enable the authorities to identify vulnerable areas in advance, assess the risks posed by different kinds of disasters and issue timely warnings. Govt said the comprehensive framework could serve as a model for flood management in the country.The expert team called on chief minister V D Satheesan and handed over the preliminary report. Following the presentation, Satheesan directed the water resources and disaster management departments to jointly examine the project’s technical feasibility, financial implications and implementation methods.The departments have been asked to complete the assessment and submit a report within a month. Further steps towards implementing the Kerala flood resilience initiative will be taken based on their findings.Revenue minister A P Anil Kumar, environment minister Sunny Joseph, agriculture minister T Siddique, water resources minister Mons Joseph and chief secretary Biswanath Sinha attended the high-level meeting.
