Back close

Fluvial Geomorphological Dynamics and Flood Modelling: An Integrated Geospatial Artificial Intelligence  (GEOAI) Approach

Dept/Center/Lab: Amrita Center for Wireless Networks and Applications (AWNA)

Project Incharge:Dr. Alka Singh
Fluvial Geomorphological Dynamics and Flood Modelling: An Integrated Geospatial Artificial Intelligence  (GEOAI) Approach

The study assesses the post-flood impact on geomorphology and vegetation in the Periyar River basin. It aims to develop a flood prediction model using MIKE-FLOOD and ML, followed by analyzing post-flood geomorphological dynamics and vegetation patterns. The ultimate goal is to derive a flood preparedness and mitigation model to minimize the impact of future flood events. 2018 and 2019 Periyar flood were estimated using SAR data and flood depth estimation using GEE and Mike+ model. Dam discharge data from Neeleswaram , Vandiperiyar and Boothathankettu has been collected. 2D Flood modelling is in progress based on high resolution GPM rainfall and dam discharge making these to 3 points as separate watersheds into the model. In Periyar river significant morphometric changes were not observed, therefore additional study area of Kosi river has been added for further exploration.

Amrita Team Members: Shebin S M

Name of the International Collaborators: Prof. Guido, Univ of Trento Italy

Related Projects

DNA ORIGAMI – Folding of the Vector (pCDH–CMV–MCS–EF1–puro) into a Predefined Shape Using 18, 20mer Staples
DNA ORIGAMI – Folding of the Vector (pCDH–CMV–MCS–EF1–puro) into a Predefined Shape Using 18, 20mer Staples
Non-Invasive Estimation of Cholesterol through Photoplethysmography leveraging IoMT and AI 
Non-Invasive Estimation of Cholesterol through Photoplethysmography leveraging IoMT and AI 
Hardware Implementation of Antenna Beamforming
Hardware Implementation of Antenna Beamforming
A Framework for event modeling and detection for Smart Buildings using Vision Systems
A Framework for event modeling and detection for Smart Buildings using Vision Systems
Mass spectrometry based proteomic characterization of carbonylated proteins as indicators of oxidative stress
Mass spectrometry based proteomic characterization of carbonylated proteins as indicators of oxidative stress
Admissions Apply Now