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Model to accurately assess rainfall-induced damages in road networks
Scientists at the Indian Institute of Technology Gandhinagar (IITGN) have developed a new integrated predictive model to accurately assess damages in road networks caused by heavy rainfall
Scientists at the Indian Institute of Technology Gandhinagar (IITGN) have developed a new integrated predictive model to accurately assess damages in road networks caused by heavy rainfall.
The approach, described in the journal Environmental Research Letters, uses satellite imageries, landslide and debris flow models, and flood prediction models to predict the geographical locations that have a high susceptibility of damages after heavy rainfall episodes. It also provides the occurrence time and magnitude of infrastructure disruptions as a consequence of landslides, debris flows, and floods, the researchers said. This hazard model can help authorities identify the 'hotspots' which should be reinforced and protected to solve highway-flooding issues and minimise the societal and economic disruptions, they said.
The team, including Udit Bhatia, Raviraj Dave and Srikrishnan Siva Subramanian, from IITGN, developed the mathematical model to accurately understand and assess the real magnitude of heavy rainfall-induced damages in road transport network. The framework uses data on road network of the terrain and daily precipitation data to analyse the shallow landslides, debris flows, and a gauged reservoir discharge data for flood inundation. The occurrence timing, locations, and magnitude of infrastructure disruptions provided by this model are predicted using numerical calculations, the researchers said. The results identify the least to most weak links within a road network and helps in deciding the locations of road segments that need strengthening, which in turn, can minimise the societal and economic disruptions during heavy rainfall emergencies, they said.
The landslide models developed by the team also take into account the existing vegetation cover, land use characteristics, water flow characteristics, and binding properties of the soil. All these parameters are fed into the mathematical model to calculate the slope stability at each pixel. This model can also estimate the effect each stabilisation effort — changes in vegetation, reinforcements at each location — would have on the slope stability. (PTI)