Land subsidence prediction using recurrent neural networks

In an environment, one of the natural geological hazards is land surface subsidence. Underground mining and subsurface coal fires are primarily responsible for subsidence of land. Activities, such as, over-exploitation of coal, minerals, groundwater and petroleum resources, depillaring of the existing galleries and water logging of the relinquished galleries are the major factors resulting in subsidence. The deformation is primarily measured in terms of change in ground elevation values (Z-dimension) at different time intervals at identified ground locations. All the conventional and exiting techniques have certain limitations in monitoring and predicting land surface subsidence. In this work, we predict the land subsidence in Jharia Coalfield, Dhanbad, India for one year in the interval of twelve days on the datasets collected through a monitoring technique called Modified PSInSAR. The sample datasets contains 14 locations and 67 previous land subsidence value calculated from each location. We train and test predictive models and perform the prediction of the land subsidence using Vanilla and Stacked long short-term memories. Finally, we demonstrate the predicted deformation values of the 14 locations for one year. The prediction model shows the subsidence rate in Nai-dunia basti near Jharia, Dhanbad is alarming as 93.8 mm/year where as Digwadih and Godhar showed the critical rate as 82 mm/year.