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Floods have been a recurring problem for a number of countries around the world including Pakistan. It is believed that densely populated forests at river banks can prevent floods from spreading towards settlements and farmlands. The role of forest in flood spread has been an area of research for a while but the role of predictive modeling in this area is yet to be investigated in detail. In this study, we have used predictive analytics and satellite imagery to develop an environmental model that can predict the flood spread by considering forest cover at river bank and month of the year as parameters. We have used the satellite images of an area situated in the northern region of Pakistan i.e. Dera Ghazi Khan from the USGS’s Land Sat program. These images comprised of a section of the Indus River and its adjoining areas. We want to analyze the forest bank at various section of the Indus River. We developed and trained our predictive model by using the satellite imagery data and tested it on a separate dataset to determine error percentage. The model showed significant promise and predicted the flood spread with an average accuracy of above 80%.

Analyzing the Impact of Forest Cover at River Bank on Flood Spread by using Predictive Analytics and Satellite Imagery