FloodGauge is a portal that aims to estimate the probability of flooding in a region based on its history of changing land cover and an increase in demand for water and forest resources owing to the increasing population. By making timely policy changes and ensuring organized infrastructural development, cities can reduce the chances of flooding. As part of our proposed future work, the model will be able to predict floods at least a week in advance to help citizens and authorities execute the timely evacuation process. Cities that understand the urgency of action, upgrade their drainage systems, and ensure stricter laws against land reclamation stand the highest chance of preventing flooding or at least reducing the losses.
How does it work?
Different cities have different complexities and connectivity of drainage systems. It is hard to quantify the extent of connectivity and effectiveness of these systems. However, land reclamation is one factor that is increasingly making cities prone to flooding. This is particularly what we study. History of EO land reclamation, forest cover, and water level data in flood-affected regions can help forecast the likelihood of areas being prone to flood based on their increasing land cover patterns.
The steps to draw correlations with land cover and forecast floods include the following:
Additionally, a growing population is increasing the demand for forest and water resources. As a future step, we will correlate population growth with land reclamation and deforestation. This will help policymakers drift to alternative sources to fulfill demands.
Insights
We correlate the deforestation and land cover patterns along with the history of flooding in 5 Indian cities and infer the following:
Impact
Securing lives and property during floods is a temporary solution. In many cases, particularly in developing countries, even this basic management fails. Prevention of floods is the need of the hour. Reckless infrastructural activities by humans and poor policy reforms, coupled with poor drainage management by developing authorities are leading issues to address. Our model helps quantify the need for these reforms and qualitatively suggests region-specific steps for flood prevention. From a wider perspective, population growth can be viewed as one root cause of increased settlements and deforestation. Governments can use our results to understand the demographics and demand in their countries, and thus initiate policies to promote the optimal spread of the population (by developing states uniformly thus preventing biased attraction for migration to particular regions)
Coastal cities are unmaking themselves and eroding their resilience to perfectly normal monsoon weather events and virtually every one of the flood-hit areas can be linked to ill-planned construction and reckless reclamation of substantial portions of river banks and coasts drastically reduces the flood-carrying capacity of rivers and natural drainage systems.
Major disasters were not just avoidable; they were a direct consequence of decisions pushed for by vested interest conceded by town planners, bureaucrats and politicians in the face of wiser counsel.
After doing a literature survey of papers and open source projects that use remote sensing data to detect natural phenomena and also map land cover over spatiotemporal domain, we developed this framework to leverage these sources for a model that could query relevant datasets and generate time-series visualizations of select urban areas.
Sentinel-1 data is used to obtain level 1 ground range detected (GRD) products, Sentinel-2 is used to obtain high-resolution optical images. Only blue, green, and red bands are used out of the 12 band information provided by Sentinel-2. Sentinel-1 SAR images are used to get the land cover backscatter classifications with the help of Sentinel-2 optical images using a supervised classifier. Pixel-based change detection is used for detecting variations in water levels prior to, during, and post-flooding. Backscatter characteristics and variation rules of different ground objects are fundamental for flood analysis. Using both Sentinel-1 and Sentinel-2 was found to be useful for rapid flood mapping and flood estimation in open areas and also under vegetation. Sentinel-2 and Landsat 8 are used to get an estimate of increasing human settlement and deforestation for necessary geolocation. We use rainfall data from GPM and TRMM mission
6-slide presentation of our solution can be found here.