Paradigm for Managing Urban Drainage Leveraging Geospatial and Crowd Sourcing Techniques

Flooding and waterlogging are common occurrence in our cities during the rainy season, and expose the inefficiencies and vulnerabilities of our existing conveyance systems. However, such instances can be effectively managed if the concerned authorities and decision makers are a priori armed with relevant data and different scenarios. With rapid advancements in technology, it is becoming increasingly easy not just to scientifically predict the probable flooding situations but also to communicate this information timely to the relevant officials in effortlessly comprehendible form. The present study encompasses integration of geospatial data, numerical models, monitoring applications, and free and open-source GIS servers for management of urban drainage systems in the NCT of Delhi, India. Firstly, Storm Water Management Model has been setup to simulate the inundation depths, volume, duration and locations for gamut of current and future conditions using high resolution spatio-temporal data. These outputs can then be analyzed by the civic engineers on the base maps in the developed information dissemination system to issue flood advisories, if required.

A Web-GIS portal has also been built for civic bodies to monitor the health of the stormwater infrastructure using geotagged images of the drainage related issues uploaded by the residents to the complaint server using the specifically developed App or this web portal. This system can be of great help not only for the citizens to lodge a complaint against poor maintenance of the drainage system but also for authorities to look into the crowdsourced complaints pertaining to the drains under their jurisdiction.

Such e-governance framework can result in significant operational efficiency and better preparedness during floods. Thus, it can be said that urban drainage systems can be commendably monitored and managed by leveraging state-of-the-art yet easy-to-use geospatial and simulation modelling techniques.