Natural Hazards

, Volume 91, Issue 3, pp 1317–1339 | Cite as

Applying fuzzy logic to open data for sustainable development decision-making: a case study of the planned city Amaravati

  • Ian Avery Bick
  • Ronita Bardhan
  • Terry Beaubois
Original Paper


The Indian State of Andhra Pradesh is in the process of designing and constructing a planned capital city on the southern banks of the Krishna River at Amaravati. This region will see a significant increase in urban land cover and impervious surface area (ISA) under the 2050 draft perspective plan from the Andhra Pradesh Capital Region Development Authority. As the city central zone sits on the former floodplain of the Krishna River and is subject to concentrated rainfall during monsoon seasons—this increase in ISA risks increasing flood risk through preventing infiltration of storm water and causing increased peak storm water flow (NRSC 2014). The State has announced plans for a “zero-flooding city” through implementation of technologies including green roofs, porous pavement, and detention ponds (ADC 2017). This study aims to facilitate these efforts through mapping of present and future land usage, regional flood risk, and environmental services utilizing open-source data in order to maximize efficiency of installed green infrastructure and minimize future flood damages. A map of relative soil infiltration capacity was created through fuzzy overlay of sand percentage, clay percentage, and bulk density at several depths. Relative flood risk maps for both present-day land cover and a 2050 scenario were developed using several factors: elevation, flow accumulation, surface runoff, and soil properties. A novel Relative Environmental Services Provided Index is proposed here to in order to encourage cost-effective and ecologically sound development through composite visualization of carbon storage, greenery, runoff coefficients, and soil flood prevention.


Sustainable development Amravati, India Environmental services Flood risk Climate change Impermeable surface area Landsat 8 OLI Fuzzy logic 



This research was performed as part of the graduate course CEE 224A: Sustainable Development Studio, Stanford Sustainable Urban Systems Initiative—and was later continued as an independent study. We extend our gratitude to David Medeiros at the Stanford Geospatial Center and Dr. Evan Lyons from the Stanford Spatial Analysis Center for facilitating with the data sources and image processing.

Supplementary material

11069_2018_3186_MOESM1_ESM.pdf (3.2 mb)
Supplementary material 1 (PDF 3277 kb)


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Ian Avery Bick
    • 1
  • Ronita Bardhan
    • 1
    • 2
  • Terry Beaubois
    • 1
  1. 1.Civil and Environmental Engineering DepartmentStanford UniversityStanfordUSA
  2. 2.Centre for Urban Science and EngineeringIndian Institute of Technology BombayMumbaiIndia

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