Machine learning to evaluate impacts of flood protection in Bangladesh, 1983–2014

Authors: Achut Manandhar, Alex Fischer, David J. Bradley, Mashfiqus Salehin, M. Sirajul Islam, Rob Hope and David A. Clifton

Impacts of climate change adaptation strategies need to be evaluated using principled methods spanning sectors and longer time frames. The authors propose machine-learning approaches to study the long-term impacts of flood protection in Bangladesh. Available data include socio-economic survey and events data (death, migration, etc.) from 1983–2014. These multidecadal data, rare in their extent and quality, provide a basis for using machine-learning approaches even though the data were not collected or designed to assess the impact of the flood control investments. This research tests whether the embankment has affected the welfare of people over time, benefiting those living inside more than those living outside.

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Kelly Ann Naylor, Associate Director, Water, Sanitation and Hygiene (WASH) Section, Programme Division, UNICEF

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