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Open AccessArticle

Machine Learning to Evaluate Impacts of Flood Protection in Bangladesh, 1983–2014

1
Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
2
School of Geography and the Environment & Smith School of Enterprise and the Environment, University of Oxford, Oxford OX1 3QY, UK
3
Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
4
London School of Hygiene and Tropical Medicine, University of London, London WC1E 7HT, UK
5
Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh
6
Laboratory Sciences and Services Division, International Centre for Diarrhoeal Disease Research, Dhaka 1000, Bangladesh
*
Author to whom correspondence should be addressed.
Water 2020, 12(2), 483; https://doi.org/10.3390/w12020483
Received: 22 December 2019 / Revised: 30 January 2020 / Accepted: 1 February 2020 / Published: 11 February 2020
(This article belongs to the Special Issue Water Security and Governance in Catchments)
Impacts of climate change adaptation strategies need to be evaluated using principled methods spanning sectors and longer time frames. We 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. We test whether the embankment has affected the welfare of people over time, benefiting those living inside more than those living outside. Machine-learning approaches enable learning patterns in data to help discriminate between two groups: here households living inside vs. outside. They also help identify the most informative indicators of discrimination and provide robust metrics to evaluate the quality of the model. Overall, we find no significant difference between inside/outside populations based on welfare, migration, or mortality indicators. However, we note a significant difference in inward/outward movement with respect to the embankment. While certain data gaps and spatial heterogeneity in sampled populations suggest caution in any conclusive interpretation of the flood protection infrastructure, we do not see higher benefits accruing to those living with higher levels of protection. This has implications for Bangladesh’s planning for future and more extreme climate futures, including the national Delta Plan, and global investments in climate resilient infrastructure to create positive social impacts. View Full-Text
Keywords: Bangladesh; climate resilience; flood protection; machine learning; socio-environmental impacts Bangladesh; climate resilience; flood protection; machine learning; socio-environmental impacts
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MDPI and ACS Style

Manandhar, A.; Fischer, A.; Bradley, D.J.; Salehin, M.; Islam, M.S.; Hope, R.; Clifton, D.A. Machine Learning to Evaluate Impacts of Flood Protection in Bangladesh, 1983–2014. Water 2020, 12, 483.

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