High-Quality Historical Flood Data Reconstruction in Bangladesh Using Hidden Markov Models

Mauerman, Max; Tellman, Elizabeth; Lall, U.; Tedesco, Marco; Colosio, Paolo; Thomas, Mitchell; Osgood, Daniel E.; Bhuyan, Arifuzzaman

doi:10.1007/978-3-030-95722-3_10

Cited by 1 publication

(1 citation statement)

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“…This work's specific empirical application -urban flood detection -is complementary to the substantial machine learning work on flood prediction, using precipitation and seasonal climate information and data from satellites or sensors (see Mosavi, Ozturk, and Chau (2018) for a comprehensive review). Mauerman et al (2022), for example, use a Bayesian latent variable model to predict seasonal floods in Bangladesh through the reconstruction of historical satellite data. Agonafir et al (2022) study infrastructure correlates of flooding using 311 reports in NYC.…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing

Agostini,

Pierson,

Garg

2024

AAAI

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Decision-makers often observe the occurrence of events through a reporting process. City governments, for example, rely on resident reports to find and then resolve urban infrastructural problems such as fallen street trees, flooded basements, or rat infestations. Without additional assumptions, there is no way to distinguish events that occur but are not reported from events that truly did not occur--a fundamental problem in settings with positive-unlabeled data. Because disparities in reporting rates correlate with resident demographics, addressing incidents only on the basis of reports leads to systematic neglect in neighborhoods that are less likely to report events. We show how to overcome this challenge by leveraging the fact that events are spatially correlated. Our framework uses a Bayesian spatial latent variable model to infer event occurrence probabilities and applies it to storm-induced flooding reports in New York City, further pooling results across multiple storms. We show that a model accounting for under-reporting and spatial correlation predicts future reports more accurately than other models, and further induces a more equitable set of inspections: its allocations better reflect the population and provide equitable service to non-white, less traditionally educated, and lower-income residents. This finding reflects heterogeneous reporting behavior learned by the model: reporting rates are higher in Census tracts with higher populations, proportions of white residents, and proportions of owner-occupied households. Our work lays the groundwork for more equitable proactive government services, even with disparate reporting behavior.

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Section: Related Workmentioning

confidence: 99%