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Using "big data" to optimally model hydrology and water quality across expansive regions

By:
, , and
DOI: 10.1061/41036(342)653

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Abstract

This paper describes a new divide and conquer approach that leverages big environmental data, utilizing all available categorical and time-series data without subjectivity, to empirically model hydrologic and water-quality behaviors across expansive regions. The approach decomposes large, intractable problems into smaller ones that are optimally solved; decomposes complex signals into behavioral components that are easier to model with "sub- models"; and employs a sequence of numerically optimizing algorithms that include time-series clustering, nonlinear, multivariate sensitivity analysis and predictive modeling using multi-layer perceptron artificial neural networks, and classification for selecting the best sub-models to make predictions at new sites. This approach has many advantages over traditional modeling approaches, including being faster and less expensive, more comprehensive in its use of available data, and more accurate in representing a system's physical processes. This paper describes the application of the approach to model groundwater levels in Florida, stream temperatures across Western Oregon and Wisconsin, and water depths in the Florida Everglades. ?? 2009 ASCE.

Additional Publication Details

Publication type:
Conference Paper
Publication Subtype:
Conference Paper
Title:
Using "big data" to optimally model hydrology and water quality across expansive regions
ISBN:
9780784410363
DOI:
10.1061/41036(342)653
Volume
342
Year Published:
2009
Language:
English
Larger Work Title:
Proceedings of World Environmental and Water Resources Congress 2009 - World Environmental and Water Resources Congress 2009: Great Rivers
First page:
6446
Last page:
6460
Conference Title:
World Environmental and Water Resources Congress 2009: Great Rivers
Conference Location:
Kansas City, MO
Conference Date:
17 May 2009 through 21 May 2009