Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resour...

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Bibliographic Details
Published in:Journal of the American Water Resources Association 2018-02, Vol.54 (1), p.55-68
Main Authors: Petty, T.R., Dhingra, P.
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
computational methods
Correlation
Data
Data management
Emergency medical services
Environmental risk
Evacuation
first responders
Flood forecasting
flooding
Floods
Hydrologic data
Hydrology
Learning algorithms
Machine learning
Mitigation
Model accuracy
Model testing
Multiple regression analysis
Regression analysis
River discharge
rivers/streams
statistics
Stream discharge
Stream flow
streamflow hydrology estimates
surface water hydrology
Training
Watersheds
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Summary:Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.
ISSN:1093-474X
1752-1688
DOI:10.1111/1752-1688.12555