Abstract
This study illustrates the benefits of data pre-processing through supervised data-mining techniques and utilizing those processed data in an artificial neural networks (ANNs) for streamflow prediction. Two major categories of physical parameters such as snowpack data and time-dependent trend indices were utilized as predictors of streamflow values. Correlation analysis of different models indicate that, for the period of January to June, using fewer predictors led to simpler modeling with equivalent accuracy on daily prediction models. This did not hold in all periods. For monthly prediction models, accuracy was improved compared to earlier works done to predict monthly streamflow for the same case of Elephant Butte Reservoir (EB), NM. Overall, superior prediction performance was achieved by utilizing data-mining techniques for pre-processing historical data, extracting the most effective predictors, correlation analysis, extracting and utilizing combined climate variability indices, physical indices, and employing several developed ANNs for different prediction periods of the year.
Highlights
Elephant Butte Reservoir, a multi-objective reservoir, provides electrical power and water for south-central New Mexico and West Texas, including irrigation water for 68,708.25 ha (169, 650 acres) of farmland
2, and 3, for the developed comprehensive daily streamflow prediction model applicable throughout the year (12 months), the effective snow water equivalent (SWE) indices were utilized as a predictor along with other effective predictors
For the months of July, November and December, the SWE indices are zero, but according to the correlation analysis results in table 2, the SWE amount in May has a significant correlation with the streamflow values in July, November, and December
Summary
Elephant Butte Reservoir, a multi-objective reservoir, provides electrical power and water for south-central New Mexico and West Texas, including irrigation water for 68,708.25 ha (169, 650 acres) of farmland. The initial operation plans based on optimization are defined for seven days ahead, 15 days ahead, and one month ahead, based on the predicted values for streamflow to the Elephant Butte Reservoir and related parameters of the control volume. These parameters include evaporation volumes from both Elephant Butte and Caballo Reservoir; tributary streamflows to the reservoirs; and seepage volume from both reservoirs. The developed prediction models are incorporated in a decision support system that uses the predicted values for 7 days ahead, 15 days ahead, and one month ahead to provide the optimal release plans from Elephant Butte Reservoir into the Caballo Reservoir.
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