Application of Data Mining Tools for Long-Term Quantitative and Qualitative Prediction of Streamflow
Publication: Journal of Irrigation and Drainage Engineering
Volume 142, Issue 12
Abstract
This paper evaluates the performances of two long-term prediction approaches for streamflow and riverine total dissolve solids (TDS) and compares their results with observed data and with short-term predicted values. The future values predicted by the first, long-term, prediction approach (Approach 1) depend on data corresponding to time steps prior to the prediction time step. The future values predicted by the second, long-term, prediction approach (Approach 2) depend on data comprised within the observational period. Each long-term prediction approach calculates streamflow and TDS over a 12-month period ranging from April through March (Scheme 1) and by agricultural water year (December through November, Scheme 2). Genetic programming (GP) is implemented for long-term prediction. Prediction is applied to the streamflow and TDS of the Karoon River in southwestern Iran. The long-term Approach 1 was found to be more accurate than the long-term Approach 2 judged by the values of several diagnostic statistics. The root mean square error (RMSE), correlation coefficient (), and Nash-Sutcliffe efficiency () statistics of long-term predictions of streamflow and TDS with Approach 1 are lower than those obtained with the long-term prediction Approach 2 for April–March and for the agricultural water-year predictions. It is concluded that prediction of the Karoon River’s streamflow and TDS is best accomplished using GP in combination with the long-term prediction Approach 1.
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Journal of Irrigation and Drainage Engineering
Volume 142 • Issue 12 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Jan 28, 2016
Accepted: May 10, 2016
Published online: Jul 18, 2016
Published in print: Dec 1, 2016
Discussion open until: Dec 18, 2016
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