USE OF ARTIFICIAL NEURAL NETWORKS IN A STREAMFLOW PREDICTION SYSTEM

BERNARD B. HSIEH CPT CHARLES L. BARTOS

USAE R&D CENTER USAE R&D CENTER

WATERWAYS EXP. STATION WATERWAYS EXP. STATION

VICKSBURG, MS 39180 USA VICKSBURG, MS 39180 USA

BIN ZHANG

SCHOOL OF CIVIL ENG.

PURDUE UNIVERSITY

WEST LAFAYETTE, IN 47907 USA

ABSTRACT

A streamflow prediction system is developed by the Artificial Neural Networks (ANN) for addressing the flood forecasting issues of two different scale watersheds, the Sav River, Croatia and a segment of the lower Mississippi River. The study investigated the prediction system with single-point river stage, upstream-downstream riverflow forecasting, and rainfall-runoff hydrological process. The study indicated that the minimum length of riverstages required achieving about 90 percent of up to 3 days forecasting reliability was about 3 months for the Sava River. The reasonable downstream riverflow prediction from upper stream gauges was found in the Sava River even only half year daily values were available for model training. On the Mississippi River, with 16 years long-term daily information, the ANN can construct a very high precision riverflow forecasting system for Memphis, TN, from two upstream inputs, near the confluence of the Ohio River, without significant rainfall contribution in this river segment.

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