论文信息 - Inflow Forecasting for Real-Time Reservoir Operation Using Artificial Neural Network

Inflow Forecasting for Real-Time Reservoir Operation Using Artificial Neural Network

Artificial neural network (ANN) is used for inflow forecasting of reservoir up to the next 12 hours. Numerical weather forecasting information (RDAPS), recorded rainfall data, water level of upstream dam and stream gauge site, and inflow of the current time are employed as input layer’s training values, and target value is +3, +6, +9, and +12 hours later inflow to Hwacheon reservoir in South Korea. Comparison result between ANN with RDAPS and without RDAPS shows that RDAPS information is useful for forecasting inflow of reservoir.

Taesoon Kim | Gian Choi | Jun Haeng Heo

[1] Momcilo Markus,et al. PRECIPITATION-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS AND CONCEPTUAL MODELS , 2000 .

[2] Yiping Guo,et al. Climate Model Simulation of Point Rainfall Frequency Characteristics , 2006 .

[3] Jy-Shing Wu,et al. Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows , 2005 .

[4] Dimitri P. Solomatine,et al. River flow forecasting using artificial neural networks , 2001 .

[5] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .

[6] John W. Labadie,et al. Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .