Predicting effects of climate fluctuations for water management by applying neural network
- South Florida Water Management District, West Palm Beach, FL (United States)
This paper investigates the applicability of neural networks in climate based forecasting for regional water resources management. A neural network is a computational method inspired by studies of the brain and nerve systems in biological organisms. Neural networks represent highly idealized mathematical models of the authors present understanding of such complex systems. Typically, a neural network consists of a set of layered processing units and weighted interconnections between the units. There exists a variety of neural network models and learning procedures. This paper applies the most widely used Back Propagation model to the climate forecasting. While the architecture of the Back Propagation network is fairly established, the process of determining the best suitable network configuration and the best parameters for a given application is trial-and-error, especially when the relationships between the variables are not well understood. On the other hand, this trial-and-error process can be used to help reveal the underlining relationships between variables. In this study, issues such as selecting a best fit neural network configuration, deploying a proper training algorithm, and preprocessing input data are addressed. The effects of various global oceanic and atmospheric variables to the regional water resources are also discussed. The study is focused on the prediction of inflow to Lake Okeechobee, the liquid heart for south Florida. Several global weather parameters over the past several decades are used as input data for training and testing. Different combinations of the variables are explored.
- OSTI ID:
- 379664
- Journal Information:
- World Resource Review, Vol. 8, Issue 3; Other Information: PBD: Sep 1996
- Country of Publication:
- United States
- Language:
- English
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