This paper addresses the problem of integrating the effects of climate history and solar variability, to enhance regional hydrologic forecasting using neural networks. A previous attempt at modeling the inflow to Lake Okeechobee employed a multilayered perceptron. While the resulting model was able to capture some regularities of the measured inflow, it was far from being a useful predictive model. In this paper, we continue the lake inflow modeling effort by examining data representation, quadratic input transformations, and time-delay neural networks.
|Number of pages||5|
|Journal||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|State||Published - 2000|
|Event||2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA|
Duration: Oct 8 2000 → Oct 11 2000