Prediction of lake inflows with neural networks

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 (see Trimble et al, 1998). While the resulting model was able to capture some regularities of the measured inflow, it was far from being a useful predictive model. We continue the lake inflow modeling effort by examining data representation, quadratic input transformations, and time-delay neural networks