9524758 Govindaraju This proposal deals with the development of a geomorphological artificial neural network (GANN) model for predicting the nonlinear rainfall-runoff relationships of watersheds. GANN represents a massively interconnected network of processing units called neurons. The network is characterized by weights, which determine the strength of each connection, and the threshold values of neurons which dictate their activation level. The network of overland flow planes, streams and subsurface flow pathways, which drains the water from the watershed, will be mapped into the topology of artificial neural networks (ANNs). This model will draw on strengths of the geomorphological representations of the watershed, and will also be capable of capturing the nonlinear response of watersheds to precipitation events. GANN models will be further utilized to study the influence of temporal and spatial scales, and to investigate the significance of various surface and subsurface flow pathways. The performance of GANN models will be tested in several ways. Four watersheds in Kansas have been identified based on size and availability of data for rainfall, soils, temperature, topography, and other variables which govern water movement within the watershed. The network will be trained using a portion of the data. Simultaneously, a physically-based model and a regression-based empirical model will be calibrated using the same historical record. These models will then be tested in prediction mode through the remaining portion of the rainfall-stream flow records. This will not only help evaluate the GANN model in terms of how well it predicts stream flow given rainfall, solar radiation, and temperature records, but will also form a basis for comparing its performance with respect to other types of models. ***
