| dc.description.abstract | The hydric resources management demands the use of tools that represent the hydrologic processes in a clear and objective way to assist the understanding and using of them. Considering this necessity, computer models that systemize complex problems in a simple way have been developed. Among these models is the Artificial Neural Network technique, a methodology inspired on human nervous system which has the ability to learn and generalize, making possible to solve complex problems. In this work is studied the application of Artificial Neural Networks of Perceptron Multilayer type, based on backpropagation learning algorithmic to estimate the thickness of Serra Geral formation, the static level and specific capacity, based on information extracted from well cadastre of Groundwater Information System for the Hydrographic Basin of Cai River in Rio Grande do Sul state. Through the Student test (test T), with a significance level of 5%, statistically, the models proposed for the estimates of the thickness of Serra Geral Formation, the level static and specific capacity did not differ from taken as true. Also, through linear regression there has been through the coefficient R2 a strong correlation between variables simulated and the real. The results demonstrate that the developed models through the Artificial Neural Networks present good results on prevision of hydrogeologic parameters, which could be used as a base to elaborate thematic maps. In the same way, they suggest the use of alternative data for conventional modeling aiming at processes optimization for groundwater exploration. | |
