Climate changes, as well as land cover changes, affect the flow regimes in streams. Understanding the contributions of climate variables and land cover changes on low flows will help planners and decision makers to improve water resources management. An approach which uses data driven artificial neural networks (ANNs) is proposed in this study. Land cover, rainfall, snow and temperature were used as inputs to the ANN model. In this approach, an index called relative strength effect was used to assess the contribution of each input used in ANN. The proposed approach was experimented in three contrasting watersheds in northwest Indiana, USA. The study indicates that the changes in low flow regime for a less urbanized watershed were explained by land cover changes up to 30% while the remaining 70% variations were explained by meteorological inputs. In the watershed with a more developed area, the low flow variations were influenced up to 80% by meteorological inputs.