Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

This article investigates the negative effect of land use and climate changes on water resources by the SWAT and SWAT-DEEP set minus LMSFO model. Due to the importance of runoff impact on water resources in this article, the hybrid hydrological-deep neural networks optimized by the improved SFO based on logistic map (LMSFO) algorithm have been used to provide more accurate results for runoff estimation. This method improves runoff simulation. Firstly, runoff under the influence of land use and climate change is estimated by the SWAT model. Once again, runoff is estimated by the SWAT-DEEP/LMSFO model. In the DEEP/LMSFO model, the primary runoff has been estimated by an un-calibrated SWAT model. Then the Primary runoff simulated is entered as an input into the DEEP model. Finally, the runoff is estimated by a test-training method. The results of the SWAT model and the DEEP/LMSFO model show that there is an inverse correlation between land use so that reducing land cover increases runoff. The results show that climate change and land-use change can affect annual runoff changes in the coming years.