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

Abstract

This article investigates the negative effect of land use and climate changes on water resources by the SWAT and SWAT-DEEP\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.

Distribution feature of heavy metals in the system of sediment-submerged plant-water in Xuanwu Lake

“…Heavy metals are highly toxic in small amount and one of the most hazardous substances in aquatic ecosystem with the features of the high toxicity, stability, concealment and non-degradability (Liu et al 2020c;Chen et al 2022). Studies have shown that aquatic plants are sinks of heavy metals in water ecosystem (Xing et al 2013;Wang et al 2022a). The accumulation ability of heavy metals in different aquatic plants showed: Submerged plants .…”

How Climate Literacy and Public Opinion Are the Driving Forces Behind Climate-Based Policy: A Student Perspective on COP27

“…The feasibility of these approaches hinges on a substantial amount of technological development and capital investment in current and emergent technologies . Global climate finance flows are currently around $650 billion per year, but in order to achieve the 2030 goals from the Paris Agreement (50% decrease in GHG emissions), annual flows will need to reach $4.5–5 trillion. , Consequently, it is important to evaluate the likelihood and magnitude of each individual tactic and assess its potential impact on a sector-by-sector basis. , Analytical tools for detailed climate prediction models including life cycle analysis, , emissions trackers, − and soil and water assessments , are currently being utilized in an enormous amount of peer-reviewed literature. It is noteworthy to mention that the impact of mitigation strategies can be significant but are not all positive.…”

The Intensity of Agriculture in the Covid-19 from Indonesia – A Systematic Literature Review

“…Germination uniformity was calculated based on the time required for 90% germination subtracted by the time required for 10% germination, namely D90-D10, in which scores with a lower value indicate a shorter time interval between 10% and 90% of seed germination and more favorable germination uniformity [34]. Some researchers also stated that the lower absolute value for the obtained values indicated more uniformity for the germinated seeds [35][36][37][38][39][40][41][42][43][44]. Accordingly, since these seeds absorb water more rapidly under the same conditions, their germination rate and D10 occur higher and lower values, respectively.…”