2021
DOI: 10.1155/2021/2201964
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Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data

Abstract: Starting from the main eco-environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco-environmental water consumption. In order to study the eco-environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in chang… Show more

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Cited by 3 publications
(2 citation statements)
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“…This approach normally relies on a large number of data samples to discover hidden relationships. Most studies dealing with the assessment of the ecological state of a river basin using ML focus on dissolved oxygen in surface water [50]. Furthermore, ML is often used in the context of the prediction of biological parameters based on chemical variables [51].…”
Section: Discussionmentioning
confidence: 99%
“…This approach normally relies on a large number of data samples to discover hidden relationships. Most studies dealing with the assessment of the ecological state of a river basin using ML focus on dissolved oxygen in surface water [50]. Furthermore, ML is often used in the context of the prediction of biological parameters based on chemical variables [51].…”
Section: Discussionmentioning
confidence: 99%
“…The neural network itself is a nonlinear dynamic system with strong self-learning, self-adapting, self-organizing and fault-tolerant capabilities. For the dispersion curve and the geoacoustic parameters, the relationship between them is a clear dispersion function relationship, but this relationship is implicit, and it is difficult to calculate through other optimizations, and the amount of calculation is huge [7] [8]. Back propagation (BP)neural networks can be used for function approximation.…”
Section: Introductionmentioning
confidence: 99%