基于地下水建模系统的地下水流量计算模型及神经网络预测

Q3 Engineering
Qiu Bo, W. Cheng, T. Sun
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引用次数: 0

摘要

地下水因其水质好、无污染、涉及面广等优点,已成为中国工业、农业和城市生活用水的重要来源。地下水水位和流量的变化趋势和规律是科学规划和合理管理地下水的重要依据。为了获得更精确的三维地下水流动模型,首先,基于地下水模拟系统(GMS)构建了地下水流动计算(GFC)模型,分析了地下水周期性补给、三维流动空间、复杂流线组合和并行流动方向;然后,对计算得到的地下水流量参数数据进行预处理,构建灰度嵌入BP神经网络进行地下水流量预测。最后,用实验结果验证了模型的有效性。该研究为其他类似领域的流量预测和地下水评价研究提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Groundwater Flow Calculation Model and Neural Network Prediction Based on Groundwater Modeling System
Received: 1 July 2020 Accepted: 12 September 2020 Due to its merits of good water quality, pollution-free, and wide range, groundwater has become an important source for the water supply of industry, agriculture and urban domestic living in China. The change trends and laws of groundwater level and flow are important basis for the scientific planning and reasonable management of groundwater. In order to obtain a more accurate 3D groundwater flow model, first, this paper constructed a Groundwater Flow Calculation (GFC) model based on the Groundwater Modeling System (GMS), and analyzed the periodic groundwater recharge, the tridimensional flow space, the complex streamline combination, and the concurrent flow direction; then, the paper gave the preprocessing process of the calculated parameter data of groundwater flow, and built a grayscale-embedded BP neural network to predict the groundwater flow. At last, the paper verified the effectiveness of the model with experimental results. This study provided a reference for the flow prediction of other similar fields and the research on groundwater evaluation.
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来源期刊
Instrumentation Mesure Metrologie
Instrumentation Mesure Metrologie Engineering-Engineering (miscellaneous)
CiteScore
1.70
自引率
0.00%
发文量
25
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