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Machine learning-based seawater concentration pathway prediction
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AI
DOI：10.1016/j.compeleceng.2021.107336.png)
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Abstract
En 中文
This paper constructs a prediction model based on Multilayer Perceptron (MLP) to explore the formation mechanism of brine (seawater evaporation or freezing). Four brine sets are extracted from the published, real-world data, and the simulation test with the same six chemical substance features. After integrated comparative experiments on six evaluation metrics, the results show that this model outperforms the other baseline prediction algorithms, achieving the highest precision 0.9625 and at least 8.45% improvement. Furthermore, for predicting the real-world test set, the results confirm the existence of freezing brine for the first time in Laizhou Bay area, China. This model is also used to analyze the mixed simulation results for brine and fresh groundwater. The experimental results indicate that only two out of twenty-nine samples of various concentrations change formation mechanisms after mixing. Overall, the model can effectively distinguish the evaporation and freezing brine, and discover the seawater concentration pathway.
Keywords:
Machine learning model
Multilayer Perceptron
Brine formation analysis
Underground brine
Seawater concentration pathway
Journal
C
IF:
4.9
Papers:
6.7K
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