Machine learning-based seawater concentration pathway prediction

Hu, Fang; Xu, Xingyong; Liang, Jun; Yang, Changguo; Huang, Mingfang; Su, Qiao

doi:10.1016/j.compeleceng.2021.107336

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…It is still debatable how the brine in Laizhou Bay was generated. The majority of researchers came to the conclusion that the brine in Laizhou Bay is the result of seawater evaporation, while some studies suggested that frozen brine existed [49]. What is certain is that different concentration paths of seawater can affect the properties of brine [50], and the assemblages of minerals that form, the order in which they precipitate, and their relative abundances are all determined by the relative composition of ionic species in the original brine [51].…”

Section: Discussionmentioning

confidence: 99%

Using Principal Component Analysis (PCA) Combined with Multivariate Change-Point Analysis to Identify Brine Layers Based on the Geochemistry of the Core Sediment

Su,

Yu,

et al. 2023

Water

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The underground brine in Southern Laizhou Bay is characterized by its large scale and high concentration, which can affect the distribution and migration of geochemical elements in sediments. Most studies on the brine are based on hydrochemical analysis, with little consideration being given from a geochemical perspective. Principal component analysis (PCA) is a powerful tool for discovering relationships among many elements and grouping samples in large geochemical datasets. However, even after reducing the dimensions through PCA, researchers still need to make judgments about the meaning represented by each principal component. Change-point analysis can effectively identify the points at which the statistical properties change in a dataset. PCA and change-point analysis have their respective advantages in the study of large sets of geochemical data. Based on the geochemical data of the LZ908 core, by combining these two methods, this study identified four elements (U, MgO, Br, and Na2O) related to the action of seawater through PCA; then, multivariate change point analysis was conducted on these elements to detect the depths of different brine layers. The results of the analysis are basically consistent with those of other studies based on the water content, salinity, and other data, thus proving the effectiveness of this method. The combination of these two methods may also lead to novel approaches for related research.

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Section: Discussionmentioning

confidence: 99%

Using Principal Component Analysis (PCA) Combined with Multivariate Change-Point Analysis to Identify Brine Layers Based on the Geochemistry of the Core Sediment

Su,

Yu,

et al. 2023

Water

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“…It is accepted that these techniques bring a significant impact and new research frameworks in industries such as finance, medicine, manufacturing, and various government, public sector, and business domains. For example, the machine learning methodology is employed in the prediction of crime [24], future price of agricultural products [25], natural gas consumption [26], commercial banks performance [27], landslide displacement [28], and seawater evaporation [29]. As may be seen, prediction algorithms have been extensively investigated in several sectors.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Machine Learning Model for Electricity Consumption Prediction Using Random Forest and Artificial Neural Networks

Kesornsit

Sirisathitkul

2022

Applied Computational Intelligence and Soft Computing

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Predicting electricity consumption is notably essential to provide a better management decision and company strategy. This study presents a hybrid machine learning model by integrating dimensionality reduction and feature selection algorithms with a backpropagation neural network (BPNN) to predict electricity consumption in Thailand. The predictive models are developed and tested using an actual dataset with related predictor variables from public sources. An open geospatial data gathered from a real service as well as geographical, climatic, industrial, household information are used to train, evaluate, and validate these models. Machine learning methods such as principal component analysis (PCA), stepwise regression (SWR), and random forest (RF) are used to determine the significant predictor variables. The predictive models are constructed using the BPNN with all available variables as baseline for comparison and selected variables from dimensionality reduction and feature selection methods. Along with creating a predictive model, the most related predictors of energy consumption are also selected. From the comparison, the hybrid model of RF with BPNN consistently outperforms the other models. Thus, the proposed hybrid machine learning model presented from this study can predict electricity consumption for planning and managing the energy demand.

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“…In most cases, subsurface brines in coastal areas are produced by mixing processes, with mixing including fresh groundwater, seawater, and atmospheric precipitation [7]. The composition of brines can also be altered by reacting with minerals in the surrounding rocks, such as dolomitization, sulfate reduction, plagioclase sodium feldsparization, and potassium/aluminum silicate formation [8].…”

Section: Introductionmentioning

confidence: 99%

“…Han et al concluded that the Quaternary Ice Age climate in the Laizhou Bay area was cold and that the seawater had conditions for freezing and concentration [20]. Hu et al constructed a multilayer perceptron (MLP) seawater concentration pathway prediction model based on seawater freezing and evaporation experimental data, and the results of their study indicate the presence of freezing-induced subsurface brines in the Laizhou Bay area [7].…”

Section: Introductionmentioning

confidence: 99%

“…The gradual depletion of underground brine resources due to long-term high-intensity mining in the Laizhou Bay area has seriously threatened the sustainable development of the local economy. The study of seawater concentration pathways in Laizhou Bay, especially the existence of seawater freezing pathways, is of great significance for expanding local brine resource reserves and maintaining sustainable mining and utilization [7]. The purpose of this research paper is to focus on the influence of the mixing of different ratios of freshwater and seawater on the methods of determining the genesis of subsurface brines by using hydrogeochemical simulation methods based on the comparison of the hydrochemical characteristics and ionic ratios of subsurface brines, subsurface freshwater, and seawater.…”

Section: Introductionmentioning

confidence: 99%

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The Study on the Genesis of Underground Brine in Laizhou Bay Based on Hydrochemical Data

Chen,

Yu,

et al. 2023

Water

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The Laizhou Bay area contains a large amount of Quaternary brine resources, which have been gradually depleted by long-term high-intensity mining. The local brine genesis is still controversial, and the dominant theory of the evaporation of brine formation limits the exploration of brine resources to the land area, while the confirmation of freezing brine formation would greatly expand the brine storage range. In this research paper, the genesis of underground brines was analyzed based on the major ion concentrations of 62 samples of underground brines at different depths at 24 stations along the south coast of Laizhou Bay. The results show that the underground brines originated from seawater; however, their chemical components were changed during the evolution process after formation. The hydrogeochemical modeling results show that the mixing of seawater and fresh groundwater also affects the chemical composition of brines. The large-scale exploitation of brine resources in recent decades has also led to significant changes in the chemical composition of the underground brine in Laizhou Bay compared with the earlier period. The special geographic environment and the development of the brine chemical industry have led to the evolution of underground brines in Laizhou Bay being affected by a variety of factors, which makes the traditional analysis of brine genesis in this region not applicable. Furthermore, although evaporation and concentration are the dominant factors in the formation of brine, there is still a lack of evidence to rule out the existence of the freezing pathway.

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Machine learning-based seawater concentration pathway prediction

Cited by 3 publications

References 19 publications

Using Principal Component Analysis (PCA) Combined with Multivariate Change-Point Analysis to Identify Brine Layers Based on the Geochemistry of the Core Sediment

Using Principal Component Analysis (PCA) Combined with Multivariate Change-Point Analysis to Identify Brine Layers Based on the Geochemistry of the Core Sediment

Hybrid Machine Learning Model for Electricity Consumption Prediction Using Random Forest and Artificial Neural Networks

The Study on the Genesis of Underground Brine in Laizhou Bay Based on Hydrochemical Data

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