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Hydrogeochemical characterization and quality assessment of groundwater using self-organizing maps in the Hangjinqi gasfield area,Ordos Basin,NW China

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Geoscience Frontiers 2021年第2期12卷 781-790页

作者： Chu Wu Chen Fang Xiong Wu Ge Zhu Yuzhe Zhang Department of Water Resources China Institute of Water Resources and Hydropower ResearchBeijing 100083China School of Water Resources and Environment China University of Geosciences(Beijing)Beijing 100083China Center for Advanced Technology and Education(CATE) Florida International UniversityMiamiFL 33174USA

Water resources are scarce in arid or semiarid areas,which not only limits economic development,but also threatens the survival of mankind.The local communities around the Hangjinqi gasfield depend on groundwater sources for water supply.A clear understanding of the groundwater hydrogeochemical characteristics and the groundwater quality and its seasonal cycle is invaluable and indispensable for groundwater protection and management.In this study,self-organizing maps were used in combination with the quantization and topographic errors and K-means clustering method to investigate groundwater chemistry datasets.The Piper and Gibbs diagrams and saturation index were systematically applied to investigate the hydrogeochemical characteristics of groundwater from both rainy and dry seasons.Further,the entropy-weighted theory was used to characterize groundwater quality and assess its seasonal variability and suitability for drinking purposes.Our hydrochemical groundwater dataset,consisting of 10 parameters measured during both dry and rainy seasons,was classified into 6 clusters,and the Piper diagram revealed three hydrochemical facies:Cl-Na type(clusters 1,2 and 3),mixed type(clusters 4 and 5),and HCO3-Ca type(cluster 6).The Gibbs diagram and saturation index suggested thatweathering of rock-forming mineralswere the primary process controlling groundwater chemical composition and validated the credibility and practicality of the clustering results.Two-thirds of 45 groundwater samples were categorized as excellent-or good-quality and were suitable as drinking water.Cluster changes within the same and different clusters from the dry season to the rainy season were detected in approximately 78%of the collected samples.The main factors affecting the groundwater quality were hydrogeochemical characteristics,and dry season groundwater quality was better than rainy season groundwater quality.Based on this work,such results can be used to investigate the seasonal variation of hydrogeochemical characteristics and assess water

关键词： self-organizing maps Seasonal change Entropy-weighted theory Hydrogeochemical characteristics Groundwater quality

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self-organizing maps for identification of zeolitic diagenesis patterns in closed hydrologic systems on the Earth and its implications for Mars

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International Journal of Sediment Research 2021年第5期36卷 567-576页

作者： Gayantha Roshana Loku Kodikara Lindsay McHenry Department of Geosciences University of Wisconsin-Milwaukee

We have conducted a survey of zeolite occurrences in saline-alkaline paleolake deposits on Earth to identify the most prominent zeolite alteration patterns and to characterize the most common authigenic minerals and their paragenetic relationships. We collected the bulk mineral assemblages(from previous and our studies) as identified by X-ray diffraction from zeolitic tuff beds and associated sedimentary beds from thirteen paleolake deposits from the USA, Mexico, Greece, and Tanzania. We applied the Kohonen self-organizing maps(SOM) to look for interesting patterns in the tuff bed mineral assemblages without prescribing any specific interpretation, and for information reduction and classification. Decision Tree(DT) method was applied to characterize these clusters. We were able to define clear class boundaries between fresh glass, non-analcime zeolites, analcime, and K feldspar. The non-analcime zeolites were further grouped into several classes based on mineral type. We also discuss the potential implications for Mars, showing that the mineral assemblages of diagenetic facies identified by SOM and DT can be used to test or validate the orbital, in situ, or modeling results, while the trained SOM provides a robust generalized ability to classify the new mineral assemblage data into the most common diagenetic facies identified in saline-alkaline paleoenvironments that contain zeolites. The study concludes that generalizing the complex geochemical behaviors using unsupervised statistical learning methods can help to identify the most prominent geochemical behaviors.

关键词： Zeolites self-organizing maps Mars Paleolakes Diagenetic patterns

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Research on Cheating Detection Method of Programming Source Codes by self-organizing maps

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中国电子商情·通信市场 2013年第1期 53-58页

作者： Guojin Zhu Le Liu

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Rapid environmental assessment in the South China Sea:Improved inversion of sound speed profile using remote sensing data

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Acta Oceanologica Sinica 2022年第7期41卷 78-83页

作者： Ke Qu Binbin Zou Jianbo Zhou College of Electronics and Information Engineering Guangdong Ocean UniversityZhanjiang524088China Shanghai Acoustics Laboratory Chinese Academy of SciencesShanghai201815China School of Marine Science and Technology Northwestern Polytechnical UniversityXi’an710072China

Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment(REA)in the South China Sea(SCS).In this paper,the remote sensing data were used to estimate sound speed profile(SSP)with the self-organizing map(SOM)method in the SCS.First,the consistency of the empirical orthogonal functions was examined by using k-means clustering.The clustering results indicated that SSPs in the SCS have a similar perturbation nature,which means the inverted grid could be expanded to the entire SCS to deal with the problem of sparsity of the samples without statistical improbability.Second,a machine learning method was proposed that took advantage of the topological structure of SOM to significantly improve their accuracy.Validation revealed promising results,with a mean reconstruction error of 1.26 m/s,which is 1.16 m/s smaller than the traditional single empirical orthogonal function regression(sEOF-r)method.By violating the constraints of linear inversion,the topological structure of the SOM method showed a smaller error and better robustness in the SSP estimation.The improvements to enhance the accuracy and robustness of REA in the SCS were offered.These results suggested a potential utilization of REA in the SCS based on satellite data and provided a new approach for SSP estimation derived from sea surface data.

关键词： South China Sea sound speed profile empirical orthogonal function self-organizing maps

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Correlations among large igneous provinces related to the West Gondwana breakup:A geochemical database reappraisal of Early Cretaceous plumbing systems

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Geoscience Frontiers 2023年第1期14卷 181-206页

作者： Antomat A.Macêdo Filho Maria Helena B.M.Hollanda Stephen Fraser Alisson L.Oliveira Alanny C.C.Melo Alana R.Dantas Instituto de Geociências Universidade de São Paulo05508-080São PauloBrazil W.H.Bryan Mining&Geology Research Centre Sustainable Minerals InstituteThe University of QueenslandSt Lucia CampusQLD 4072Australia Programa de Pós-Graduação em Geodinamica e Geofisica Universidade Federal do Rio Grande do NorteCampus Universitário S/NLagoa Nova59078-970 NatalRNBrazil

The opening and spreading of the Atlantic Ocean between Africa and South America evolved during the Early Cretaceous and were preceded by dramatic tholeiitic(mafic)magmatism and minor silicic and alkaline volcanism.These features are presently recognized from the equatorial regions of Brazil and Africa to the Falklands and South Africa.In southeastern South America,continental flood basalts and related plumbing systems constitute the ParanáMagmatic Province(PMP),whose African counterpart is the Etendeka Magmatic Province(EMP).In northeastern Brazil,dike swarms and sill complexes compose the Equatorial Atlantic Magmatic Province(EQUAMP).As a common feature,these provinces are chemically represented by two prevalent magma types:(1)tholeiitic basalts and basaltic andesites with lowTi(TiO_(2)~2 wt.%)and incompatible element contents.High Ti tholeiites are relevant in the northern PMP and EMP,and dominant in EQUAMP.Evolved rocks(SiO_(2)=57-65 wt.%)interpreted as byproducts of assimilation and/or fractional crystallization(AFC)processes from high Ti tholeiitic magmas,are scarce(but present)in all three provinces.An accurate analysis of multivariable databases collected from the literature for dikes and sills,including major and trace element and radiogenic isotope data,reveals close similarities in their geochemical signatures.In a paleogeographic reconstruction of West Gondwana,the intrusive remnants of the PMP,EMP and EQUAMP are spread over an area of nearly 10×10^(6)km^(2).forming perhaps the most extensive set of plumbing systems on Earth,with a relatively consistent chronology based on a vast collection of K-Ar and^(40)Ar/^(39)Ar data available in the literature.This work provides the first comprehensive data comparison to support the exis

关键词： LIP-related plumbing systems Continental tholeiites Paraná-Etendeka EQUAMP self-organizing maps

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Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

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Computer Modeling in Engineering & Sciences 2018年第12期117卷 387-423页

作者： Marco Gotz Ferenc Leichsenring Thomas Kropp Peter Müller Tobias Falk Wolfgang Graf Michael Kaliske Welf-Guntram Drossel Institute for Structural Analysis Technische UniversitatDresdenGermany Fraunhofer Institute for Machine Tools and Forming Technology DresdenGermany

Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised.The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design.Multiple analysis methods are known and available to gain insight into existing models.In this contribution,selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process.The selection of introduced methods comprises techniques of machine learning and data mining,in which the utilization is aiming at a decreased numerical effort.The methods of choice are basically discussed and references are given as well as challenges in the context of meta-modelling and sensitivities are shown.An incremental knowledge gain is provided by a step-bystep application of the numerical methods,whereas resulting consequences for further applications are highlighted.Furthermore,a visualisation method aiming at an easy design guideline is proposed.These visual decision maps incorporate the uncertainty coming from the reduction of dimensionality and can be applied in early stage of design.

关键词： Design data mining computational intelligence meta-modelling permissible design space sensitivity analysis self-organizing maps inverse problem early stage of design clinching

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SOM approach for clustering customers using credit card transactions

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International Journal of Intelligent Computing and Cybernetics 2019年第3期12卷 372-388页

作者： Seda Yanık Abdelrahman Elmorsy Istanbul Technical University IstanbulTurkey

Purpose–The purpose of this paper is to generate customer clusters using self-organizing map(SOM)approach,a machine learning technique with a big data set of credit card consumptions.The authors aim to use the consumption patterns of the customers in a period of three months deducted from the credit card transactions,specifically the consumption categories(e.***.food,entertainment,etc.).Design/methodology/approach–The authors use a big data set of almost 40,000 credit card transactions to cluster customers.To deal with the size of the data set and the eliminated the required parametric assumptions the authors use a machine learning technique,SOMs.The variables used are grouped into three as demographical variables,categorical consumption variables and summary consumption variables.The variables are first converted to factors using principal component analysis.Then,the number of clusters is specified by k-means clustering trials.Then,clustering with SOM is conducted by only including the demographical variables and allvariables.Then,a comparisonis made and the significance of the variablesis examined by analysis of variance.Findings–The appropriate number of clusters is found to be 8 using k-means clusters.Then,the differences in categorical consumption levels are investigated between the clusters.However,they have been found to be insignificant,whereas the summary consumption variables are found to be significant between the clusters,as well as the demographical variables.Originality/value–The originality of the study is to incorporate the credit card consumption variables of customers to cluster the bank customers.The authors use a big data set and dealt with it with a machine learning technique to deduct the consumption patterns to generate the clusters.Credit card transactions generate a vast amount of data to deduce valuable information.It is mainly used to detect fraud in the literature.To the best of the authors’knowledge,consumption patterns obtained from credit card transaction are first used for clustering

关键词： PCA Clustering self-organizing maps k-means Credit card transactions

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Nonlinear Dimensionality Reduction and Data Visualization:A Review

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International Journal of Automation and computing 2007年第3期4卷 294-303页

作者： Hujun Yin School of Electrical and Electronic Engineering The University of ManchesterManchester M60 1QDUK.

Dimensionality reduction and data visualization are useful and important processes in pattern recognition. Many techniques have been developed in the recent years. The self-organizing map （SOM） can be an efficient method for this purpose. This paper reviews recent advances in this area and related approaches such as multidimensional scaling （MDS）, nonlinear PC A, principal manifolds, as well as the connections of the SOM and its recent variant, the visualization induced SOM （ViSOM）, with these approaches. The SOM is shown to produce a quantized, qualitative scaling and while the ViSOM a quantitative or metric scaling and approximates principal curve/surface. The SOM can also be regarded as a generalized MDS to relate two metric spaces by forming a topological mapping between them. The relationships among various recently proposed techniques such as ViSOM, Isomap, LLE, and eigenmap are discussed and compared.

关键词： Dimensionality reduction nonlinear data projection multidimensional scaling self-organizing maps nonlinear PCA principal manifold

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Performance of CMIP5 Models in the Simulation of Climate Characteristics of Synoptic Patterns over East Asia

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Journal of Meteorological Research 2015年第4期29卷 594-607页

作者：王永弟江志红陈威霖 Key Laboratory of Meteorological Disaster of Ministry of Education Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science & Technology School of Geography and Remote Sensing Nanjing University of Information Science & Technology

The evolution of daily synoptic weather patterns is the main driver of day-to-day weather change. These patterns are generally associated with changes in temperature, precipitation, etc., especially during extreme weathers. Evaluating the ability of climate models to reproduce the frequency and intensity of daily synoptic patterns is essential for increasing confidence in future projections. In this study, we investigated the ability of 34 global climate models （GCMs） included in the Coupled Model Intercomparison Project Phase 5 （CMIP5） to simulate synoptic patterns over East Asia and their evolution features in winter and summer. Daily synoptic patterns in sea level pressure and their occurrence frequencies were identified by using an objective clustering algorithm, self-organizing maps （SOMs）. The evaluation consists of correlating the frequencies of these patterns in the 34 CMIP5 models with the frequencies in the NCEP reanalysis during the baseline period of 1980-1999. The results illustrated that most of these models were able to reproduce the synoptic patterns of the NCEP reanalysis. In addition, the frequencies of temporal sea level pressure （SLP） anomaly patterns were reproduced by most of the models over the baseline period, but the frequencies of spatial SLP anomaly patterns were only reproduced by a few GCMs. Overall, the models performed better in summer than in winter. Comprehensive evaluation shows that the four top-performing models for both winter and summer are bcc-csml-l-m, NorESM1-M, MRI-CGCM3, and CCSM4. They show good performance in simulating the daily synoptic patterns in SLP and in reproducing their occurrence frequencies. The results showed that the SOM was an effective tool for differentiating characteristics of synoptic circulation patterns and for evaluating the ability of climate models to simulate the frequency of daily synoptic patterns. The results can also help users to choose a better model for future climate projection and downscaling over East Asia.

关键词： CMIP5 climate model evaluation self-organizing maps atmospheric circulation pattern

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Fault Identification and Health Monitoring of Gas Turbine Engines Using Hybrid Machine Learning-based Strategies

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风机技术 2022年第1期64卷 71-80页

作者： Yan-yan Shen Khashayar Khorasani Department of Electrical and Computer Engineering Concordia UniversityMontrealCanada.

Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.

关键词： Gas Turbine Engines Health Monitoring Fault Identification self-organizing maps Machine Learn-ing Recurrent Neural Networks

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