{"title":"探索机器学习模型,预测铜污染粘土中的解冻水含量","authors":"","doi":"10.1016/j.coldregions.2024.104296","DOIUrl":null,"url":null,"abstract":"<div><p>The article provides new insights into predicting unfrozen water content(u<sub>nf</sub>) in clays contaminated with copper. The objectives of this study included creating machine learning prediction models based on Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Random Forest (RF) algorithms. These models were developed using seventeen soil physicochemical parameters. A total of 575 experimental observations of unfrozen water content, determined by the DSC method over a temperature range of −23 °C to −1 °C, were analyzed. The findings suggest that the unfrozen water content in copper-contaminated clays can be most accurately predicted using the Random Forest model, which achieved a high correlation coefficient (<em>R</em> = 0.962). This model demonstrated greater effectiveness than existing empirical models in estimating unfrozen water content in these soils. Further research should focus on exploring alternative machine learning techniques to improve predictions of unfrozen water content.</p></div>","PeriodicalId":10522,"journal":{"name":"Cold Regions Science and Technology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring machine learning models to predict the unfrozen water content in copper-contaminated clays\",\"authors\":\"\",\"doi\":\"10.1016/j.coldregions.2024.104296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The article provides new insights into predicting unfrozen water content(u<sub>nf</sub>) in clays contaminated with copper. The objectives of this study included creating machine learning prediction models based on Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Random Forest (RF) algorithms. These models were developed using seventeen soil physicochemical parameters. A total of 575 experimental observations of unfrozen water content, determined by the DSC method over a temperature range of −23 °C to −1 °C, were analyzed. The findings suggest that the unfrozen water content in copper-contaminated clays can be most accurately predicted using the Random Forest model, which achieved a high correlation coefficient (<em>R</em> = 0.962). This model demonstrated greater effectiveness than existing empirical models in estimating unfrozen water content in these soils. Further research should focus on exploring alternative machine learning techniques to improve predictions of unfrozen water content.</p></div>\",\"PeriodicalId\":10522,\"journal\":{\"name\":\"Cold Regions Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cold Regions Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165232X24001770\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cold Regions Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165232X24001770","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
摘要
文章为预测铜污染粘土中的解冻水含量(unf)提供了新的见解。这项研究的目标包括创建基于高斯过程回归(GPR)、支持向量机(SVM)和随机森林(RF)算法的机器学习预测模型。这些模型是利用 17 个土壤理化参数建立的。分析了在 -23 °C 至 -1 °C 温度范围内通过 DSC 方法测定的 575 个解冻含水量实验观测值。研究结果表明,使用随机森林模型可以最准确地预测铜污染粘土中的解冻水含量,该模型达到了很高的相关系数(R = 0.962)。在估算这些土壤中的解冻水含量方面,该模型比现有的经验模型更有效。进一步的研究应侧重于探索其他机器学习技术,以改进对解冻水含量的预测。
Exploring machine learning models to predict the unfrozen water content in copper-contaminated clays
The article provides new insights into predicting unfrozen water content(unf) in clays contaminated with copper. The objectives of this study included creating machine learning prediction models based on Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Random Forest (RF) algorithms. These models were developed using seventeen soil physicochemical parameters. A total of 575 experimental observations of unfrozen water content, determined by the DSC method over a temperature range of −23 °C to −1 °C, were analyzed. The findings suggest that the unfrozen water content in copper-contaminated clays can be most accurately predicted using the Random Forest model, which achieved a high correlation coefficient (R = 0.962). This model demonstrated greater effectiveness than existing empirical models in estimating unfrozen water content in these soils. Further research should focus on exploring alternative machine learning techniques to improve predictions of unfrozen water content.
期刊介绍:
Cold Regions Science and Technology is an international journal dealing with the science and technical problems of cold environments in both the polar regions and more temperate locations. It includes fundamental aspects of cryospheric sciences which have applications for cold regions problems as well as engineering topics which relate to the cryosphere.
Emphasis is given to applied science with broad coverage of the physical and mechanical aspects of ice (including glaciers and sea ice), snow and snow avalanches, ice-water systems, ice-bonded soils and permafrost.
Relevant aspects of Earth science, materials science, offshore and river ice engineering are also of primary interest. These include icing of ships and structures as well as trafficability in cold environments. Technological advances for cold regions in research, development, and engineering practice are relevant to the journal. Theoretical papers must include a detailed discussion of the potential application of the theory to address cold regions problems. The journal serves a wide range of specialists, providing a medium for interdisciplinary communication and a convenient source of reference.