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Estimating the thermal conductivity of soils using six machine learning algorithms

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2022年第0期136卷

作者： Li, Kai-Qi Liu, Yong Kang, Qing Wuhan Univ Inst Engn Risk & Disaster Prevent State Key Lab Water Resources & Hydropower Engn Sc 299 Bayi Rd Wuhan 430072 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore

Many machine learning algorithms have been applied to determine soil properties in recent years. However, the prediction performances of thermal conductivity of soils via machine learning algorithms are still unclear due to the lack of sufficient databases. In this work, a large database containing 2197 data points from various literature was compiled. Six machine learning algorithms, namely multivariance linear regression (MLR), Gaussian process regression (GPR), support vector machine (SVM), decision tree (DT), random forest (RF) and adaptive boosting methods (AdaBoost) were implemented to predict the thermal conductivity of soils based on the compiled database. In addition, Spearman correlation analysis and grey relational analysis were adopted to compute the strength of influencing factors in thermal conductivity. For obtaining the best prediction model, the performances of six machine learning algorithms were assessed via eight evaluation indicators and compared with three typical empirical models. Results show that the AdaBoost can yield good predicted values of the thermal conductivity of soils with minimum errors (i.e., RMSE = 0.099). This study constructs a database of thermal conductivity and provides a reference for evaluating the thermal conductivity of soils via machine learning algorithms.

关键词： machine learning algorithms Thermal conductivity database Parameter correlation Performance indicators Ensemble learning model

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Comparison of machine learning algorithms in the Prediction of Hospitalized Patients with Schizophrenia

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SENSORS 2022年第7期22卷 2517页

作者： Gongora Alonso, Susel Marques, Goncalo Agarwal, Deevyankar De la Torre Diez, Isabel Franco-Martin, Manuel Univ Valladolid Dept Signal Theory & Commun Paseo Belen 15 Valladolid 47011 Spain Polytech Coimbra ESTGOH Rua Gen Santos Costa P-3400124 Oliveira Do Hospital Portugal Higher Coll Technol Dept Engn EEE Sect Muscat 113 Oman Hosp Zamora Psiquiatry Serv Zamora 49021 Spain

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naive Bayes, Support Vector machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders;these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

关键词： hospitalization machine learning algorithms predictive models random forest schizophrenia

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MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three machine learning algorithms

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REMOTE SENSING 2017年第12期9卷 1326-1326页

作者： Wang, Xuanyu Yao, Yunjun Zhao, Shaohua Jia, Kun Zhang, Xiaotong Zhang, Yuhu Zhang, Lilin Xu, Jia Chen, Xiaowei Beijing Normal Univ Fac Geog Sci State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Beijing Normal Univ Inst Remote Sensing Sci & Engn Fac Geog Sci Beijing Engn Res Ctr Global Land Remote Sensing P Beijing 100875 Peoples R China Minist Environm Protect Satellite Environm Ctr Beijing 100094 Peoples R China Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China

Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs) across North America using three machine learning algorithms: artificial neural network (ANN);support vector machines (SVM);and, multivariate adaptive regression spline (MARS) driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period 2000-2007, all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002-2004 using a spatial resolution of 0.05 degrees, which proved to be useful for estimating the long-term LE over North America.

关键词： latent heat machine learning algorithms plant functional type

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Comparison of SERS spectra of intact and inactivated viruses via machine learning algorithms for the viral disease's diagnosis application

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PHOTONICS AND NANOSTRUCTURES-FUNDAMENTALS AND APPLICATIONS 2024年 61卷

作者： Andreeva, Olga Tabarov, Artem Grigorenko, Konstantin Dobroslavin, Alexander Gazizulin, Azat Gorshkov, Andrey Zheltukhina, Alyona Gavrilova, Nina Danilenko, Daria Vitkin, Vladimir ITMO Univ Birzhevaya Liniya 14 St Petersburg 199034 Russia Smorodintsev Res Inst Influenza Prof Popova Str 15-17 St Petersburg 197376 Russia

In this work, Surface-enhanced Raman spectroscopy (SERS) along with machine learning algorithms (MLA) were used to detect and classify the viral particles to assess the possibility of using the spectra of inactivated influenza A viruses for MLA training and spectra database compilation for further study and diagnosis of intact forms of the virus. Viral particles inactivation was performed by formalin, ultraviolet and beta-propiolactone. Support vector method and principal component analysis allowed to classify intact and inactivated viral particles spectra with an accuracy of 80.0-96.7 %. The results obtained suggest that it is not advisable to create a spectral database and train machine learning algorithms for their further application in SERS diagnostics of intact viruses based on the spectra of the inactivated virus particles.

关键词： Surface-enhanced Raman spectroscopy Influenza A viruses Inactivation machine learning algorithms Support vector machine Principal component analysis

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Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms

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GEOMATICS NATURAL HAZARDS & RISK 2023年第1期14卷

作者： Wu, Hao Nian, Tingkai Shan, Zhigang Chinese Acad Sci Inst Mt Hazards & Environm Chengdu Sichuan Peoples R China POWERCHINA Huadong Engn Corp Ltd Hangzhou Zhejiang Peoples R China Dalian Univ Technol Sch Civil Engn Dalian Liaoning Peoples R China

A rapid and accurate prediction of a landslide dam's life span is of significant importance for emergency geological treatment. However, current prediction models for the state of a landslide dam are based solely on geomorphological indexes, and do not take into consideration attribute properties such as landslide types, trigger factors, and dam types. This study investigates the relationships between a landslide dam's geometry and the capacity of the barrier lake and proposes fitting models, which supplement the current landslide dam database. Subsequently, six predictive models for landslide dam life span are established, utilizing machine learning algorithms such as logistic regression, k-nearest neighbors, support vector machine, Naive Bayes, decision tree, and random forest, which consider five factors, including geometry parameters and attribute properties. The performances of these six models are analyzed and compared to a typical prediction model, the dimensionless blockage index (DBI). The results suggest that the models established in this study not only have a consistent absolute accuracy as the DBI model, but also overcome the disadvantage that a large number of cases cannot be judged by the DBI model. Among the formulated machine learning models, the random forest model exhibits the highest absolute accuracy (89%), lowest error rate (7%), lowest false alarm rate (15%), and no uncertainty rate. Additionally, three renowned landslide dams, namely the Costantino, Hsiaolin, and Baige landslide dams, are analyzed to illustrate the applicability of the established machine learning models. The study results provide essential guidance for the predictions and emergency geological treatments of landslide dam disasters.

关键词： Landslide dam life span prediction machine learning algorithms database landslide dam type

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Intelligent prediction of comprehensive mechanical properties of recycled aggregate concrete with supplementary cementitious materials using hybrid machine learning algorithms

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Miao, Xu Zhu, Ji-Xiang Zhu, Wen-Biao Wang, Yuzhou Peng, Ligang Dong, Hao-Le Xu, Ling-Yu Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Zhejiang Provin Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Kowloon Hong Kong Peoples R China

Considering the wide utilization of multi-component supplementary cementitious materials in recycled aggregate concrete, an intelligent prediction of comprehensive mechanical properties of this sustainable low-carbon concrete was proposed in this study using hybrid machine learning algorithms, rather than conducting tremendous experiments to establish a statistical empirical model previously. The database consisting of 980 datasets was used to train, validate and test the machine learning algorithms with various evaluation indicators. SHapley Additive exPlanations (SHAP) values were used for interpretability analysis of the predictive results of algorithms to quantify the contribution of each input feature. The results show that the hybrid algorithms possess higher prediction accuracy in terms of compressive strength, flexural strength, splitting tensile strength and elastic modulus compared to the single algorithms, among which Sparrow Search Algorithm-Extreme Gradient Boosting (SSA-XGB) stands out as the most promising algorithm model with satisfactory stability and generalization performance. For all mechanical properties, the effective water-binder ratio, cement content, and superplasticizer dosage are extremely important influencing factors. This study provides a systematic assessment framework for predicting the comprehensive mechanical properties of recycled aggregate concrete with supplementary cementitious materials, beneficial for the data-driven intelligent mix proportions design of sustainable low-carbon concrete in construction sector.

关键词： Recycled aggregate concrete Supplementary cementitious materials Mechanical properties machine learning algorithms Interpretability analysis

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Surface Soil Moisture Retrieval on Qinghai-Tibetan Plateau Using Sentinel-1 Synthetic Aperture Radar Data and machine learning algorithms

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REMOTE SENSING 2023年第1期15卷 153-153页

作者： Dong, Leilei Wang, Weizhen Jin, Rui Xu, Feinan Zhang, Yang Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Remote Sensing Gansu Prov Heihe Remote Sensing Expt Res Stn Lanzhou 730000 Peoples R China Chinese Acad Sci Key Lab Land Surface Proc & Climate Change Cold & Lanzhou 730000 Peoples R China

Soil moisture is a key factor in the water and heat exchange and energy transformation of the ecological systems and is of critical importance to the accurate obtainment of the soil moisture content for supervising water resources and protecting regional and global eco environments. In this study, we selected the soil moisture monitoring networks of Naqu, Maqu, and Tianjun on the Qinghai-Tibetan Plateau as the research areas, and we established a database of surface microwave scattering with the AIEM (advanced integral equation model) and the mathematical expressions for the backscattering coefficient, soil moisture, and surface roughness of the VV and VH *** proposed the soil moisture retrieval models of empirical and machine learnings algorithms (backpropagation neural network (BPNN), support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) for the ascending and descending orbits using Sentinel-1 and measurement data, and we also validated the accuracies of the retrieval model in the research areas. According to the results, there is a substantial logarithmic correlation among the backscattering coefficient, soil moisture, and combined roughness. Generally, we can use empirical models to estimate the soil moisture content, with an R-2 of 0.609, RMSE of 0.08, and MAE of 0.064 for the ascending orbit model and an R-2 of 0.554, RMSE of 0.086, and MAE of 0.071 for the descending orbit model. The soil moisture contents are underestimated when the volumetric water content is high. The soil moisture retrieval accuracy is improved with machine learning algorithms compared to the empirical model, and the performance of the RF algorithm is superior to those of the other machine learning algorithms. The RF algorithm also achieved satisfactory performances for the Maqu and Tianjun networks. The accuracies of the inversion models for the ascending orbit in the three soil moisture monitoring networks were better than those for the descending

关键词： soil moisture AIEM machine learning algorithms Sentinel-1 Qinghai-Tibetan Plateau

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Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings

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ENERGY CONVERSION AND MANAGEMENT 2021年 238卷 114153-114153页

作者： Javanmard, Majid Emami Ghaderi, S. F. Hoseinzadeh, Mohamad Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Res Inst Energy Management & Planning Tehran Iran

In recent years, various models, which employ green and sustainable energy-supplying systems, have been presented in different ways to optimize water and energy consumption in several countries, pursuing the purpose of reducing water and energy consumption and costs. The present study considered an integrated and unconcentrated water and energy consumption optimization model in the building and investigated it economically and environmentally using mixed-integer linear programming. In this research, the data of different sections, including the data related to the climatic conditions, environment, costs, technologies, etc., were collected. Next, the researchers sought to predict the model results using 12 highly accurate machine learning algorithms and considering four indices to examine the prediction accuracy of the algorithms. The utilization of the machine learning prediction algorithms helped us economically and environmentally investigate and predict the model conditions in every geographical location with respect to the data that varied according to the climatic conditions. The results obtained from investigating the indices of the algorithms for the data examined for the objective functions, including cost optimization and carbon emission reduction, revealed that the prediction accuracy ranged from 0.8 to 0.96, and 0.79 to 0.91 for the first and second objective functions, respectively, in the 12 examined algorithms. Meanwhile, the Light Gradient Boosting machine and Extra Tree algorithms enjoyed higher prediction accuracy in this research than other algorithms. Next, to analyze the results, the researchers implemented the Principal Component Analysis method to reduce the input data dimension to the algorithms. The results of the algorithms reflected a decline in the prediction accuracy after the dimension reduction. Finally, the effective variables in the prediction accuracy of the algorithms were presented by using the Stepwise Regression method for both o

关键词： Integrated energy-water model Sustainable hybrid energy-water systems Data mining machine learning algorithms Prediction accuracy Optimization energy-water model

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Estimating the grade of storm surge disaster loss in coastal areas of China via machine learning algorithms

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ECOLOGICAL INDICATORS 2022年第0期136卷 108533-108533页

作者： Zhang, Suming Zhang, Jie Li, Xiaomin Du, Xuexue Zhao, Tangqi Hou, Qi Jin, Xifang China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources China Inst Oceanog 1 Qingdao 266061 Peoples R China State Ocean Adm North Sea Marine Forecast Ctr Qingdao 266001 Peoples R China

Storm surge is the most severe marine disaster in China, affecting the whole coastal area. Estimating storm surge disaster loss (SSDL) is significant to disaster prevention, sustainability and decision-making. Taking 11 provincial administrative regions in the coastal areas of China as the study area, this paper estimated SSDL grades based on four machine learning (ML) algorithms. A total of 132 pieces of official open-source data of storm surge disasters were collected and divided into a cross-validation set (CV set) and a test set. First, a comprehensive indicator system was constructed from three perspectives, covering the hazard (16) of disaster-causing factors, the vulnerability (22) and resilience (12) of disaster-bearing bodies, including 50 indicators. A few data preprocessing methods are implemented to improve the model performance such as normalization, SMOTE, etc. Then, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic model tree (LMT), and K-star were applied to construct the estimation model of SSDL grades. Principal component analysis (PCA) and recursive feature elimination (RFE) are adopted for an intelligent screening of the indicators. Finally, the models' performance is compared through Precision, Recall, F1 score and Kappa metrics. The results show that scientific and efficient data preparation is a strong guarantee for the reliability and stability of the models. RFE is verified more suitable for indicator selection in this paper compared with PCA. The importance ranking of RFE enhances the interpretability of the ML model, which shows that the hazard indicator is the most important, the vulnerability indicator is the second, and the resilience indicator is the least. The 27-indicator K-star model, with advantages of accurate estimation, strong generalization, and less workload, is the optimal SSDL estimation model. The number of input indicators of the optimal SSDL estimation model is 27, its CV Precision, Recall, F1 score, and K

关键词： Storm surge disaster loss machine learning algorithms Indicator screening Model interpretability

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Research on hot deformation behavior of Cu-Ti alloy based on machine learning algorithms and microalloying

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MATERIALS TODAY COMMUNICATIONS 2024年 39卷

作者： Zhang, Mengxiao Chen, Dayong Liu, Huan Zhang, Yanyan Song, Hongwu Xu, Yong Zhang, Shihong Chinese Acad Sci Shi Changxu Innovat Ctr Adv Mat Inst Met Res Shenyang 110016 Peoples R China Univ Sci & Technol China Sch Mat Sci & Engn Shenyang 110016 Peoples R China Shenyang Ligong Univ Sch Mech Engn Shenyang 110159 Peoples R China

High strength and high elasticity copper alloys have good application prospects due to their excellent mechanical properties. The deformation and heat treatment in its preparation process are important steps to ensure the service performance. In this work, Cu -Ti based alloys were employed and hot compression experiments were conducted at 500 - 800 degree celsius under four different strain rates. Their constitutive equations and thermal processing maps were constructed. The influence of microalloying elements on activation energy was also analyzed. The microstructure evolution and recrystallization behavior under hot deformation conditions were studied based on the results of TEM. At the same time, Arrhenius, back propagation neural network (BPNN) and support vector machine (SVM) algorithms were used to predict the flow stress of the alloy. The results indicate that the relationship between flow stress and the input factors (strain rate, temperature and deformation) can be well predicted using the support vector machine algorithm. Microalloying can increase the activation energy of copper alloys, and the sensitivity of activation energy to temperature changes is related to the interaction between precipitates and dislocations.

关键词： Cu-Ti alloy Constitutive equations Thermal processing maps machine learning algorithms

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