Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics

作     者：Eyo Eyo Samuel Abbey Eyo Eyo;Samuel Abbey

作者机构：Faculty of Environment and TechnologyDepartment of Geography and Environmental ManagementCivil Engineering ClusterUniversity of the West of EnglandBristolBS161QYUK Fugro GB Marine Geotechnical Services LimitedWallingfordOX109RBUK 

出 版 物：《Journal of Rock Mechanics and Geotechnical Engineering》 (岩石力学与岩土工程学报（英文版）)

年 卷 期：2022年第14卷第2期

页      面：603-615页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 09[农学] 0903[农学-农业资源与环境] 0835[工学-软件工程] 0811[工学-控制科学与工程] 090301[农学-土壤学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Soil classification Physico-chemistry Soil plasticity Machine learning Logistic regression(LR) Machine learning ensembles Artificial neural network(ANN) 

摘      要：This study has provided an approach to classify soil using machine *** elements of stand-alone machine learning algorithms(*** regression(LR)and artificial neural network(ANN)),decision tree ensembles(*** forest(DF)and decision jungle(DJ)),and meta-ensemble models(*** ensemble(SE)and voting ensemble(VE))were used to classify soils based on their intrinsic physico-chemical ***,the multiclass prediction was carried out across multiple cross-validation(CV)methods,*** validation split(TVS),k-fold cross-validation(KFCV),and Monte Carlo cross-validation(MCCV).Results indicated that the soils’clay fraction(CF)had the most influence on the multiclass prediction of natural soils’plasticity while specific surface and carbonate content(CC)possessed the least within the nature of the dataset used in this ***-alone machine learning models(LR and ANN)produced relatively less accurate predictive performance(accuracy of 0.45,average precision of 0.5,and average recall of 0.44)compared to tree-based models(accuracy of 0.68,average precision of 0.71,and recall rate of 0.68),while the meta-ensembles(SE and VE)outperformed(accuracy of 0.75,average precision of 0.74,and average recall rate of 0.72)all the models utilised for multiclass *** analysis of the meta-ensembles proved their capacities to discriminate between soil classes across the methods of CV *** learning training and validation using MCCV and KFCV methods enabled better prediction while also ensuring that the dataset was not overfitted by the machine learning *** confirmation of this phenomenon was depicted by the continuous rise of the cumulative lift curve(LC)of the best performing models when using the MCCV ***,this study demonstrated that soil’s physico-chemical properties do have a direct influence on plastic behaviour and,therefore,can be relied upon to classify soils.