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Artificial Neural Network （ANN） and regression tree （CART） applications for the indirect estimation of unsaturated soil shear strength parameters

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Frontiers of Earth Science 2014年第3期8卷 439-456页

作者： D.P. KANUNGO Shaifaly SHARMA Anindya PAIN Geotechnical Engineering Group CSIR - Central Building Research Institute (CBRI) Roorkee 247667 India

The shear strength parameters of soil （cohesion and angle of internal friction） are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network （ANN） and regression tree （CART） techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network （i.e., 6/2/2） were attempted using Connec- tion Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.

关键词： cohesion friction angle Artificial NeuralNetwork regression tree Connection Weight Weight-bias Approach

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Simulation of reservoir outflows using regression tree and support vector machine

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AI in Civil Engineering 2023年第1期2卷 93-104页

作者： Vijay Kaushik Noopur Awasthi Research Scholar Department of Civil EngineeringDelhi Technological UniversityDelhi 110042India

Water stored in reservoirs has a lot of crucial function,including generating hydropower,supporting water supply,and relieving lasting *** floods,water deliveries from reservoirs must be acceptable,so as to guarantee that the gross volume of water is at a safe level and any release from reservoirs will not trigger flooding *** study aims to develop a well-versed assessment method for managing reservoirs and pre-releasing water outflows by using the machine learning *** a new and exciting AI area,this technology is regarded as the most valuable,time-saving,supervised and cost-effective *** this study,two data-driven forecasting models,i.e.,regression tree(RT)and Support Vector Machine(SVM),were employed for approximately 30 years’hydrological records,so as to simulate reservoir *** SVM and RT models were applied to the data,accurately predicting the fluctuations in the water outflows of a Bhakra *** input combinations were used to determine the most effective *** cross-validation,the number of folds *** is found that quadratic SVM for 10 folds with seven different parameters would give the minimum RMSE,maximum R2,and minimum MAE;therefore,it can be considered as the best model for the dataset used in this study.

关键词： Reservoir outflow regression tree Support vector machine Error analysis

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Machine learning prediction of methane,ethane,and propane solubility in pure water and electrolyte solutions:Implications for stray gas migration modeling

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Acta Geochimica 2024年第5期43卷 971-984页

作者： Ghazal Kooti Reza Taherdangkoo Chaofan Chen Nikita Sergeev Faramarz Doulati Ardejani Tao Meng Christoph Butscher Institute of Geotechnics TU Bergakademie FreibergGustav-Zeuner-Str.109599 FreibergGermany Department of Petroleum Engineering Amirkabir University of TechnologyTehranIran Freiberg Center for Water Research ZeWaF TU Bergakademie Freiberg09599 FreibergGermany School of Mining College of EngineeringUniversity of TehranTehranIran Taiyuan University of Science and Technology Taiyuan 030024China

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow *** stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric *** knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the ***,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and *** machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of *** predictions were compared with the experimental data as well as four well-established thermodynamic *** analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic *** coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.

关键词： Gas solubility Hydraulic fracturing Thermodynamic models regression tree Boosted regression tree Groundwater contamination

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Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing,China

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Journal of Environmental Sciences 2007年第2期19卷 250-256页

作者： XIAO Rong-bo OUYANG Zhi-yun ZHENG Hua LI Wei-feng SCHIENKE Erich W WANG Xiao-ke State Key Lab of Urban and Regional Ecology Environmental Sciences Chinese Academy of Sciences Belting 100085 China Department of Science and Technology Studies Rensselaer Polytechnic Institute Troy NY 12180 USA

Land surface temperature （LST）, which is heavily influenced by urban surface structures, is a significant parameter in urban environmental analysis. This study examined the effect impervious surfaces （IS） spatial patterns have on LST in Beijing, China. A classification and regression tree model （CART） was adopted to estimate IS as a continuous variable using Landsat images from two seasons combined with QuickBird. LST was retrieved from the Landsat Thematic Mapper （TM） image to examine the relationships between IS and LST. The results revealed that CART was capable of consistently predicting LST with acceptable accuracy （correlation coefficient of 0.94 and the average error of 8.59%）. Spatial patterns of IS exhibited changing gradients across the various urban-rural transects, with LST values showing a concentric shape that increased as you moved from the outskirts towards the downtown areas. Transect analysis also indicated that the changes in both IS and LST patterns were similar at various resolution levels, which suggests a distinct linear relationship between them. Results of correlation analysis further showed that IS tended to be positively correlated with LST, and that the correlation coefficients increased from 0.807 to 0.925 with increases in IS pixel size. The findings identified in this study provide a theoretical basis for improving urban planning efforts to lessen urban temperatures and thus dampen urban heat island effects.

关键词： urban heat islands urban land cover normalized difference vegetation index (NDVI) climate mitigation regression tree

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Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 770-774页

作者： Xiaojun Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian UniversityDalian 116622 China

In ladle furnace, the prediction of the liquid steel temperature is always a hot topic for the researchers. The most of the existing temperature prediction models use small sample set. Today, the precision of them can not satisfy practical production. Fortunately, the large sample set is accumulated from the practical production process. However, a large sample set makes it difficult to build a liquid steel temperature model. To deal with the issue, the random forest method is preferred in this paper, which is a powerful regression method with low complexity and can be designed very quickly. It is with the parallel ensemble structure,uses sample subsets,and employs a simple learning algorithm of sub-models. Then, the random forest method is applied to establish a temperature model by using the data sampled from the production process. The experiments show that the random forest temperature model is more precise than other temperature models.

关键词： Ladle furnace random forest regression tree temperature prediction

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Integrating global land cover products for improved forest cover characterization: an application in North America

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International Journal of Digital Earth 2014年第9期7卷 709-724页

作者： Xiao-Peng Song Chengquan Huang Min Feng Joseph O.Sexton Saurabh Channan John R.Townshend Global Land Cover Facility Department of Geographical SciencesUniversity of MarylandCollege ParkMDUSA

Six widely used coarse-resolution global land cover data-sets–Global Land Cover Characterization(GLCC),Global Land Cover 2000(GLC2000),GlobCover land cover product(GlobCover),MODIS land cover product(MODIS LC),the University of Maryland land cover product(UMD LC),and the MODIS Vegetation Continuous Fields tree cover layer(MODIS VCF)disagree substantially in their estimates of forest *** a regression tree model trained on higher-resolution,Landsat-based data,these multisource multiresolution maps were integrated for an improved characterization of forest cover over North *** using a withheld test sample,the integrated percent forest cover(IPFC)data-set has a root mean square error of 11.75%–substantially better than the 17.37% of GLCC,17.61% of GLC2000,17.96% of GlobCover,15.23% of MODIS LC,19.25%of MODIS VCF,and 15.15%of UMD LC,*** demonstrated for forest,this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.

关键词： land cover forest data fusion regression tree North America

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Machine learning-based constitutive models for cement-grouted coal specimens under shearing

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International Journal of Mining Science and Technology 2021年第5期31卷 813-823页

作者： Guichen Li Yuantian Sun Chongchong Qi School of Mines China University of Mining and TechnologyXuzhou 221116China State Key Laboratory of Coal Resources and Safe Mining China University of Mining and TechnologyXuzhou 221116China School of Resources and Safety Engineering Central South UniversityChangsha 410083China

Cement-based grouting has been widely used in mining engineering;its constitutive law has not been comprehensively *** this study,a novel constitutive law of cement-grouted coal specimens(CGCS)was developed using hybrid machine learning(ML)*** tests were performed on CGCS for the analysis of stress-strain curves and the preparation of the *** maintain the interpretation of the trained ML models,regression tree(RT)was used as the main *** effect of maximum RT depth(Maxdepth)on its performance was studied,and the hyperparameters of RT were tuned using the genetic algorithm(GA).The RT performance was also compared with ensemble learning *** optimum correlation coefficient on the training set was determined as 0.835,0.946,0.981,and 0.985 for RT models with Maxdepth=3,5,7,and 9,*** overall correlation coefficient was over 0.9 when the Maxdepth≥5,indicating that the constitutive law of CGCS can be well ***,the failure type of CGCS could not be captured using the trained RT *** forest was found to be the optimum algorithm for the constitutive modeling of CGCS,while RT with the Maxdepth=3 performed the worst.

关键词： Constitutive law Cement-grouted coal specimens Machine learning regression tree Ensemble learning

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Improving global land cover characterization through data fusion

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Geo-Spatial Information Science 2017年第2期20卷 141-150页

作者： Xiao-Peng Song Chengquan Huang John R.Townshend Department of Geographical Sciences University of MarylandCollege ParkMDUSA

Global-scale land cover characterization has advanced from a spatial resolution of 1×1°in the mid-1990s to 30×30 m resolution to ***,some mapping challenges exist persistently regardless of the increasing spatial *** fusion has been proved as an effective way of improving land cover *** we applied a machine learning-based data integration approach for improving global-scale forest cover *** approach employed six coarse-resolution(250-1000 m)global land cover maps as input and various regional,higher-resolution land cover data-sets as reference to build regression tree models per *** average error of 10-fold cross validation of the regression tree models varied between 7.70 and 15.68% forest cover and the r2 varied between 0.76 and 0.94,indicating the robustness of the trained *** a result of data fusion,the synthesized global forest cover map was more accurate than any input global *** also showed that other major vegetative land cover types such as cropland,woodland,grassland,and wetland all exhibit similar magnitude of discrepancies as forest among existing land cover *** developed method,because of its type-and scale-invariant feature,can be implemented for other land cover types for improving their global *** ensemble approach can also be internalized for improving data quality when generating a global land cover product,where multiple versions can be produced and subsequently integrated.

关键词： Satellite remote sensing land cover data fusion regression tree global

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Environmental control of daily stem radius increment in the montane conifer Cedrus libani

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Journal of Forestry Research 2020年第4期31卷 1159-1171页

作者： Aylin Güney Serkan Gülsoy Ozdemir Sentürk Armin Niessner Manfred Küppers Institute of Botany University of HohenheimStuttgartGermany Faculty of Forestry Isparta University of Applied SciencesIspartaTurkey Golhisar Vocational School Burdur Mehmet Akif Ersoy UniversityBurdurTurkey

Growth response of conifers is species-specific and depends on site and climate *** on daily radial stem growth use different analytical approaches to determine species reactions to environmental *** results contribute to improve forecasts of tree growth under a changing *** 2013 and2014,radial stem growth of 33 mature Cedrus libani individuals growing under different climatic conditions in Turkey and Germany was monitored hourly using high precision point *** radius increments(SRI)were extracted from dendrometer *** annual course of SRI showed site-specific patterns with mean daily values ranging between 9.9 and 29.3μm over the growing *** and principal component analyses indicated that humidity and low temperatures during the growing season favored *** regression analyses demonstrated that precipitation and relative air humidity were the most important factors influencing daily ***-growth relationships were further evaluated using the regression tree *** was the most significant factor on daily SRI for all *** close coupling of SRI to relative air humidity and precipitation underlines the importance of stem water status for radial stem growth of *** which is native to regions with summer *** further explains the superior growth of *** in Germany.

关键词： CART Climate-growth relationship Dendrometer Lebanon cedar regression tree

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Spatial variation and prediction of forest biomass in a heterogeneous landscape

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Journal of Forestry Research 2012年第1期23卷 13-22页

作者： S. Lamsal D. M. Rizzo R. K. Meentemeyer Department of Geography and Earth Sciences University of North Carolina Charlotte NC U. S. A Department of Plant Pathology University of California Davis CA U. S. A.

Large areas assessments of forest bioinass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistical and geospatial modeling on densely sample.d forest biomass data to analyze the relative importance of ecological and physiographic variables as determinants of spatial variation of forest biomass in the environmentally heterogeneous region of the Big Sur, California. We estimated biomass in 280 forest Plots （one plot per 2.85 km2） and meas- ured an array of ecological （vegetation community type, distance to edge, amount of surrounding non-forest vegetation, soil properties, fire history） and physiographic drivers （elevation, potential soil moisture and solar radiation, proximity to the coast） of tree growth at each plot location. Our geostatistical analyses revealed that biomass distribution is spatially structured and autocorrelated up to 3.1 kin. regression tree （RT） models showed that both physiographic and ecological factors influenced bio- mass distribution. Across randomly selected sample densities （sample size 112 to 280）, ecological effects of vegetation community type and distance to forest edge, and physiographic effects of elevation, potential soil moisture and solar radiation were the most consistent predictors of biomass. Topographic moisture index and potential solar radiation had apositive effect on biomass, indicating the importance of topographically- mediated energy and moisture on plant growth and biomass accumula- tion. RT model explained 35% of the variation in biomass and spatially autocorrelated variation were retained in regession residuals. regression kriging model, developed from RT combined with kriging of regression residuals, was used to map biomass across the Big Sur. This study dem- onstrates how statistical and geospatial modeling can be used to dis- criminate the relative importance of physiographic and ecologic effect

关键词： forest biomass landscape heterogeneity spatial variation semivariogram regression tree regression kriging Big Sur California

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