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Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

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Chinese Physics B 2023年第4期32卷 1-10页

作者：励明君杨哩娜王登王斯艺唐静楠姜毅陈杰 Laboratory of Aerospace Launching Technology School of Aerospace EngineeringBeijing Institute of TechnologyBeijing 100081China Center for Phononics and Thermal Energy Science China–EU Joint Laboratory for NanophononicsMinistry of Education Key Laboratory of Advanced Micro-structured MaterialsSchool of Physics Science and EngineeringTongji UniversityShanghai 200092China

The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements are required to bear large impulse load.However,traditional methods cannot non-destructively and quickly detect the internal structural of pavements.Thus,accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity.In recent years,machine learning has shown great superiority in solving nonlinear problems.In this work,we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing.The regression coefficient R^(2)of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses,which indicates that the prediction results have great consistency with finite element simulation results.This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements,and has application potential in non-destructive evaluation of pavement structure.

关键词： deflection basin parameters pavement condition assessment instantaneous large impact random forest regression

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Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

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Computer Systems Science & Engineering 2022年第8期42卷 545-560页

作者： R.Saravana Ram M.Vinoth Kumar N.Krishnamoorthy A.Baseera D.Mansoor Hussain N.Susila Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing processes.Sensors play a vital role in emerging technologies of the 20th century.Localization of sensors in needed locations is a very serious problem.The environment is home to every living being in the world.The growth of industries after the industrial revolution increased pollution across the environment.Owing to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are needed.An interesting and challenging task is choosing the place to fit the sensors.Many meta-heuristic techniques have been introduced in node localization.Swarm intelligent algorithms have proven their efficiency in many studies on localization problems.In this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor network.First,our work aims at selecting industrial areas in the sensed location.We use random forest regression methodology to select the polluted area.Then,the elephant herding algorithm is used in sensor node localization.These two algorithms are combined to produce the best standard result in localizing the sensor nodes.To check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with algorithms.The results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer perceptron.Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology.Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5.

关键词： Wireless sensor networks node localization industrial-centric approach random forest regression elephant herding optimization swarm intelligence pollution

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Robust Length of Stay Prediction Model for Indoor Patients

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Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and random forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital sta

关键词： Length of stay machine learning robust model random forest regression

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Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery

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The Crop Journal 2022年第5期10卷 1376-1385页

作者： Guomin Shao Wenting Han Huihui Zhang Yi Wang Liyuan Zhang Yaxiao Niu Yu Zhang Pei Cao College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of AgricultureYangling 712100ShaanxiChina Institute of Water-Saving Agriculture in Arid Areas of China Northwest A&F UniversityYangling 712100ShaanxiChina Water Management and Systems Research Unit USDA-ARS2150 Centre AvenueBldg.D.Fort CollinsCO 80526USA College of Information Xi’an University of Finance and EconomicsXi’an 710100ShaanxiChina Institute of Soil and Water Conservation Northwest A&F UniversityYangling 712100ShaanxiChina University of Chinese Academy of Sciences Beijing 100049China

Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning(ML) method for estimating CTc and AGB using time-series unmanned aerial vehicle(UAV)-based multispectral vegetation indices(VIs)of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression(MLR), support vector regression(SVR), random forest regression(RFR), and adaptive boosting regression(ABR), were used to address the complex relationship between CTcand VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTcestimation model. The UAV VIs-derived CTcusing the RFR estimation model yielded the highest accuracy(R^(2)= 0.91, RMSE = 0.0526, and n RMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTcmodel. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy(R^(2)= 0.76, RMSE = 282.8 g m, and n RMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTcperformed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale.

关键词： Crop transpiration Normalized difference red-edge index Unmanned aerial vehicles random forest regression Biomass

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Explainable Artificial Intelligence Solution for Online Retail

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Computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning

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Geoscience Frontiers 2021年第1期12卷 365-373页

作者： Runhong Zhang Chongzhi Wu Anthony T.C.Goh Thomas Bohlke Wengang Zhang School of Civil Engineering Chongqing UniversityChongqing400045China School of Civil and Environmental Engineering Nanyang Technological University639798Singapore Institute of Engineering Mechanics Karlsruhe Institute of Technology(KIT)Kaiserstraße 1076131KarlsruheGermany Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing UniversityMinistry of EducationChongqing400045China

This paper adopts the NGI-ADP soil model to carry out finite element analysis,based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated.More than one thousand finite element cases were numerically analyzed,followed by extensive parametric studies.Surrogate models were developed via ensemble learning methods(ELMs),including the e Xtreme Gradient Boosting(XGBoost),and random forest regression(RFR)to predict the maximum lateral wall deformation(δhmax).Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree regression(DTR),Multilayer Perceptron regression(MLPR),and Multivariate Adaptive regression Splines(MARS).This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast,alternative way.

关键词： Anisotropic clay NGI-ADP Wall deflection Ensemble learning eXtreme gradient boosting random forest regression

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Exploring nationally and regionally defined models for large area population mapping

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International Journal of Digital Earth 2015年第12期8卷 989-1006页

作者： A.E.Gaughan F.R.Stevens C.Linard N.N.Patel A.J.Tatem Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA Biological Control and Spatial Ecology UniversitéLibre de BruxellesBrusselsBelgium Department of Geography and Geoinformation Science George Mason UniversityFairfaxVAUSA Department of Geography and Environment University of SouthamptonSouthamptonUK Fogarty International Center National Institutes of HealthBethesdaMDUSA Flowminder Foundation StockholmSweden

Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

关键词： human population modeling random forest regression dasymetric mapping gridded population datasets

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Pore pressure prediction in offshore Niger delta using data-driven approach: Implications on drilling and reservoir quality

能源地球科学(英文)

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能源地球科学(英文) 2023年第3期4卷 252-265页

作者： Joshua Pwavodi Ibekwe N.Kelechi Perekebina Angalabiri Sharon Chioma Emeremgini Vivian O.Oguadinma Univ.Grenoble Alpes Univ.Savoie Mont BlancCNRSIRDUGEISTerre38000GrenobleFrance TotalEnergies SA CSTJF Avenue Larribau64000PauFrance Centre of Excellence in Petroleum Geoscience and Engineering University of BeninBenin CityEdo StateNigeria Univ.Lille CNRSUniv.Littoral Cote d’OpaleUMR 8187LOGLaboratoire d’Oceanologie et de Geosciences59000LilleFrance

Despite exploration and production success in Niger Delta,several failed wells have been encountered due to overpressures.Hence,it is very essential to understand the spatial distribution of pore pressure and the generating mechanisms in order to mitigate the pitfalls that might arise during drilling.This research provides estimates of pore pressure along three offshore wells using the Eaton's transit time method,multi-layer perceptron artificial neural network(MLP-ANN)and random forest regression(RFR)algorithms.Our results show that there are three pressure magnitude regimes:normal pressure zone(hydrostatic pressure),transition pressure zone(slightly above hydrostatic pressure),and over pressured zone(significantly above hydrostatic pressure).The top of the geopressured zone(2873 mbRT or 9425.853 ft)averagely marks the onset of overpressurization with the excess pore pressure above hydrostatic pressure(P∗)varying averagely along the three wells between 1.06−24.75 MPa.The results from the three methods are self-consistent with strong correlation between the Eaton's method and the two machine learning models.The models have high accuracy of about>97%,low mean absolute percentage error(MAPE0.98).Our results have also shown that the principal generating mechanisms responsible for high pore pressure in the offshore Niger Delta are disequilibrium compaction,unloading(fluid expansion)and shale diagenesis.

关键词： Niger Delta Pore pressure Reservoir Fracturing pressure Artifidal neural network Machine leaming algorithm random forest regression

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Synthesis of True Color Images from the Fengyun Advanced Geostationary Radiation Imager

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Journal of Meteorological Research 2021年第6期35卷 1136-1147页

作者： Yuchen XIE Xiuzhen HAN Shanyou ZHU School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044 National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081

The production of true color images requires observational data in the red,green,and blue(RGB)bands.The Advanced Geostationary Radiation Imager(AGRI)onboard China’s Fengyun-4(FY-4)series of geostationary satellites only has blue and red bands,and we therefore have to synthesize a green band to produce RGB true color images.We used random forest regression and conditional generative adversarial networks to train the green band model using Himawari-8 Advanced Himawari Imager data.The model was then used to simulate the green channel reflectance of the FY-4 AGRI.A single-scattering radiative transfer model was used to eliminate the contribution of Rayleigh scattering from the atmosphere and a logarithmic enhancement was applied to process the true color image.The conditional generative adversarial network model was better than random forest regression for the green band model in terms of statistical significance(e.g.,a higher determination coefficient,peak signal-to-noise ratio,and structural similarity index).The sharpness of the images was significantly improved after applying a correction for Rayleigh scattering,and the images were able to show natural phenomena more vividly.The AGRI true color images could be used to monitor dust storms,forest fires,typhoons,volcanic eruptions,and other natural events.

关键词： Advanced Geostationary Radiation Imager(AGRI) RGB true color random forest regression conditional generative adversarial networks Rayleigh scattering

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A Meta-Learning Approach for Aircraft Trajectory Prediction

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Communications and Network 2023年第2期15卷 43-64页

作者： Syed Ibtehaj Raza Rizvi Jamal Habibi Markani René Jr. Landry É cole de technologies supérieure (&#201TS) Montréal Canada

The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, random forest regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.

关键词： Trajectory Prediction General Aviation Safety Meta-Learning random forest regression Long Short-Term Memory Short to Mid-Term Trajectory Prediction Operational Safety

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