检索结果-内蒙古大学图书馆

Radiometric Cross-Calibration of HJ-2A/CCD3 Using the random forest algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI

引用

REMOTE SENSING 2024年第22期16卷 4337-4337页

作者： Zhou, Xiang Chen, Yidan Xie, Yong Han, Jie Shao, Wen Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Nanjing Univ Informat Sci & Technol Sch Geog Sci Nanjing 210044 Peoples R China Xuchang Univ Sch Geog & Geomat Xuchang 461000 Peoples R China

In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross-Li model in current research. However, the Ross-Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to

关键词： HuanJing-2A (HJ-2A) Sentinel-2 cross-calibration bidirectional reflectance distribution function (BRDF) random forest algorithm spectral difference

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Placement prediction system using Support Vector Machine over random forest algorithm 14

Performance Analysis of Placement prediction system using Su...

引用

14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Kumar, Konanki Vinay Malathi, P. Ramesh, Sindhu Saveetha Univ Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665460712

The main purpose of the study is to propose an innovative Placement prediction system by using Support Vector Machine and random forest algorithm to predict the placement of students using machine learning. Results were then comparing the efficiency of the algorithms. Materials and Methods: Support Vector Machine with sample size = 5 and random forest algorithm with sample size = 5 was iterated 10 times for predicting accuracy percentage to identify the placement of students. Results: Support Vector Machine has significantly better accuracy (92.7%) compared to random forest algorithm (63.6%). The statistical significance of the random forest algorithm is 0.03 (p!0.05 Independent Sample T-test) is high. Conclusion: Within the limits of this study, Support Vector Machine offers the best accuracy in Placement Prediction.

关键词： Support Vector Machine (SVM) random forest algorithm Placement Prediction Novel Data Classification Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Fiber Nonlinearity Mitigation for mQAM Coherent Optical Communication System Utilizing random forest algorithm

Fiber Nonlinearity Mitigation for mQAM Coherent Optical Comm...

引用

Asia Communications and Photonics Conference (ACP) / International Conference on Information Photonics and Optical Communications (IPOC)

作者： Zhan, Guanqun Yi, Anlin Yang, Jianbo Yan, Lianshan Jiang, Lin Luo, Bin Southwest Jiaotong Univ Sch Informat Sci & Technol Ctr Informat Photon & Commun Chengdu Peoples R China

ISBN: (纸本)9781665481557

In this paper, the random forest approach is used to mitigate fiber nonlinear damage in a coherent optical communication system. The feasibility of the proposed method is verified in both uniformly distributed m-level quadrature amplitude modulation (mQAM) and probabilistic shaping (PS) mQAM long-haul coherent optical communication system. All of the simulation findings indicate that the tolerance of nonlinearity can be improved by applying this algorithm. As a result, the optimal incoming fiber power can be increased in contrast with using only chromatic dispersion compensation (CDC).

关键词： coherent optical communication system decision tree random forest algorithm probabilistic shaping technology nonlinear mitigation

来源：评论

学校读者我要写书评

暂无评论

An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm

引用

JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022年第2期5卷 629-659页

作者： Shin, Tacksoo Long, Jeffrey D. Davison, Mark L. Myongji Univ Dept Youth Educ & Leadership Seoul South Korea Univ Iowa Dept Psychiat Iowa City IA 52242 USA Univ Minnesota Twin Cities Dept Educ Psychol Minneapolis MN USA

The objective of this study was to examine the performance of the two most popular missing data methods (i.e., multiple imputation and maximum likelihood), as well as newly developed machine learning framework based on random forest algorithm for missing data under various reserach conditions. The design of the simulation study included random and non-random missingness (i.e., MCAR, MAR, and MNAR), small samples, and different levels of missing rates. All statistical inferences were investigated using latent variable interaction modeling. Consistent with the missing data literature, the combined effects of small sample sizes, higher missing rates, and non-ignorable missingness along with complicated modeling structure adversely affected the accuracy of statistical inferences. Although there is a possibility for overparameterization, it is a good way to select MI when convergence is concerned. If the primary goal of research is to investigate the relationship between variables as in many studies, ML would be attractive. MF presented similar performance compared to MI and ML across all research conditions and outperformed when estimating the variability of parameter estimates. Other practical issues pertaining to the missing data methods were also discussed.

关键词： Missing data Multiple imputation Maximum likelihood estimation random forest algorithm Latent interaction model

来源：评论

学校读者我要写书评

暂无评论

Fusion of complementary information of SAR and optical data for forest cover mapping using random forest algorithm

引用

CURRENT SCIENCE 2021年第1期120卷 193-199页

作者： Veerabhadraswamy, Naveen Devagiri, Guddappa M. Khaple, Anil Kumar Univ Agr & Hort Sci Shivamogga Coll Forestry Ponnampet 571216 Kodagu India

We developed a methodological framework for accurate forest cover mapping of Shivamogga taluk, Karnataka, India using multi-sensor remote sensing data. For this, we used Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data. These datasets were fused using principal component analysis technique, and forest and non-forest areas were classified using a random forest (RF) algorithm. Backscatter analysis was performed to understand the variation in gamma(0) values between forest and non-forest sample points. The average gamma(0) values of forest were higher than the non-forest samples in VH and VV polarizations. The average gamma(0) backscatter difference between forest and non-forest samples was 8.50 dB in VH and 5.64 dB in VV polarization. The highest classification accuracy of 92.25% was achieved with the multi-sensor fused data compared to the single-sensor SAR (78.75%) and optical (83.10%) data. This study demonstrates that RF classification of multi-sensor data fusion improves the classification accuracy by 13.50% and 9.15%, compared to SAR and optical data.

关键词： forest cover mapping multi-sensor data fusion principal component analysis remote sensing random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

Predicting open-plan office window operating behavior using the random forest algorithm

引用

JOURNAL OF BUILDING ENGINEERING 2021年 42卷

作者： Zhou, Xin Ren, Jiawen An, Jingjing Yan, Da Shi, Xing Jin, Xing Southeast Univ Sch Architecture Nanjing 210096 Jiangsu Peoples R China Beijing Univ Civil Engn & Architecture Sch Environm & Energy Engn Beijing 100084 Peoples R China Tsinghua Univ Sch Architecture Beijing 100084 Peoples R China Tongji Univ Key Lab Ecol & Energy Saving Study Dense Habitat Minist Educ Coll Architecture & Urban Planning Shanghai Peoples R China

Understanding window operating behavior in offices is important in terms of its influence on reducing energy consumption and improving indoor comfort. Researchers have applied different mathematical methods to develop useful window operating behavior models, however, the applicable machine learning algorithms are still in their preliminary research stage, requiring additional development. In the work described here, the authors applied the random forest (RF) algorithm to predict window operating behavior in open-plan offices, using data from three such offices in Nanjing, Jiangsu Province, China. The three open-plan offices were different in terms of their areas, office types, numbers of occupants, and layouts. The importance of various elements influencing window operating behavior was determined through the RF method, and the resulting rankings were consistent with the occupants' subjective understanding, as determined using a questionnaire. The sensitivity of the RF model to the number of inputs was explored, and showed that, with four inputs, its accuracy could reach 80%. The RF model also showed high accuracy and stability in predicting window operating behavior in two application formats-namely, for different offices, and for the same office over different years. Meanwhile, the RF model was compared with the other two popular machine learning methods, namely SVM and XGBoost algorithms, which also proves the high accuracy of RF models. The results obtained in this study should provide insights into applying machine learning methods to window operating behavioral studies generally-and may also inspire their application to other behavioral study types.

关键词： Behavior modeling Window operating behavior random forest algorithm Open-plan office Model verification

来源：评论

学校读者我要写书评

暂无评论

Quantitative expression of LNAPL pollutant concentrations in capillary zone by coupling multiple environmental factors based on random forest algorithm

引用

JOURNAL OF HAZARDOUS MATERIALS 2024年 479卷 135695页

作者： Han, Kexue Zuo, Rui Xu, Donghui Zhao, Xiao Shi, Jian Xue, Zhenkun Xu, Yunxiang Wu, Ziyi Wang, Jinsheng Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China Minist Educ Engn Res Ctr Groundwater Pollut Control & Remediat Beijing 100875 Peoples R China China Geol Survey China Inst Geol Environm Monitoring Beijing 100081 Peoples R China

The capillary zone plays a crucial role in migration and transformation of pollutants. Light nonaqueous liquids (LNAPLs) have become the main organic pollutant in soil and groundwater environments. However, few studies have focused on the concentration distribution characteristics and quantitative expression of LNAPL pollutants within capillary zone. In this study, we conducted a sandbox-migration experiment using diesel oil as a typical LNAPL pollutant, with the capillary zone of silty sand as the research object. The variation characteristics of LNAPL pollutants (total petroleum hydrocarbon) concentration and environmental factors (moisture content, electrical conductivity, pH, and oxidation-reduction potential) were essentially consistent at different locations with the same height. These characteristics differed within range of 10.0-50.0 cm and above 60.0 cm from groundwater. A model for quantitative expression of concentrations was constructed by coupling multiple environmental factors of 968 sets-7744 data via random forest algorithm. The goodness of fit (R2) for both training and test sets was greater than 0.90, and the mean absolute percentage error (MAPE) was less than 16.00 %. The absolute values of relative errors in predicting concentrations at characteristic points were less than 15.00 %. The constructed model can accurately and quantitatively express and predict concentrations in capillary zone.

关键词： Capillary zone random forest algorithm LNAPL pollutant concentration Environmental factors Quantitative expression

来源：评论

学校读者我要写书评

暂无评论

Application of random forest algorithm in Network Intrusion Detection of Government Affairs Departments

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2024年第0期

作者： Jiao, Meng State Informat Ctr Beijing 100038 Peoples R China

This paper reviews the current situation of intrusion detection and the commonly used random forest (RF) method, and confirms that support vector machine (SVM) and artificial neural network (ANN) are good methods, which are important components of building state network information security. Very complex and critical system technology. Appropriate security strategy must be based on the real needs of the government, guided by design principles, and establish a comprehensive and dynamic security system with technical management measures to truly protect government information security and maximize government efficiency. In this environment, we get the following conclusions: (1) In the performance test of different RF algorithms, the accuracy of the traditional RF algorithm is 83.4%, the security is 79.3%, and the correlation is 84.9%. In the data, we conclude that the high-dimensional clustering RF algorithm is the best in the performance test of different RF algorithms. (2) The security awareness and cognition of users also account for 6%, which shows that the security awareness of many users in government departments is not perfect, and the main reason for network intrusion in government departments is the intrusion of intruders and malicious code.

关键词： random forest algorithm government department network intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Investigating of the climatic parameters effectiveness rate on barley water requirement using the random forest algorithm, Bayesian multiple linear regression and cross-correlation function

引用

PADDY AND WATER ENVIRONMENT 2021年第1期19卷 137-148页

作者： Zarei, Abdol Rassoul Mahmoudi, Mohammad Reza Shabani, Ali Fasa Univ Dept Range & Watershed Management Nat Engn Fac Agr Fasa Iran Fasa Univ Fac Sci Dept Stat Fasa Iran Fasa Univ Coll Agr Sci Dept Water Engn Fasa Iran

Due to the pressure on water resources, especially in agricultural sectors, evaluation of the effective strategies on decrease in water consumption has an applicable and important role in field water management. In this research, the effectiveness rate of the 7 climatic parameters on the water requirement of barley (amount of potential evapotranspiration of barley (PETB) during the growth period) using the random forest algorithm (RF), Bayesian multiple linear regression (BR) and cross-correlation function (CCF) was investigated and prioritized. The results of this research can help the managers to control effective climatic variables on PETB. In this paper, the climatic data series of 8 stations with different climate conditions (with two replicates for each climate condition) in Iran during 1968-2017 was used. The results showed the linear regression of simulated PETB using AquaCrop model and predicted PETB using the RF and BR models had no difference with perfect reliable in 0.05 significant levels (T-Statistics varies from 0.010-0.103 and 0.000-0.842 in BR and RF models, respectively, and the R-square (R-2) between simulated and predicted PETB were significant in 0.01 levels at all stations (R(2)varies from 0.843-0.996 and 0.930-0.999 in BR and RF models, respectively;therefore, the RF and BR models had a good accuracy to predict PETB based on the climatic parameters and the accuracy of RF was more than BR model. The results indicated that based on the all statistical methods the wind speed had the most impact on the PETB (at 62.5% of stations) and based on the RF, BR and CCF methods the average of minimum temperature, the average of sunshine and the average of precipitation had the lowest impact on the PETB, respectively.

关键词： random forest algorithm Bayesian multiple linear regression AquaCrop model Cross-correlation function Water requirement Iran

来源：评论

学校读者我要写书评

暂无评论

Large group activity security risk assessment and risk early warning based on random forest algorithm

引用

PATTERN RECOGNITION LETTERS 2021年 144卷 1-5页

作者： Chen, Yanyu Zheng, Wenzhe Li, Wenbo Huang, Yimiao Zhejiang Normal Univ Coll Econ & Management Jinhua 321004 Zhejiang Peoples R China Changchun Univ Technol Sch Publ Adm Changchun 130012 Jilin Peoples R China

With the continuous development of artificial intelligence, machine learning, the necessary way to achieve artificial intelligence, is also constantly improving, of which deep learning is one of the contents. The purpose of this paper is to evaluate and warn the security risk of large-scale group activities based on the random forest algorithm. This paper uses the methods of calculating the importance of the random forest algorithm to variables and the calculation formula of the weight of the security risk index, and combining the model parameters of the random forest algorithm The optimization experiment and the random forest model training experiment are used for risk analysis, and the classification accuracy rate reaches a maximum of 0.86, which leads to the conclusion that the random forest algorithm has good predictive ability in the risk assessment of large-scale group activities. This article takes a certain international youth environmental protection festival as an example for analysis, and better verifies the feasibility and effectiveness of this article. (c) 2021 Elsevier B.V. All rights reserved.

关键词： random forest algorithm Large-scale group activities Security risk assessment Risk warning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：