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Synergy of multi-temporal polarimetric SAR and optical image satellite for mapping of marsh vegetation using object-based random forest algorithm

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ECOLOGICAL INDICATORS 2021年 131卷 108173-108173页

作者： Fu, Bolin Xie, Shuyu He, Hongchang Zuo, Pingping Sun, Jun Liu, Lilong Huang, Liangke Fan, Donglin Gao, Ertao Guilin Univ Technol Coll Geomat & Geoinformat 12 Jiangan St Guilin 541004 Peoples R China

The accurate classification of marsh vegetation is an important prerequisite for wetland management and pro-tection. In this study, the Honghe National Nature Reserve was used as the research area. The VV and VH polarized backscattering coefficients of Sentinel-1B, the polarimetric decomposition parameters of Sentinel-1B, and Sentinel-2A multi-spectral images from June and September were selected to construct 18 multi-dimensional data sets. A highly correlated variable elimination algorithm, a recursive feature elimination vari-able selection algorithm (RFE-RF), and an optimized random forest algorithm (RF) were used to construct a marsh vegetation identification model. In this study, we searched for an RF model to achieve the accurate classification of marsh vegetation and find the best feature for identifying various types of vegetation. Addi-tionally, the applicability of different optimized RF models to the task of the identification of wetland vegetation and the stability of the identification of marsh vegetation using different classification models were quantita-tively analyzed. The results show the following: (1) RFE-RF variable selection and RF parameter optimization can reduce the data dimensionality, improve the accuracy and stability of the wetland vegetation classification model, and achieve a training accuracy of up to 85.39%. (2) The RF model integrating multi-spectral data, backscattering coefficients, and polarimetric decomposition parameters for June and September can obtain the highest overall accuracy (91.16%), and the model has the strongest applicability. (3) The importance of multi-spectral variables in wetland vegetation classification is higher than that of backscattering coefficients and polarimetric decomposition parameters. The visible bands and vegetation index are the most important vari-ables, while the cross-polarized backscattering coefficient (Mean_VH), polarimetric decomposition eigenvalue (Mean_l1, Mean_l2), and calculated eigenva

关键词： Marsh vegetation classification Backscattering coefficient Polarimetric decomposition parameters Multi-scale inheritance segmentation Variable selection random forest algorithm

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Application of random forest algorithm in hail forecasting over Shandong Peninsula

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ATMOSPHERIC RESEARCH 2020年 244卷 105093-105093页

作者： Yao, Han Li, Xiaodong Pang, Huaji Sheng, Lifang Wang, Wencai Qingdao Municipal Peoples Govt Weather Modificat Off Qingdao 266003 Peoples R China Qingdao Meteorol Disaster Prevent Engn Technol Re Qingdao 266003 Peoples R China Ocean Univ China Coll Ocean & Atmospher Sci Qingdao 266100 Peoples R China Ocean Univ China Key Lab Phys Oceanog Qingdao 266100 Peoples R China

To improve the accuracy of hail forecasting, this study applies the random forest (RF) algorithm in hail identification and prediction in Shandong Peninsula. Hail observation data of 41 meteorological stations in Shandong Peninsula from 1998 to 2013 are used. The hail forecasting model with a 0-6 h range based on the RF algorithm is constructed using the convection index and related physical quantities calculated by the reanalysis data of the European Centre for Medium-Range Weather Forecasts during the same period. The model is built by undersampling within the RF algorithm (balanced RF), and the cross-validation is adopted to select the optimal forecast probability. The cross-validation exhibits high simulation accuracy, stable fitting effect, and small average generalization error. The performance of the balanced RF is tested by the independent data samples from 2014 to 2018, which shows excellent results. A trial report on the weather process on 13 June 2018 shows that the model is effective in identifying hail-fall areas and capable of forecasting all hail stations and the occurrence time of hail disasters. The RF algorithm focuses on thermal factors. The physical meaning of the selected factors is clear and consistent with the subjective prediction. The thresholds of the thermal factors, such as the lifted index, Showalter stability index, and total index, can be utilized as a reference for hailstorm prediction over Shandong Peninsula.

关键词： Hail forecast random forest algorithm Meteorological factors

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A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1110页

作者： Yuan, Ke Yu, Daoming Feng, Jingkai Yang, Longwei Jia, Chunfu Huang, Yiwang Henan Univ Sch Comp & Informat Engn Kaifeng Henan Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng Henan Peoples R China Henan Univ Int Educ Coll Zhengzhou Henan Peoples R China Nankai Univ Coll Cybersecur Tianjin Peoples R China Tongren Univ Sch Data Sci Tongren Guizhou Peoples R China

Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

关键词： Cryptographic algorithm identification Machine learning randomness test random forest algorithm K-nearest neighbor algorithm

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Utilizing random forest algorithm for early detection of academic underperformance in open learning environments

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1708页

作者： Balabied, Shikah Abdullah Albriki Eid, Hala F. Univ Bisha Coll Educ & Human Dev Dept Qual Life & Continuing Educ Bisha Saudi Arabia Univ Bisha Appl Coll Dept Business Adm Bisha Saudi Arabia

Background: One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and ***: The specific objective of this study was to build a model that identifies atrisk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or ***: The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed.

关键词： Open learning environments random forest algorithm OULAD MOOCs Learning analytics

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

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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

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Feature Engineering of Geohazard Susceptibility Analysis Based on the random forest algorithm: Taking Tianshui City, Gansu Province, as an Example

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REMOTE SENSING 2022年第22期14卷 5658-5658页

作者： Ling, Xiao Zhu, Yueqin Ming, Dongping Chen, Yangyang Zhang, Liang Du, Tongyao China Univ Geosci Beijing Sch Informat Engn 29 Xueyuan Rd Beijing 100083 Peoples R China Minist Emergency Management Natl Inst Nat Hazards Beijing 100085 Peoples R China China Aero Geophys Survey & Remote Sensing Ctr Na Beijing 100083 Peoples R China

In this paper, Feature Engineering (FE) was applied to Landslide Susceptibility Mapping (LSM), while the most suitable conditioning feature dataset and analysis method were tested and analyzed. Tianshui city was taken as the study area, three types of geohazard (collapse, landslide, and unstable slopes) were used, while a total of twenty-three conditioning features were generated;two dimensionless methods (normalization and standardization) were tested afterward. Four random-forest-based (RF-based) feature selection methods using different indicators (Gini Impurity, GI;Out of Bag Accuracy, OOBA) were proposed and tested separately. The LSMs of four models were carried out under the guidance results of FE, namely Classification and Regression Tree (CART), random forest (RF), Logistic Regression (LR), and Support Vector Machine for Classification (SVC). For feature enhancement, standardization had significant advantages over normalization. All RF-based methods were proven effective, lifting the AUC by 0.01 similar to 0.02. The RF model achieved the highest LSM accuracies, respectively, 0.949 (landslide), 0.957, and 0.949 (unstable slopes), improved by 0.008 (landslide), 0.005 (collapse), and 0.013 (unstable slopes). This proved that the FE helped to improve LSM and can help to decide the dominant conditioning factors for regional geohazards.

关键词： landslide feature engineering landslide susceptibility mapping random forest algorithm

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Hybrid Approach to Improving the Results of the SVM Classification Using the random forest algorithm

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Procedia Computer Science 2019年 150卷 455-461页

作者： L.A. Demidova I.A. Klyueva A.N. Pylkin Ryazan State Radio Engineering University 59/1 Gagarina Str. Ryazan 390005 Russia

The aim of this work is to improve the results of the SVM classification (Support Vector Machine) by hybridizing the SVM classifier with the random forest classifier (random forest, RF) used as the auxiliary. Specification of the classification decisions obtained on the basis of the SVM classifier is performed for the objects located in the experimentally determined subareas near the hyperplane separating the classes and including both correctly and erroneously classified objects. In the case of improving the quality of the objects classification from the initial dataset, the proposed hybrid approach to the objects classification can be recommended for classification of new objects. When developing the SVM classifier, the fixed default parameters values are used. A comparative analysis of the classification results obtained during the computational experiments in the hybridization of the SVM classifier with two auxiliary classifiers – the random forest classifier (RF classifier) and the k nearest neighbor classifier ( k NN classifier), for which the parameters values are determined randomly, confirms the expediency of using of these classifiers to increase the SVM classification quality. It was found that in most cases, the random forest classifier works better in terms of improving the SVM classification quality in comparison with the k NN classifier.

关键词： intelligent classification algorithm SVM algorithm k -nearest neighbors algorithm random forest algorithm support vector kernel function kernel function parameter regularization parameter hybridization

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Detection of DNS DDoS Attacks with random forest algorithm on Spark

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Procedia Computer Science 2018年 134卷 310-315页

作者： Liguo Chen Yuedong Zhang Qi Zhao Guanggang Geng ZhiWei Yan School of Computer and Control University of Chinese Academy of Sciences Zhongguancun Nansijie Haidian District Beijing 100190 China China Computer Network Information Center Zhongguancun Nansijie Haidian District Beijing 100190 China

Domain Name System(DNS) is one of the most foundational and essential services on the Internet, the security and robustness of DNS are of great significance. However, the stable operation of DNS has been threatened by Distributed Denial of Service(DDoS) for quite a long time, especially when the number of registered names of. CN are over 20 million on November 11, 2016. According to our observation, the frequency of volume-based DDoS attacks increased rapidly in recent years, and when the attack happened, not only the authoritative servers were affected, servers of Top Level Domain(TLD) also suffered a lot. In this paper, a model based on random forest [1] is applied to traffic classification with an accuracy of 99.2% on Spark. The result shows that the model could be used to deal with large-scale DNS query flows, which is fast enough to be used in practice.

关键词： DNS DDoS Traffic Filtering random forest algorithm Spark

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

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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

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An Optimized Object-Based random forest algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data

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REMOTE SENSING 2020年第8期12卷 1270-1270页

作者： Lou, Peiqing Fu, Bolin He, Hongchang Li, Ying Tang, Tingyuan Lin, Xingchen Fan, Donglin Gao, Ertao Guilin Univ Technol Coll Geomat & Geoinformat 12 Jiangan St Guilin 541004 Peoples R China Chinese Acad Sci Res Ctr Remote Sensing & Geosci Northeast Inst Geog & Agroecol 4888 Shengbei St Changchun 130102 Peoples R China

Discriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (mtry and ntrees) of an object-based random forest (RF) algorithm to improve the applicability of marsh vegetation classification. Multidimensional datasets were used as the input variables for model training, then variable selection was performed on the variables to eliminate redundancy, which improved classification efficiency and overall accuracy. Finally, the performance of a new generation of Chinese high-spatial-resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images for marsh vegetation classification was evaluated using the improved object-based RF algorithm with accuracy assessment. The specific conclusions of this study are as follows: (1) Optimized object-based RF classifications consistently produced more than 70.26% overall accuracy for all scenarios of GF-1 and ZY-3 at the 95% confidence interval. The performance of ZY-3 imagery applied to marsh vegetation mapping is lower than that of GF-1 imagery due to the coarse spatial resolution. (2) Parameter optimization of the object-based RF algorithm effectively improved the stability and classification accuracy of the algorithm. After parameter adjustment, scenario 3 for GF-1 data had the highest classification accuracy of 84% (ZY-3 is 74.72%) at the 95% confidence interval. (3) The introduction of multidimensional datasets improved the overall accuracy of marsh vegetation mapping, but with many redundant variables. Using three variable selection algorithms to remove redundant variables from the multidimensional datasets effectively improved the classification efficiency and overall accuracy. The recursive feature elimination (RFE)-based variable selection algorithm had the best performance. (4) Optical spectral bands, spectral indices, mean value of gree

关键词： marsh vegetation mapping random forest algorithm parameter optimization multidimensional datasets variable selection GF-1 ZY-3 Northeast China

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