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

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

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

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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

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

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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

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Automatic identification method for fraudulent users on e-commerce websites based on random forest algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第2期25卷 1147-1154页

作者： Zhai, Jingbo Shanghai Sipo Polytech Sch Int Business & Management 1408 Chengnan Rd Shanghai 201399 Peoples R China

With the rapid development of e-commerce, online fraud is increasing, seriously threatening the interests of users and the reputation of e-commerce platforms. In order to effectively identify and prevent fraudulent users on e-commerce websites, a research proposes an automatic identification method on the basis of random forest algorithm. It achieves automatic detection of fraudulent behavior by analyzing the behavioral characteristics and user activity patterns of the website. The research results demonstrated that the accuracy on the experimental dataset is as high as 98.76%, with a recall rate of 97.53%, which is much higher than other methods and demonstrates extremely high recognition performance. This method can also effectively identify different types of fraudulent behavior, with a recognition rate of over 98%. This result further confirms the advantages of the random forest algorithm in dealing with imbalanced datasets and complex classification problems. Research can help combat online fraud and promote the healthy development of the e-commerce industry.

关键词： random forest algorithm electronic commerce website fraud automatic recognition URL

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

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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

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A Comparative Study of Oil-Water Two-Phase Flow Pattern Prediction Based on the GA-BP Neural Network and random forest algorithm

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PROCESSES 2023年第11期11卷 3155页

作者： Sun, Yongtuo Guo, Haimin Liang, Haoxun Li, Ao Zhang, Yiran Zhang, Doujuan Yangtze Univ Coll Geophys & Petr Resources Wuhan 430100 Peoples R China Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China Sinopec Shengli Oilfield Co Res Inst Explorat & Dev Dongying 257000 Peoples R China

As global oil demand continues to increase, in recent years, countries have continued to expand the development of oil reserves, highlighting the importance of oil. In order to adapt to different strata distribution conditions, domestic drilling technology is becoming more and more perfect, resulting in a gradual increase in horizontal and inclined wells. Because of the influence of various downhole factors, the flow pattern in the wellbore will be more complex. Accurately identifying the flow pattern of multiphase flow under different well deviation conditions is very important to interpreting the production log output profile accurately. At the same time, in order to keep up with the footsteps of artificial intelligence, big data and artificial intelligence algorithms are applied to the oil industry. This paper uses the GA-BP neural network and random forest algorithm to conduct fluid flow pattern prediction research on the logging data of different water cuts at different inclinations and flow rates. It compares the predicted results with experimental fluid flow patterns. Finally, we can determine the feasibility of these two algorithms for predicting flow patterns. We use the multiphase flow simulation experiment device in the experiment. During the process, the flow patterns are observed and recorded by visual inspection, and the flow pattern is distinguished by referring to the theoretical diagram of the oil-water two-phase flow pattern. The prediction results show that the accuracy of these two algorithms can reach 81.25% and 93.75%, respectively, which verifies the effectiveness of these two algorithms in the prediction of oil-water two-phase flow patterns and provides a new idea for the prediction of oil-water two-phase flow patterns and other phases.

关键词： inclined well horizontal well vertical well GA-BP neural network random forest algorithm flow pattern prediction

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The TPRF: A Novel Soft Sensing Method of Alumina-Silica Ratio in Red Mud Based on TPE and random forest algorithm

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PROCESSES 2024年第4期12卷 663页

作者： Meng, Fanguang Shi, Zhiguo Song, Yongxing Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310013 Peoples R China Zhejiang JingLiFang Digital Technol Grp Co Ltd Hangzhou 310012 Peoples R China Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Compressor Technol Lab Anhui Prov State Key Lab Compressor Technol Hefei 230031 Peoples R China

The online measurement of the aluminum-silicon ratio of red mud in the dissolution stage of the Bayer alumina production process is difficult to achieve. The offline assay method has a high cost and strong time delay. Soft sensors are an effective and economical method to solve such problems. In this paper, a hybrid model (TPRF model) based on a tree-structured Parzen estimator (TPE) optimized random forest (RF) algorithm is proposed to measure the Al-Si ratio of red mud. The probability distribution of the hyperparameters of the random forest model is estimated by combining the TPE optimization algorithm with the random forest algorithm. According to this probability distribution, the hyperparameters of the random forest algorithm are adjusted in the parameter search space to obtain the best combination of hyperparameters. We established a TPRF soft sensing model based on the optimal combination of hyperparameters. The results show that the best performance of the TPRF model is a mean absolute percentage error (MAPE) of 0.0015, a root-mean-square error (RMSE) of 0.00378, a mean absolute error (MAE) of 0.00162, and a goodness of fit (R2) of 0.9893. The goodness of fit improved by 93.2% compared to the linear model, 39.1% compared to the SVR model, about 21.2% compared to the GRU model, and 5.5% compared to the RF model. This level of performance is demonstrated to be better than traditional soft sensors.

关键词： TPE algorithm random forest algorithm soft sensor alumina-silica ratio

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Flight Test Analysis of UTM Conflict Detection Based on a Network Remote ID Using a random forest algorithm

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DRONES 2023年第7期7卷

作者： Ruseno, Neno Lin, Chung-Yan Guan, Wen-Lin Natl Formosa Univ Dept Power Mech Engn Huwei 632301 Taiwan Natl Formosa Univ Dept Aeronaut Engn Huwei 632301 Taiwan Taiwan Transportat Safety Board New Taipei City 231 Taiwan

In an area where unmanned aerial system (UAS) traffic is high, a conflict detection system is one of the important components for the safety of UAS operations. A novel UAS traffic management (UTM) monitoring application was developed, including a conflict detection system using the inverted teardrop area detection based on real-time flight data transmitted from the network remote identification (Remote ID) modules. This research aimed to analyze the performance of the UTM-monitoring application based on flight test data using statistical and machine learning approaches. The flight tests were conducted using several types of small fixed-wing unmanned aerial vehicles (UAVs) controlled by a human pilot using a Taiwan cellular communication network in suburban and rural areas. Two types of scenarios that involved a stationary, on-the-ground intruder and a flying intruder were used to simulate a conflict event. Besides the statistical method calculating the mean and standard deviation, the random forest algorithm, including regressor and classifier modules, was used to analyze the flight parameters and timing parameters of the flight tests. The result indicates that the processing time of the UTM application was the most significant parameter to the conflict warning parameter, besides the relative distance and height between UAVs. In addition, the latency time was higher for the flight in the rural area than the suburban area and also higher for data transmitted from the flying position than the ground position. The findings of our study can be used as a reference for aviation authorities and other stakeholders in the development of future UTM systems.

关键词： UAS traffic management conflict detection network remote ID inverted teardrop algorithm communication latency flight test random forest algorithm

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