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Crop phenology retrieval via polarimetric SAR decomposition and random forest algorithm

REMOTE SENSING OF ENVIRONMENT 2019年 231卷 111234-000页

作者： Wang, Hongquan Magagi, Ramata Goita, Kalifa Trudel, Melanie McNairn, Heather Powers, Jarrett Univ Sherbrooke Sherbrooke PQ Canada Agr & Agri Food Canada Ottawa ON Canada

Knowledge of crop phenology assists in making agricultural decisions such as appropriate irrigation and fertilization applications in order to optimize crop yield. The objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset, acquired during the Soil Moisture Active Passive (SMAP) Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The model-based and eigen-based polarimetric parameters are used to separate the vegetation and soil scattering contributions in the total radar signal. As the crop morphological shape and structure vary with phenological growth, our study assumes that the polarimetric parameters related to the volume scattering mechanism have the potential to track the crop phenology. The sensitivity of the polarimetric parameters to the ground identified crop phenology is analyzed for different crop types. For canola, a single polarimetric parameter is sufficient to characterize the crop phenology, due to the high volume scattering power and large temporal dynamic. For corn, soybean and wheat, combinations of multiple polarimetric parameters are required. For each crop type, the random forest algorithm trained using 60% of the data is used to retrieve the, crop phenology. Performances are compared to Artificial Neural Network, Support Vector Machine Regression and k-Nearest neighborhood algorithms. The random forest algorithm provides the best phenology retrieval with significant (p-value < 0.01) spearman correlation coefficients (between the retrieved and ground identified phenology) of 0.93, 0.90, 0.85 and 0.91 for canola, corn, soybean and wheat, respectively. While a single polarimetric parameter demonstrates limited sensitivity to corn phenology, the retrieved phenology from the random forest algorithm using multiple polarimetric parameters agrees well with the ground measurements. Furthermore, the importance

关键词： Crop phenology Polarimetric decompositions random forest algorithm SMAPVEX16-MB RADARSAT-2

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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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Multi-factor fuzzy prediction model of concrete surface chloride concentration with trained samples expanded by random forest algorithm

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MARINE STRUCTURES 2022年 86卷

作者： Guo, Zengwei Guo, Ruiqi Lin, Sen Chongqing Jiaotong Univ State Key Lab Mt Bridge & Tunnel Engn 66 Xuefu Ave Chongqing 400074 Peoples R China Chongqing Jiaotong Univ Sch Civil Engn 66 Xuefu Ave Chongqing 400074 Peoples R China

Although many factors affect the surface chloride concentration (Cs) of concrete structures in ocean exposure environments, establishing a quantitative prediction model that considers mul-tiple factors using traditional regression methods is still a challenge. Herein, a multi-factor hybrid model combining the advantages of the random forest algorithm and fuzzy logic system is pro-posed. This hybrid fuzzy logic system can achieve quantitative, continuous, and visual expres-sions of the influence law of various environmental and material factors on Cs. It has high precision in predicting time-dependent Cs with multiple factors considered. The calculation re-sults indicate that the stable value of Cs increases with increasing water-to-binder ratio (w/b). Nonetheless, when w/b is greater than a critical value, the increment in w/b no longer affects Cs. Nonetheless, w/b does not affect the time required for Cs to reach a steady state. Additionally, the mineral admixtures affect both the final stable value and required time. The service times required to achieve a stable value of Cs for ordinary cement concrete in the submerged, splash, and tidal zones are 2.75, 3, and 3.5 years, respectively. For concrete with identical material conditions, the stable value of Cs in the tidal zone is the highest, followed by that in the sub-merged and splash zones, and the atmospheric zone is the least. Finally, a predictive model of Cs, which is expressed using a multivariate function with piecewise coefficients, is proposed for quantitative predicting durability design and service life.

关键词： Surface chloride concentration Machine learning Hybrid prediction model random forest algorithm Fuzzy logic system

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

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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

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Passenger Travel Behavior in Public Transport Corridor After the Operation of Urban Rail Transit: A random forest algorithm Approach

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IEEE ACCESS 2020年 8卷 211303-211314页

作者： Li, Xiaofei Gao, Yueer Zhang, Huizhen Liao, Yanqing Huaqiao Univ Coll Architecture Xiamen 361021 Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

A competitive relationship has been generated between urban rail transit and bus transit since the operation of the former. Despite different roles in providing services in public transport corridor, they affect each other in actual situations. In terms of urban transportation planning and policy formulation, it is necessary to explore and master the rules of passengers' travel mode choice from different means. In order to study their choices between the rail transit and the bus transit after the operation of the former, taking Xiamen, China as an example, this article analyzed the overall travel features of passenger flow before and after the operation of rail transit by using the public transit IC card data from two consecutive weeks in November 2017 and November 2018. Some features of travel distance, travel time, travel cost, whether to travel in peak hours, the number of collinear stations between bus transit and rail transit or that of rail transit stations are sorted out. With random forest algorithm, a model is set up for the travel mode choice of passengers after urban rail transit is put into use to find out the impact of different travel features. The result shows that travel cost is the most crucial factor that affects passengers' decisions, followed by the number of collinear stations between bus transit and rail transit or that of rail transit stations, travel time and travel distance. Whether to travel in peak hours have less impact on their choices. This study is constructive for cities in the stage of facing competition between newly opened rail transit and bus transit and support transportation decision-making.

关键词： Travel mode choice feature importance after the operation of newly urban rail transit random forest algorithm

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AI Based Forecasting of Influenza Patterns from Twitter Information Using random forest algorithm

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2021年 11卷

作者： Shanmuganathan, Vimal Yesudhas, Harold Robinson Madasamy, Kaliappan Alaboudi, Abdulellah A. Luhach, Ashish Kr Jhanjhi, Noor Zaman Ramco Inst Technol Dept Comp Sci & Engn Rajapalayam Tamil Nadu India Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India Shaqra Univ Dept Comp Sci Shaqra Saudi Arabia Papua New Guinea Univ Technol Lae Papua N Guinea Taylors Univ Sch Comp Sci & Engn SCE Subang Jaya Malaysia

Nowadays, people are highly addicted to social media or any other social platform, and there is no one without the Internet or Android mobile devices. Therefore, social media is considered to be a large dataset repository. For gathering information on the Internet life, Twitter is one of the largest data repositories where users can share information through tweets (#Hashtags). Early detection of influenza through Twitter information enables a timely response to an influenza pandemic. This paper proposes a machine learning methodology to detect the flu (influenza) virus spreading among people mainly across Asia. The proposed work applies a classification based on the random forest algorithm, a regulated AI strategy for analyzing the text data in the dataset and finding the accuracy level based on its classification. The computed matrix through the proposed technique is used to predict the accuracy with the training and responses using substitution function. The bootstrapping system utilizes the execution model to estimate the tree-based clustering model. The Twitter dataset was collected with many positive tweets, and the text cleaning was enabled with various classifiers to enhance the prediction. Subsequently, the text analysis processing was carried out using multiple plots, such as box plot, scatter plot, lexical dispersion plot, term frequency, and plotting. The proposed system uses the concept of geospatial data analytics for predicting the flu-affected region. Further, the proposed system enhanced the accuracy level of the flu prediction in the given dataset.

关键词： Tweetluenza-Flu Prediction Social Media Content Mining Classification random forest algorithm Geospatial Data Analytics

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Prediction of ground vibrations induced by bench blasting using the random forest algorithm

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JOURNAL OF THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY 2023年第3期123卷 123-132页

作者： Dzimunya, N. Besa, B. Nyirenda, R. Univ Zimbabwe Harare Zimbabwe Univ Zambia Lusaka Zambia

The accurate estimation of peak particle velocity (PPV) is crucial during the design of bench blasting operations in open pit mines, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and other structures. Conventional models used to predict blast-induced vibrations are not capable of capturing nonlinear relationships between the different blasting-related parameters. Soft computing techniques, i.e., techniques that are founded on the principles of artificial intelligence, effectively model these complexities. In this paper, we use the random forest (RF) algorithm to develop a model to predict blast-induced ground vibrations from bench blasting using 48 data records. The model was trained and tested using WEKA datamining software. To build this model, a feature selection process using several combinations of Attribute Evaluators and Search Methods under the WEKA Select Attributes tab was performed. The correlation coefficient of the actual data and RF model-predicted data was 0.95, and the weighted average of the relative absolute error (RAE) was 10.9%. The RF model performance was also compared to the equivalent-path-based (EPB) equation on the testing data-set, and it was seen that the RF model can effectively be used to predict PPV. The study also demonstrates that the EPB equation is a suitable empirical method for predicting PPV.

关键词： Blast-induced ground vibrations peak particle velocity random forest algorithm EPB equation

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Recognizing protein-metal ion ligands binding residues by random forest algorithm with adding orthogonal properties

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年第0期98卷 107693页

作者： You, Xiaoxiao Hu, Xiuzhen Feng, Zhenxing Wang, Ziyang Hao, Sixi Yang, Caiyun Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China Neural Network Modeling Inner Mongolia Key Lab Stat Anal Theory Life Data Hohhot Peoples R China

Accurately identifying protein-metal ion ligand binding residues is the key to study protein functions. Because the number of binding residues and non-binding residues is significantly imbalanced, false positives is hard to be eliminated from the binding residues prediction result. Therefore, identification of protein-metal ion ligand binding residues remains challenging. In this paper, the binding site of 7 metal ions (Ca2+, Mg2+, Zn2+, Fe3+, Mn2+, Cu2+ and Co2+) were used as the objects of the study. Besides generally adopted parameters: amino acids and predicted secondary structure information, we creatively introduced ten orthogonal properties as a parameter. These orthogonal properties are clustering of 188 physical and chemical characteristics that can be used to describe three-dimension structural information. With the optimized parameters, we used the random forest algorithm to predict ion ligand binding residues. The proposed method obtained good prediction results with the MCC values of Mg2+, Ca2+ and Zn2+ reaching 0.255, 0.254, 0.540, respectively. Comparing to the IonSeq method, the method developed in this paper has advantages on the binding residues prediction of some ions.

关键词： Metal ion ligands Binding residues Ten orthogonal properties random forest algorithm

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Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm

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ENERGY CONVERSION AND MANAGEMENT 2022年 264卷 1页

作者： Fang, Yi Ma, Li Yao, Zhiyi Li, Wangliang You, Siming Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Nanjing Univ Aeronaut & Astronaut Natl Key Lab Rotorcraft Aeromech Nanjing 210016 Peoples R China CBE Ecosolut Pte Ltd Singapore 117602 Singapore Chinese Acad Sci Inst Proc Engn CAS Key Lab Green Proc & Engn Beijing 100190 Peoples R China

Gasification technologies have been extensively studied for their potential to convert biomass feedstocks into syngas (a mixture of CH4, H-2, and CO mainly) that can be further turned into heat or electricity upon combustion. It is crucial to understand optimal gasification process parameters for practical design and operation for maximizing the potential. This study combined the Monte Carlo simulation approach, gasification kinetic modeling, and the random forest algorithm to predict the optimal gasification process parameters (i.e. water content, particle size, porosity, thermal conductivity, emissivity, shape, and reaction temperature) towards a maximum syngas yield. The Monte Carlo approach randomly generated a data pool of the process parameters following either a normal or uniform distribution, which was then fed into a validated kinetic model to create 2,000 datasets (process parameters and syngas yields). For the random forest model, the mean decrease accuracy and mean decrease Gini were used to assess the importance of the process parameters on syngas yields. The accuracy of the optimization method was evaluated using the coefficient of determination (R-2), the root means square error (RMSE), and the mean absolute error (MAE). Generally, the predictions for the normal distribution case were closer to the experimental data obtained from existing literature than that for the uniform distribution case. The model was used to predict the optimal syngas yield and process parameters of wood gasification and it was shown that the predictions were generally in good agreement (<12% difference for the case of normal distribution) with existing experimental results. The method serves as a useful tool for determining optimal gasification process parameters for process and operation design.

关键词： Biomass gasification Kinetic model Monte Carlo simulation Machine-learning random forest algorithm

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