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Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the random forest algorithm

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2020年第0期87卷 102032-000页

作者： Li, Huapeng Zhang, Ce Zhang, Shuqing Atkinson, Peter M. Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130012 Peoples R China Univ Lancaster Lancaster Environm Ctr Lancaster LA1 4YQ England Ctr Ecol & Hydrol Lib Ave Lancaster LA1 4AP England Univ Lancaster Fac Sci & Technol Lancaster LA1 4YR England

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the random forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in struc

关键词： Crop classification Multitemporal SAR imagery Polarimetric SAR random forest algorithm UAVSAR

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Financial Data Sharing System for State-owned Enterprises based on random forest algorithm

Financial Data Sharing System for State-owned Enterprises ba...

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2022年第六届国际科技创新与教育发展学术会议

作者： Xiaotao Chang University of San Carlos

With the development of social science and technology, information technology has been gradually applied to various fields and played a key role in the development of enterprises, while as a state-owned enterprise, under the background of the continuous development of information technology, the business involved has become more complex and its scale has been increasing, and the scope of application of the financial data sharing system for state-owned enterprises is also expanding. At the same time, the requirements of enterprise managers for the timeliness, comprehensiveness and accuracy of financial information are subsequently increasing, and the traditional financial management can no longer meet the current needs. Therefore, it is necessary to promote the information construction of local state-owned enterprises in financial management, improve the level of financial information management, enhance the competitiveness of state-owned enterprises, and stabilize the position of state-owned enterprises in the market. The random forest algorithm can be used in the financial data sharing system of state-owned enterprises.

关键词： random forest algorithm State-owned enterprises Financial data Sharing system

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Research on Non-Intrusive Load Monitoring Based on random forest algorithm 4

Research on Non-Intrusive Load Monitoring Based on Random Fo...

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4th International Conference on Smart Grid and Smart Cities (ICSGSC)

作者： Wu Guohua Yuan Diping Yin Jiyao Zeng Wenhua Deng Peng Xiao Yiqing Harbin Inst Technol Shenzhen 518000 Peoples R China Shenzheng Urban Publ Safety & Technol Inst Shenzhen 50080 Guangdong Peoples R China

ISBN: (数字)9781728194042

ISBN: (纸本)9781728194042

It is of great significance for load monitoring to monitor illegal use of electricity. Load monitoring can provide supervision for government and improve residents' awareness of safety. Compared with the intrusive load monitoring, non-intrusive load monitoring has the advantages of good economy, high reliability, and quickly realizing the electricity decomposition. Many scholars have carried out this research, but there still exists the problems: it is difficult to obtain key information from big data and the diagnostic results are inaccuracy. Therefore, load monitoring is conducted in this paper. To overcome the above shortcomings, firstly this paper obtains key information of sample based on harmonic analysis. Secondly, random forest based on multiple decision trees has better accuracy in recognition. So the random forest algorithm is applied to machine learning and pattern recognition. Finally, the proposed method is identified and analyzed. The results show that the accuracy of the online electrical detection based on the harmonic analysis and random forest algorithm is greater than 86%, which shows the effectiveness of the method.

关键词： non-intrusive load monitoring harmonic analysis random forest algorithm

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Research on Fault Determination of Active Distribution Network Based on random forest-SVM algorithm 3rd

Research on Fault Determination of Active Distribution Netwo...

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3rd International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Zhao, Wei Xiao, Shiwu North China Elect Power Univ Sch Elect Power & Elect Engn State Key Lab New Energy Beijing Peoples R China

ISBN: (纸本)9789819905553;9789819905539;9789819905522

After the distributed generation supply is connected to the traditional distribution network, the traditional distribution network changes from "passive" to "active", and the power flowchanges from one-way to two-way. When the active distribution network fails, the short-circuit current is small, inconspicuous fault characteristics and rich harmonics make it difficult to locate faults. In this paper, a method for locating fault sections of active distribution network based on random forest-SVM algorithm is proposed. The method first performs fast Fourier transform on the voltage information collected at each measurement point to form the fundamental frequency voltage of each measurement point. And the fault feature set of the 2-7th harmonic voltage, the feature importance evaluation and feature selection of each fault feature are carried out by the random forest algorithm, and finally the fault section is located by the SVMalgorithm. By connecting distributed generation and nonlinear loads in the IEEE33 node distribution network, the simulation results show that the method has rapidity, accuracy and fault tolerance for fault section location.

关键词： Component Active distribution network Fault location location random forest algorithm SVM 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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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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Damage Evaluation Method of CFRP Structures Based on PCA and random forest algorithm

Damage Evaluation Method of CFRP Structures Based on PCA and...

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Chinese Automation Congress (CAC)

作者： Lu, Shizeng Li, Qiangya Yu, Hongliang Wang, Xiaohong Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China

ISBN: (纸本)9781728176871

The accurate and timely assessment of carbon fiber reinforced plastics (CFRP) structure damage is very important for ensuring the safety of aviation and aerospace composite structures. This paper studied the CFRP damage evaluation method by using principal component analysis (PCA) and random forest algorithm. First, the structure was excited to obtain the dynamic response signal. Thus, the frequency characteristics were obtained. Then, PCA was used to extract the damage feature. On this basis, the structural damage evaluation model based on random forest was established. Finally, the damage assessment experiment system of composite structures was established and experimentally verified. The testing result showed that the proposed method by using principal component analysis and random forest algorithm is effective.

关键词： damage evaluation method CFRP structures principal component analysis random forest algorithm

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Wind Power Forecasting Using Parallel random forest algorithm 8th

Wind Power Forecasting Using Parallel Random Forest Algorith...

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8th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Natarajan, V. Anantha Kumari, N. Sandhya Sree Vidyanikethan Engn Coll Dept Comp Sci & Engn Tirupati Andhra Pradesh India

ISBN: (纸本)9789811500350;9789811500343

Wind power keeps on developing and it is broadly observed as the sustainable power source best capable to compete with fossil fuel electricity generation. In the near past wind power forecasting has improved the situation for the estimation of power production in wind farms. In general wind power forecasts are generated in two different ways one namely using Numerical Weather Prediction (NWP) and the other one using physical forecasting methods. Physical forecasting is deeply dependent on meteorological facts and the data from the NWP. The approach of physical method is vulnerable by the way that wind speeds are estimated a few feet over the ground can fluctuate. Statistical scheme encompasses models likewise ANN, SVM, and etc. less reliant on accuracy of Numerical Weather Predictions (NWP), yet relies more extremely on historical information of wind speed at respective areas. To compute large accurate wind energy forecast a good amount of real-time observations of historical observations from the wind farms becomes essential. Wind power forecasts using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) suffers from slow training speed, and poor generalization ability. This paper aims at conducting experiments to assess the performance and test the suitability of the random forest algorithm for wind power forecasting. The prediction results are seeming to be close with the actual wind power generated at the wind farms and it is more accurate when compared to the results of the ANN.

关键词： Wind power forecasting Numerical weather predictions random forest algorithm Support vector machines Artificial neural networks

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Predicting Video Website DAU by random forest algorithm

Predicting Video Website DAU by Random Forest Algorithm

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IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)

作者： Dai, Jianhua Guo, Xiao Jin, Libo Commun Univ China Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781728181172

With the rise of major video websites, the scale of video website users is expanding. Users are the core of all video websites, so it is of great significance to study video website DAU to evaluate and guide its operation. Based on the data of a video website DAU and platform layout from January 2017 to December 2018, this article analyzes the influencing factors of the video website DAU, selects 10 influencing factors from broadcasting arrangement, content level, content mode, and winter and summer vacations, constructs a random forest model, makes regression prediction on the daily average DAU of the video website and calculates the importance of 10 variables. The results show that the most important predictor of DAU is the self-produced drama, followed by the number of variety shows S-level, variety shows A-level and TV series. Video websites can use the method to predict DAU, and improve their operation from the above predictors.

关键词： video websites random forest algorithm DAU

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Analysis of Precision Agriculture based on random forest algorithm by using Sensor Networks 5

Analysis of Precision Agriculture based on Random Forest Alg...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Pavithra, K. Jayalakshmi, M. Natl Engn Coll Comp Sci & Engn Kovilpatti Tamil Nadu India

ISBN: (纸本)9781728146850

India is the largest country where most of the human lives depends upon agriculture. Due to the shortage of water facilities, the yield of irrigation is affected. To reduce this shortage of water, different types of methods are invented to supply the correct level of water to the agricultural lands. Here precision irrigation system is used to maintain the perfect water supply from this method wastage of water can be reduced. By using this technique, we can achieve better harvesting results. The total amount of water for lands and plants can be calculated by using the PH value. To detect the PH level, a random forest algorithm is used. There is a direct relationship between the number of plants in the land and the results it can get. It improves the prediction accuracy.

关键词： Irrigation Precision Agriculture random forest algorithm Water Prediction

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