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Salt Land classification based on pansharpening images using random forest algorithm

JOURNAL OF SPATIAL SCIENCE 2024年第2期69卷 621-647页

作者： Diastarini, Diastarini Virtriana, Riantini Harto, Agung Budi Bandung Inst Technol Fac Earth Sci & Technol Geodesy & Geomat Engn Bandung Indonesia

The analysis and policymaking of national salt production require quality Salt Land maps with more detailed object information. This study successfully classified Salt Land into Active and Inactive based on pansharpening images using random forest algorithm. The results show: 1) the best pansharpening method is UNB PanSharp;2) the texture variables have more significant contributions to enhance classification accuracy;3) scheme 5 is the most efficient and recommended scheme in research, achieves an OA of 66.699% and a Kappa coefficient of 39.4%. This study can provide a good reference for developing Salt Land mapping and policies for practitioners or stakeholders.

关键词： Salt Land pansharpening random forest algorithm

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Research on Prediction of Dam Seepage and Dual Analysis of Lag-Sensitivity of Influencing Factors Based on MIC Optimizing random forest algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2023年第2期27卷 508-520页

作者： Liu, Yongtao Zheng, Dongjian Wu, Xin Chen, Xingqiao Georgakis, Christos T. Qiu, Jianchun Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing 210098 Peoples R China Aarhus Univ Dept Civil & Architectural Engn DK-8000 Aarhus C Denmark Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225009 Peoples R China

The seepage of the dam is an important representation of the operation characteristics of the dam, and there are many factors affecting the seepage with a certain lag. It is still difficult to predict its change and sensitivity because of complex operating conditions. At present, the lag-sensitivity of influence factors of the dam seepage has not been studied. The time series influence factors of seepage are determined by HTRT (hydrostatic-thermal-rainfall-time) model in this paper. To avoid the pseudo fitting of conventional methods, HTRT model nested random forest algorithm is used to establish the predicting model of the dam seepage. And MIC algorithm is used to achieve the dual purposes of time lag and sensitivity analysis. Firstly, the time lag of relationship between seepage and its influencing factors is characterized, and the most appropriate lag time of the HTRT model factors is determined. Secondly, independent correlation analysis on all influencing factors is carried out and the sensitivity of each factor is analyzed by MIC. Meanwhile, the sensitivity of the factors to seepage is quantitatively analyzed by the two parameters of %IncMSE and IncNodePurity of RF algorithm. The sensitivity of influencing factors is analyzed by comparing MIC results with RF results. Combined with the case, taking the error of fitting prediction as the evaluation index of seepage prediction, the prediction accuracy of MIC-RF model, RF model and MIC-BPNN (Back Propagation neural network) model is calculated and compared. Case study showed that MIC- RF monitoring model has high prediction accuracy, strong adaptability and high robustness in dam seepage, and the sensitivity and time lag of influencing factors can be quantitatively analyzed. The water pressure and rainfall of the lag time are 14 days and 16 days respectively. The sensitivity study of the time series influencing factors of seepage showed that the water pressure component is the main controlling factor of seepage, a

关键词： Seepage prediction Dam MIC random forest algorithm Influencing factors Time lag

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Optimization of parallel random forest algorithm based on distance weight

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1951-1963页

作者： Wang, Qinge Chen, Huihua Cent South Univ Sch Civil Engn Changsha Peoples R China

In order to overcome the problems of long execution time and low parallelism of existing parallel random forest algorithms, an optimization method for parallel random forest algorithm based on distance weights is proposed. The concept of distance weights is introduced to optimize the algorithm. Firstly, the training sample data are extracted from the original data set by random selection. Based on the extracted results, a single decision tree is constructed. The single decision tree is grouped together according to different grouping methods to form a random forest. The distance weights of the training sample data set are calculated, and then the weighted optimization of the random forest model is realized. The experimental results show that the execution time of the parallel random forest algorithm after optimization is 110 000 ms less than that before optimization, and the operation efficiency of the algorithm is greatly improved, which effectively solves the problems existing in the traditional random forest algorithm.

关键词： Distance weights parallel algorithm random forest algorithm algorithm optimization

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Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest algorithm

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ANALYTICAL SCIENCES 2024年第9期40卷 1709-1722页

作者： Du, Hongkun Ke, Shaoying Zhang, Wei Qi, Dongfeng Sun, Tengfei Shandong Univ Technol Ctr Adv Laser Mfg CALM Sch Mech Engn Zibo 255000 Shandong Peoples R China Minnan Normal Univ Sch Phys & Informat Engn Key Lab Light Field Manipulat & Syst Integrat Appl Zhangzhou 363000 Fujian Peoples R China

Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064 nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with random forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-random forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450;coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.

关键词： Laser-induced breakdown spectroscopy random forest algorithm Coal analysis

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Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm and discrete cosine transform

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 143卷 59-65页

作者： Akkoc, Betul Arslan, Ahmet Kok, Hatice Selcuk Univ Fac Engn Dept Comp Engn Aleaddin Keykubad Campus TR-42075 Konya Turkey Konya Food & Agr Univ Dept Comp Engn Konya Turkey Necmettin Erbakan Univ Dept Orthodont Konya Turkey

Background and Objective: One of the first stages in the identification of an individual is gender determination. Through gender determination, the search spectrum can be reduced. In disasters such as accidents or fires, which can render identification somewhat difficult, durable teeth are an important source for identification. This study proposes a smart system that can automatically determine gender using 3D digital maxillary tooth plaster models. Methods: The study group was composed of 40 Turkish individuals (20 female, 20 male) between the ages of 21 and 24. Using the iterative closest point (ICP) algorithm, tooth models were aligned, and after the segmentation process, models were transformed into depth images. The local discrete cosine transform (DCT) was used in the process of feature extraction, and the random forest (RF) algorithm was used for the process of classification. Results: Classification was performed using 30 different seeds for random generator values and 10 fold cross-validation. A value of 85.166% was obtained for average classification accuracy (CA) and a value of 91.75% for the area under the ROC curve (AUC). Conclusions: A multi-disciplinary study is performed here that includes computer sciences, medicine and dentistry. A smart system is proposed for the determination of gender from 3D digital models of maxillary tooth plaster models. This study has the capacity to extend the field of gender determination from teeth. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Gender determination Feature extraction Image processing random forest algorithm

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UWB Rectangular Microstrip Patch Antenna Design in Matching Liquid and Evaluating the Classification Accuracy in Data Mining Using random forest algorithm for Breast Cancer Detection with Microwave

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2019年第5期14卷 2127-2136页

作者： Aydin, Emine Avsar Keles, Mumie Kaya Adana Alparslan Turkes Sci & Technol Univ Dept Aerosp Engn Adana Turkey Adana Alparslan Turkes Sci & Technol Univ Dept Comp Engn Adana Turkey

The most common type of cancer for a female is breast cancer in the world. Regular checks and effective-timely treatment are noteworthy parameters for patients' survival struggle . Against existing imaging methods, microwave imaging method has been considered more powerful and effective method by many researchers. In this paper, comprehensive design equations and parameters of rectangular microstrip patch antenna (RMPA) are given for microwave breast cancer detection. The layered breast model with a spherical tumor that was placed into the fibro-glandular layer was created by using CST Microwave Studio Software, and it was embedded in canola oil to decrease the distorted signals between the transmitting and receiving antennas. The RMPA has a wideband performance from 3 to 18GHz. The simulation results show that differences in the electric field and reflection coefficients might more efficiently give a possibility to assign the tumor in the breast model. In addition, in this study, the data obtained from these experiments are classified by using the random forest algorithm from the data mining methods. According to the classification result, the random forest algorithm can diagnose breast cancer by classifying the tumor as 94% accuracy.

关键词： Breast cancer detection Classification Data mining random forest algorithm Rectangular microstrip patch antenna

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

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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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Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2014年第1期33卷 192-202页

作者： Liu, Meiling Liu, Xiangnan Li, Jin Ding, Chao Jiang, Jiale China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Natl Marine Data & Informat Serv Tianjin 300171 Peoples R China

Satellites routinely provide frequent, large-scale, near-surface views of many oceanographic variables pertinent to plankton ecology. However, the nutrient fertility of water can be challenging to detect accurately using remote sensing technology. This research has explored an approach to estimate the nutrient fertility in coastal waters through the fusion of synthetic aperture radar (SAR) images and optical images using the random forest (RF) algorithm. The estimation of total inorganic nitrogen (TIN) in the Hong Kong Sea, China, was used as a case study. In March of 2009 and May and August of 2010, a sequence of multi-temporal in situ data and CCD images from China's HJ-1 satellite and RADARSAT-2 images were acquired. Four sensitive parameters were selected as input variables to evaluate TIN: single-band reflectance, a normalized difference spectral index (NDSI) and Hy and VH polarizations. The RF algorithm was used to merge the different input variables from the SAR and optical imagery to generate a new dataset (i.e., the TIN outputs). The results showed the temporal-spatial distribution of TIN. The TIN values decreased from coastal waters to the open water areas, and TIN values in the northeast area were higher than those found in the southwest region of the study area. The maximum TIN values occurred in May. Additionally, the estimation accuracy for estimating TIN was significantly improved when the SAR and optical data were used in combination rather than a single data type alone. This study suggests that this method of estimating nutrient fertility in coastal waters by effectively fusing data from multiple sensors is very promising. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multisensor data fusion random forest algorithm Total inorganic nitrogen RADARSAT-2 images Optical images

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Evaluation model of multimedia-aided teaching effect of physical education course based on random forest algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 555-567页

作者： Liu, Gang Zhuang, Hongbo Xiangtan Med & Hlth Vocat Coll Inst Med Technol Xiangtan 411102 Peoples R China Hunan Univ Sci & Technol Coll Phys Educ Xiangtan 411201 Peoples R China Krirk Univ Int Coll Bangkok 10220 Thailand

The multimedia technology and computer technology supported by the development of modern science and technology provide an important platform for the development of college physical education teaching activities. To better play the role of network auxiliary teaching platform in college sports teaching and improve the effectiveness of college sports teaching, the construction method of multimedia auxiliary teaching effect evaluation model based on the random number forest algorithm is proposed. Through the specification of the random forest algorithm and the optimization of the teaching quality evaluation index, the auxiliary teaching level of the college physical education network is analyzed, and the evaluation of the multimedia auxiliary teaching effect of the physical education courses is completed. The experimental results verify the effectiveness of the evaluation model designed in this article, with a user satisfaction of 72%. Teachers and students can use the evaluation model to improve the teaching quality and teaching efficiency, improve the management work, and promote the scientific, standardization, and specialization of physical education teaching management in colleges and universities.

关键词： random forest algorithm physical education curriculum auxiliary teaching effect evaluation

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An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT

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TECHNOLOGY AND HEALTH CARE 2024年第4期32卷 2657-2671页

作者： Li, Jiahao Beijing Sports Univ Competit Sports Coll Beijing Peoples R China

BACKGROUND: The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. OBJECTIVE: The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. METHODS: A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and random forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times RESULTS: The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. CONCLUSION: All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.

关键词： Discrete wavelet transform random forest algorithm three-dimensional mechanics gyroscope computational efficiency

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