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Analysis of RVI for rice crops in small-scale agricultural fields using Sentinel-1 SAR data: case study on LAI retrieval using regression algorithms

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PADDY AND WATER ENVIRONMENT 2025年第1期23卷 197-212页

作者： Salma, Shaik Ket, Snehal Kishor Dodamani, B. M. Natl Inst Technol Karnataka Dept Water Resources & Ocean Engn Surathkal India

Leaf Area Index (LAI) is a crucial indicator for assessing plant growth, canopy structure, photosynthetic capacity, and overall productivity. The Radar Vegetation Index (RVI), a well-established microwave metric, serves as an effective tool for retrieving the LAI due to its sensitivity to vegetation characteristics. The primary objective of utilizing RVI in LAI studies is to improve the accuracy and reliability of LAI estimation, where optical methods may be hindered by atmospheric conditions. Over the past decade, numerous studies have explored the relationship between RVI and LAI, highlighting the potential of RVI for accurate LAI estimation in crops. In particular, for rice crop analysis in this study, the RVI is derived by incorporating the Degree of Polarization (DOP) from a 2 x 2 covariance matrix as the coefficient, along with the polarization backscatter of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data. The study also explores RVI derivation from M-chi (m-chi) and M-delta (m-delta) decomposition (assuming circularity in dual-polarized data) and linear backscattering intensities. Using the RVI's, machine learning regression models are applied to retrieve LAI. The DOP over crop period, the temporal analysis of RVI, and in-situ LAI has been employed to examine trends during crop growth. Notably, among all derived RVIs, the one obtained using the DOP technique, particularly when combined with random forest regression, consistently exhibits superior performance for rice crop LAI estimation (R = 0.91;RMSE = 0.25 m2/m2), whereas, the R value for other models ranges a lower value of 0.63 to a higher value of 0.83 with RMSE of higher value 0.64 m2/m2 to a lower value of 0.32 m2/m2. The findings in the study highlights the sensitivity of SAR data to the DOP and the vegetation structure of rice crops in small-scale agricultural fields.

关键词： Degree of polarization (DOP) Radar vegetation index (RVI) Leaf area index (LAI) regression algorithms Sentinel-1 SAR data

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Thermodynamic properties of binary, ternary and quaternary mixtures: N-butyl acetate- n-hexanol - n,n-dimethylacetamide-water. Modeling using regression algorithms and optimization using socially-inspired evolutionary algorithms

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2024年 165卷

作者： Birgauanu, Iuliana Gavrilescu, Marius Leon, Florin Curteanu, Silvia Lisa, Gabriela Gheorghe Asachi Tech Univ Iasi Cristofor Simionescu Fac Chem Engn & Environm Prot 73 Prof D Mangeron Blvd Iasi 700050 Romania Gheorghe Asachi Tech Univ Fac Automat Control & Comp Engn 27 Prof D Mangeron Blvd Iasi 700050 Romania

Background: The design of chemical plants requires knowledge of the thermodynamic properties of the fluids involved. Methods: In our research, we performed experimental measurements of the refractive index and density of binary, ternary and quaternary systems containing n-butyl acetate, n-hexanol, n,n-dimethylacetamide and water, at atmospheric pressure and the following temperatures: 293.15 K, 303.15 K, 313.15 K and 323.15 K. They were used to determine the excess molar volume, which was correlated with mole fractions, normalized temperature and refractive index, using artificial neural networks and other models obtained with regression algorithms, optimized with different socially-inspired evolutionary algorithms. Significant Findings: The best results were achieved with decision tree regression and the queuing search optimization algorithm. The present approach is based on the fact that there are few similar studies in the literature;in addition, the models obtained can complement/supplement the experimental data through predictions.

关键词： Density Refractive index Excess molar volume regression algorithms Socially-inspired evolutionary algorithms n-butyl acetate n-hexanol n-dimethylacetamide Water

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Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

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REMOTE SENSING OF ENVIRONMENT 2012年 118卷 127-139页

作者： Verrelst, Jochem Munoz, Jordi Alonso, Luis Delegido, Jesus Pablo Rivera, Juan Camps-Valls, Gustavo Moreno, Jose Univ Valencia Image Proc Lab IPL E-46003 Valencia Spain

ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (53) aim to ensure continuity for Landsat 5/7, SPOTS, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from the ESA-led field campaign SPARC (Barrax, Spain) we have compared the utility of four state-of-the-art machine learning regression algorithms and four different S2 and S3 band settings to assess three important biophysical parameters: leaf chlorophyll content (Chl), leaf area index (LAI) and fractional vegetation cover (FVC). The tested Sentinel configurations were: S2-10 m (4 bands), S2-20 m (8 bands), 52-60 m (10 bands) and S3-300 m (19 bands), and the tested methods were: neural networks (NN), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR). GPR outperformed the other retrieval methods for the majority of tested configurations and was the only method that reached the 10% precision required by end users in the estimation of Chl. Also, although validated with an RMSE accuracy around 20%, GPR yielded optimal LAI and FVC estimates at highest S2 spatial resolution of 10 m with only four bands. In addition to high accuracy values, GPR also provided confidence intervals of the estimates and insight in relevant bands, which are key advantages over the other methods. Given all this, GPR proved to be a fast and accurate nonlinear retrieval algorithm that can be potentially implemented for operational monitoring applications. (C) 2011 Elsevier

关键词： Sentinel-2 Sentinel-3 Machine learning regression algorithms Support vector regression (SVR) Kernel ridge regression (KRR) Gaussian Processes regression (GPR) Biophysical parameter retrieval

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HOS network-based classification of power quality events via regression algorithms

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2015年第1期2015卷 1-11页

作者： Palomares Salas, Jose Carlos Gonzalez de la Rosa, Juan Jose Sierra Fernandez, Jose Maria Perez, Agustin Agueera Res Grp PAIDI TIC 168 Computat Instrumentat & Ind E-11202 Algeciras Cadiz Spain Univ Cadiz Polytech Sch Engn Area Elect E-11202 Algeciras Cadiz Spain

This work compares seven regression algorithms implemented in artificial neural networks (ANNs) supported by 14 power-quality features, which are based in higher-order statistics. Combining time and frequency domain estimators to deal with non-stationary measurement sequences, the final goal of the system is the implementation in the future smart grid to guarantee compatibility between all equipment connected. The principal results are based in spectral kurtosis measurements, which easily adapt to the impulsive nature of the power quality events. These results verify that the proposed technique is capable of offering interesting results for power quality (PQ) disturbance classification. The best results are obtained using radial basis networks, generalized regression, and multilayer perceptron, mainly due to the non-linear nature of data.

关键词： Artificial neural networks (ANN) Power quality (PQ) Cumulants Higher-order statistics (HOS) regression algorithms Smart grid (SG) Spectral kurtosis (SK)

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Benchmarking of regression algorithms for Simulating the Building's Energy 11

Benchmarking of Regression Algorithms for Simulating the Bui...

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IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Dimara, Asimina Anagnostopoulos, Christos-Nikolaos Krinidis, Stelios Tzovaras, Dimitrios Hellas Informat Technol Inst Ctr Res & Technol Thessaloniki Greece Univ Aegean Social Sci Sch Cultural Technol & Commun Dept Mitilini Greece

ISBN: (纸本)9781665414906

Applications utilizing simulation tools play a significant role in building design, demand response and building's performance optimization. In this paper a set of regression algorithms are evaluated in order to exploit the best algorithm performance for simulating the buildings energy consumption. Commonly used regression algorithms like Decision Trees and Random Forests and rarely used ones like Lasso and Pansac were implemented, tested and compared on the same use case and data set. Choosing the appropriate algorithm that better fits the solution of a problem is a thorny task. As a result, we address an evaluation of regression algorithms for simulation. The results display the potential to choose the best algorithm to simulate the building's energy.

关键词： Benchmarking analysis regression algorithms Building Energy Simulation

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Software package for regression algorithms based on Gaussian Conditional Random Fields 21

Software package for regression algorithms based on Gaussian...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Markovic, Tijana Devedzic, Vladan Zhou, Fang Obradovic, Zoran Malardalen Univ Sch Innovat Design & Engn Vasteras Sweden Univ Belgrade Fac Org Sci Belgrade Serbia East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China Temple Univ Ctr Data Analyt & Biomed Informat Philadelphia PA USA

ISBN: (纸本)9781665462839

The Gaussian Conditional Random Fields (GCRF) algorithm and its extensions are used for machine learning regression problems in which the attributes of objects and the correlation between objects should be considered when making predictions. These algorithms can be applied in different domains where problems can be seen as graphs, but their implementation requires complex calculations and good programming skills. This paper presents an open source software package that includes a tool with graphical user interface (GCRFs tool) and Java library (GCRFs library). GCRFs tool is software that integrates various GCRF-based algorithms and supports training and testing of those algorithms on real-world datasets. The main goal of GCRFs tool is to provide a straightforward and user-friendly graphical user interface that will simplify the use of GCRF-based algorithms. GCRFs Java library contains basic classes for GCRF concepts and can be used by researchers who have experience in Java programming. Also, this paper presents the results of a pilot usability evaluation of the GCRFs tool, where the software was evaluated with expert and non-expert users. This evaluation gave us detailed insight into the experiences and opinions of the users and helped us outline priorities for future development.

关键词： Gaussian Conditional Random Fields regression algorithms Graphs Software Library

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Towards an MCDM-based evaluation framework for regression algorithms

Towards an MCDM-based evaluation framework for regression al...

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IEEE International Conference on Smart Computing (SMARTCOMP)

作者： Yao, Di Yuen, Kevin Kam Fung Xian Jiaotong Liverpool Univ Dept Comp Sci & Software Engn Suzhou Peoples R China

ISBN: (纸本)9781509065172

Prediction is a challenging topic and regression is one of the methods developed for this problem. Various regression algorithms produce different prediction results for different datasets. Various performance criteria are used to measure the regression algorithms performance. The Multiple Criteria Decision Making (MCDM) method can be used to synthesize measurements of the performance criteria for the algorithms to select the most appropriate algorithm for a dataset. This paper introduces a MCDM-based evaluation framework for regression algorithms selection for a dataset. Experiments are demonstrated for usability of the proposed framework.

关键词： regression algorithms Multiple Criteria Decision Making (MCDM) Algorithm Evaluation Framework

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Capacity Loss Analysis Using Machine Learning regression algorithms 9

Capacity Loss Analysis Using Machine Learning Regression Alg...

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9th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Atay, Sergen Ayranci, Ahmet Aytug Erkmen, Burcu Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey Istanbul Kultur Univ Dept Elect & Elect Engn Istanbul Turkey

ISBN: (纸本)9781665467544

In this study, time dependent measurements of the power capacitor, which is the main equipment of a compensation unit, are given. The power capacitor is actively working in an industrial facility. Six months of the data from this capacitor were recorded and tests were carried out using Machine Learning (ML) algorithms for its remaining useful life. ML algorithms were selected from the algorithms that used for regression problems. In the study, Support Vector Machine (SVM), Linear regression (LR) and regression Trees (RT) algorithms were used. The rated powers of the analyzed capacitor are 50kVAR and 25kVAR from the active plant. The data set was created by running the capacitor continuously for 6 months and the capacity loss was examined with using ML algorithms. The algorithm that gives the best result in the regression analyzes is the LR algorithm. With the results obtained, it is possible to analyze how long the useful life of capacitors with the same characteristics have under the same stress.

关键词： machine learning power capacitors regression algorithms remaining capacity of capacitor

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A Study of Comparative Analysis of regression algorithms for Reusability Evaluation of Object Oriented Based Software Components

A Study of Comparative Analysis of Regression Algorithms for...

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7th International Conference on Open Source Systems and Technologies (ICOSST)

作者： Zahara, Syeda Iffat Ilyas, Muhammad Zia, Tehseen Univ Sargodha Dept Comp Sci & Informat Technol Sargodha Pakistan

ISBN: (纸本)9781479920464

Reusability of software is found to be a key feature of quality. The most obvious outcomes of software reuse are overcoming the software crisis, advancing in software quality and improving productivity. The issue of spotting reusable software components from given existing system is very important but yet it is not much cultivated. For identification and evaluation of reusable software we use an approach that has foundation of software models and metrics. Idea of this study is to examine the competence and effectiveness of machine learning regression techniques which are experimented here to build precise and constructive evaluation model that can assess the reusability of Object Oriented based software components based on the values of five metrics of metrics suite presented by "Shyam R. Chaidmber and Chris ***". By setting different values of parameters of these algorithms, it is also concluded that which specific algorithm or class of algorithms is appropriate for reusability evaluation and with which parameter's values. For this comparative analysis we have used Weka and experimented different regression techniques as Multi-linear regression, Model Tree M5P, Standard instance-based learning scheme IBk and Meta-learning scheme Additive regression. As the result of this analysis and experimentation "Standard instance-based learning IBk with no distance weighting" is found to be the best regression algorithm for reusability evaluation of Object Oriented software components using CK metrics.

关键词： Reusability regression Software metrics regression algorithms Reusability Evaluation Object Oriented Based Software Components

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An analytical model for analyzing the value of information flow in the production chain model using regression algorithms and neural networks

Supply Chain Analytics

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Supply Chain Analytics 2023年 2卷

作者： Biyeme, Florent Mbakop, André Marie Chana, Anne Marie Voufo, Joseph National Advanced School of Engineering of Yaoundé (NASEY) Cameroon Laboratory of Civil and Mechanical Engineering Yaoundé Cameroon Department of Industrial and Mechanical Engineering Yaoundé Cameroon Laboratory of Mathematics Informatics Bio-informatics and Applications Yaoundé Cameroon Department of Computer Science Engineering Yaoundé Cameroon

Managing information flow has always been a challenging and critical driver of performance increase in manufacturing companies. Each bit of information related to the manufacturing process has an information flow value that can impact the process. Recent studies have focused on the traditional classification algorithms methods to analyze the value of information flow. In this research paper, we use regression algorithms to develop an analytics model for the value of information flow in manufacturing shop floors of developing countries. The analysis shows that the Artificial Neural Network (ANN) has the best regression coefficient score of 0.775 with a prediction error of 0.0125. The lowest regression coefficient score of 0.323 was for the Multi-Linear regression (MLR) with a prediction error of 0.0556. These results help companies use regression algorithms effectively to analyze the value of information flows on the manufacturing chains. © 2023 The Authors

关键词： Artificial neural network Information flow management Production chain model regression algorithms Value of information

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