1
|
Nian Y, Su X, Yue H, Zhu Y, Li J, Wang W, Sheng Y, Ma Q, Liu J, Li X. Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm. FRONTIERS IN PLANT SCIENCE 2024; 15:1396183. [PMID: 38726299 PMCID: PMC11079175 DOI: 10.3389/fpls.2024.1396183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/11/2024] [Indexed: 05/12/2024]
Abstract
Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.
Collapse
Affiliation(s)
- Ying Nian
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Xiangxiang Su
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Hu Yue
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Yongji Zhu
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Jun Li
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Weiqiang Wang
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Yali Sheng
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Qiang Ma
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
| | - Jikai Liu
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
- Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China
| | - Xinwei Li
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, China
- Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China
- Anhui Province Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Science and Technology University, Chuzhou, China
| |
Collapse
|
2
|
Park S, Ceulemans E, Van Deun K. Logistic regression with sparse common and distinctive covariates. Behav Res Methods 2023; 55:4143-4174. [PMID: 36781701 PMCID: PMC10700465 DOI: 10.3758/s13428-022-02011-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/10/2022] [Indexed: 02/15/2023]
Abstract
Having large sets of predictor variables from multiple sources concerning the same individuals is becoming increasingly common in behavioral research. On top of the variable selection problem, predicting a categorical outcome using such data gives rise to an additional challenge of identifying the processes at play underneath the predictors. These processes are of particular interest in the setting of multi-source data because they can either be associated individually with a single data source or jointly with multiple sources. Although many methods have addressed the classification problem in high dimensionality, the additional challenge of distinguishing such underlying predictor processes from multi-source data has not received sufficient attention. To this end, we propose the method of Sparse Common and Distinctive Covariates Logistic Regression (SCD-Cov-logR). The method is a multi-source extension of principal covariates regression that combines with generalized linear modeling framework to allow classification of a categorical outcome. In a simulation study, SCD-Cov-logR resulted in outperformance compared to related methods commonly used in behavioral sciences. We also demonstrate the practical usage of the method under an empirical dataset.
Collapse
Affiliation(s)
- S Park
- Tilburg University, Tilburg, Netherlands.
| | | | - K Van Deun
- Tilburg University, Tilburg, Netherlands
| |
Collapse
|
3
|
Yuki S, Tanioka K, Yadohisa H. Estimation and visualization of heterogeneous treatment effects for multiple outcomes. Stat Med 2023; 42:693-715. [PMID: 36574770 DOI: 10.1002/sim.9638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 12/12/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022]
Abstract
We consider two-arm comparison in clinical trials. The objective is to identify a population with characteristics that make the treatment effective. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying the interactions between treatments and covariates. For a single outcome, there are several ways available to identify the subgroups. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we thus propose a new method that allows for a straightforward interpretation of subgroups and deals with both continuous and binary outcomes. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The interpretation of the subgroups is made by visualizing treatment effects and latent variables. Since we are performing sparse estimation, we can interpret the covariates related to the treatment effects and subgroups. Finally, simulation and real data examples demonstrate the effectiveness of the proposed method.
Collapse
Affiliation(s)
- Shintaro Yuki
- Graduate School of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Kensuke Tanioka
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Hiroshi Yadohisa
- Department of Culture and Information Science, Doshisha University, Kyoto, Japan
| |
Collapse
|
4
|
Hirose K, Miura K, Koie A. Hierarchical clustered multiclass discriminant analysis via cross-validation. Comput Stat Data Anal 2023. [DOI: 10.1016/j.csda.2022.107613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
5
|
Li F, Zhang X, Lu A, Xu L, Ren D, You T. Estimation of metal elements content in soil using x-ray fluorescence based on multilayer perceptron. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:95. [PMID: 35029753 DOI: 10.1007/s10661-022-09750-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/31/2021] [Indexed: 06/14/2023]
Abstract
X-ray fluorescence (XRF) is widely used to rapidly detect heavy metals in soil. Spectra processing has been an important research topic to improve accuracy. In this study, 80 soil samples were analyzed by XRF under indoor conditions, where different preprocessing and quantitative analysis methods were compared in terms of prediction accuracy. Denoising algorithms were used to preprocess the soil spectra before establishing prediction models for As, Pb, Cu, Cr, and Cd in soil. The influence of denoising methods on the prediction effects of different models was compared and discussed. The results on five heavy metal spectra show that the proper spectral preprocessing method can effectively improve the prediction performance of the model. The multilayer perceptron model provides promising analysis and modeling for the five metal elements. The determination coefficients (R2) of the models were 0.857, 0.976, 0.977, 0.995, and 0.886, respectively. The proposed method provides the potential to support accurate quantitation by XRF analysis.
Collapse
Affiliation(s)
- Fang Li
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China
- Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Xiaofeng Zhang
- College of Computer and Information Technology, Three Gorges University, Yichang, 443002, Hubei, China
| | - Anxiang Lu
- Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Li Xu
- Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Dong Ren
- College of Computer and Information Technology, Three Gorges University, Yichang, 443002, Hubei, China
| | - Tianyan You
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
| |
Collapse
|
6
|
Kawano S. Sparse principal component regression via singular value decomposition approach. ADV DATA ANAL CLASSI 2021. [DOI: 10.1007/s11634-020-00435-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractPrincipal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss from the singular value decomposition, with sparse regularization. The proposed method enables us to obtain principal component loadings that include information about both explanatory variables and a response variable. An estimation algorithm is developed by using the alternating direction method of multipliers. We conduct numerical studies to show the effectiveness of the proposed method.
Collapse
|
7
|
Kääriäinen A, Pesola V, Dittmann A, Kontio J, Koivunen J, Pihlajaniemi T, Izzi V. Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer. Int J Mol Sci 2020; 21:E8837. [PMID: 33266472 PMCID: PMC7700160 DOI: 10.3390/ijms21228837] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/20/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022] Open
Abstract
The expression and regulation of matrisome genes-the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors-is of paramount importance for many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding of the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of "landmark" matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.
Collapse
Affiliation(s)
- Anni Kääriäinen
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland; (A.K.); (V.P.); (A.D.); (J.K.); (J.K.); (T.P.)
| | - Vilma Pesola
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland; (A.K.); (V.P.); (A.D.); (J.K.); (J.K.); (T.P.)
| | - Annalena Dittmann
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland; (A.K.); (V.P.); (A.D.); (J.K.); (J.K.); (T.P.)
| | - Juho Kontio
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland; (A.K.); (V.P.); (A.D.); (J.K.); (J.K.); (T.P.)
| | - Jarkko Koivunen
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland; (A.K.); (V.P.); (A.D.); (J.K.); (J.K.); (T.P.)
| | - Taina Pihlajaniemi
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland; (A.K.); (V.P.); (A.D.); (J.K.); (J.K.); (T.P.)
| | - Valerio Izzi
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland; (A.K.); (V.P.); (A.D.); (J.K.); (J.K.); (T.P.)
- Faculty of Medicine, University of Oulu, P.O. BOX 8000, FI-90014 Oulu, Finland
- Finnish Cancer Institute, 00130 Helsinki, Finland
| |
Collapse
|
8
|
Xu C, Yang M, Zhang J. Fast deflation sparse principal component analysis via subspace projections. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1728761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Cong Xu
- School of Mathmatics and Information Sciences, Yantai University, Yantai, People's Republic of China
| | - Min Yang
- School of Mathmatics and Information Sciences, Yantai University, Yantai, People's Republic of China
| | - Jin Zhang
- Department of Mathematics, Shandong Normal University, Jinan, People's Republic of China
| |
Collapse
|
9
|
Chow C, Andrášik R, Fischer B, Keiler M. Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 246:85-100. [PMID: 31176183 DOI: 10.1016/j.jenvman.2019.05.084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 06/09/2023]
Abstract
Knowledge about the cause of differential structural damages following the occurrence of hazardous hydro-meteorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain. In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community.
Collapse
Affiliation(s)
- Candace Chow
- University of Bern, Geography Institute, Hallerstrasse 12, 3012, Bern, Switzerland.
| | - Richard Andrášik
- CDV Transport Research Centre, Líšeňská 33a, 63600, Brno, Czech Republic.
| | - Benjamin Fischer
- Geoformer Igp AG, Sebastiansplatz 1, 3900, Brig-Glis, Switzerland.
| | - Margreth Keiler
- University of Bern, Geography Institute, Hallerstrasse 12, 3012, Bern, Switzerland.
| |
Collapse
|
10
|
Ghasemi-Varnamkhasti M, Mohammad-Razdari A, Yoosefian SH, Izadi Z, Siadat M. Aging discrimination of French cheese types based on the optimization of an electronic nose using multivariate computational approaches combined with response surface method (RSM). Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.04.099] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
|