Tree-Structured Regression Model Using a Projection Pursuit Approach

Cho, Hyunsun; Lee, Eunkyung

doi:10.3390/app11219885

Cited by 5 publications

(3 citation statements)

References 25 publications

(26 reference statements)

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“…Figure 2 depicts the network. Furthermore, this study conducts a comparative analysis of three prevalent machine learning algorithms-linear regression, decision tree regression, and XGBoost (eXtreme Gradient Boosting)-to elucidate the superiority of the proposed method in assessing the chlorophyll content within rice leaves ( [42][43][44]).…”

Section: Modeling 251 Convolutional Neural Network and Long Short-ter...mentioning

confidence: 99%

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Zhou,

Li,

Wang

et al. 2024

Plants

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Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization–Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

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Section: Modeling 251 Convolutional Neural Network and Long Short-ter...mentioning

confidence: 99%

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Zhou,

Li,

Wang

et al. 2024

Plants

View full text Add to dashboard Cite

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“…PPA, as a high-dimensional data analysis method, can identify data structures and characteristics by projecting high-dimensional monitoring data into a low-dimensional space. PPA [47][48][49][50] is used to optimize the weight parameters. The calculation steps are given as follows.…”

Section: Weight Optimizationmentioning

confidence: 99%

Research on Temperature Control Index for High Concrete Dams Based on Information Entropy and Cloud Model from the View of Spatial Field

Yang,

Sun,

Zhang

et al. 2023

Water

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It is significant to adopt scientific temperature control criteria for high concrete dams in the construction period according to practical experience and theoretical calculation. This work synthetically uses information entropy and a cloud model and develops novel in situ observation data-based temperature control indexes from the view of a spatial field. The order degree and the disorder degree of observation values are defined according to the probability principle. Information entropy and weight parameters are combined to describe the distribution characteristics of the temperature field. Weight parameters are optimized via projection pursuit analysis (PPA), and then temperature field entropy (TFE) is constructed. Based on the above work, multi-level temperature control indexes are set up via a cloud model. Finally, a case study is conducted to verify the performance of the proposed method. According to the calculation results, the change law of TFEs agrees with actual situations, indicating that the established TFE is reasonable, the application conditions of the cloud model are wider than those of the typical small probability method, and the determined temperature control indexes improve the safety management level of high concrete dams. Research results offer scientific reference and technical support for temperature control standards adopted at other similar projects.

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“…The evaluation of complex system schemes often involves many indicators, and the relationship between indicators is also complex. Conventional scheme decision-making methods mainly adopt the combination of qualitative and quantitative methods, and the fusion of objective information and subjective information, such as fuzzy optimization method (Xu and Zhao, 2008;Ignatius et al, 2018;Sitorus and Brito-Parada, 2022), grey relational analysis (GRA) (Xia et al, 2016;Tian et al, 2018;Cai et al, 2021), TOPSIS method (Liu and Zhang, 2014;Imam and Gurol, 2018), projection pursuit (PP) (Lan and Huang, 2018;Lee, 2018;Cho and Lee, 2021), analytic hierarchy process (AHP) (Wang et al, 2021;Yu et al, 2021;Ye and Chen, 2022) and artificial neural network (Galdo et al, 2021;Yuan et al, 2021;Leng and Huang, 2022). In the decision-making field of reservoir operation schemes, Zhu et al (2017b) used TOPSIS method, fuzzy optimization method and fuzzy matter-element method to rank all feasible flood control alternatives of multi-reservoir system, and the optimization scheme provides support for decision-making.…”

Section: Introductionmentioning

confidence: 99%

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

Lin

et al. 2023

Front. Plant Sci.

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Reservoir operation is an important part of basin water resources management. The rational use of reservoir operation scheme can not only enhance the capacity of flood control and disaster reduction in the basin, but also improve the efficiency of water use and give full play to the comprehensive role the reservoir. The conventional decision-making method of reservoir operation scheme is computationally large, subjectivity and difficult to capture the nonlinear relationship. To solve these problems, this paper proposes a reservoir operation scheme decision-making model IWGAN-IWOA-CNN based on artificial intelligence and deep learning technology. In view of the lack of data in the original reservoir operation scheme and the limited improvement of data characteristics by the traditional data augmentation algorithm, an improved generative adversarial network algorithm (IWGAN) is proposed. IWGAN uses the loss function which integrates Wasserstein distance, gradient penalty and difference item, and dynamically adds random noise in the process of model training. The whale optimization algorithm is improved by introducing Logistic chaotic mapping to initialize population, non-linear convergence factor and adaptive weights, and Levy flight perturbation strategy. The improved whale optimization algorithm (IWOA) is used to optimize hyperparameters of convolutional neural networks (CNN), so as to obtain the best parameters for model prediction. The experimental results show that the data generated by IWGAN has certain representation ability and high quality; IWOA has faster convergence speed, higher convergence accuracy and better stability; IWGAN-IWOA-CNN model has higher prediction accuracy and reliability of scheme selection.

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Tree-Structured Regression Model Using a Projection Pursuit Approach

Cited by 5 publications

References 25 publications

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Research on Temperature Control Index for High Concrete Dams Based on Information Entropy and Cloud Model from the View of Spatial Field

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

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