A new uncertain linear regression model based on equation deformation

Wang, Shuai; Ning, Yufu; Hong-mei, Shi

doi:10.1007/s00500-021-06030-7

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…Subsequently, this study used linear regression analysis to verify the relationship between visitors’ landscape experiences and emotional perceptions [ 74 ]. The landscape relative evaluation rate represented the landscape experience as an independent variable, while the relative emotional value represented the emotional perception as a dependent variable.…”

Section: Methodsmentioning

confidence: 99%

Landscape Aesthetic Value of Waterfront Green Space Based on Space–Psychology–Behavior Dimension: A Case Study along Qiantang River (Hangzhou Section)

Xiao-jia

Chen²,

Huang

et al. 2023

IJERPH

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As an important part of urban green infrastructure, the landscape effect of the urban waterfront green space varies, and sometimes, the green space with an excellent landscape aesthetic value fails to serve the needs of most citizens. This seriously affects the construction of a green ecological civilization and the implementation of the concept of “common prosperity” in China. Based on multi-source data, this study took the Qiantang River Basin as an example, selected 12 representative waterfront green spaces along the river as the research objects, and used qualitative and quantitative analysis methods to determine the landscape aesthetic value of the research area from the different dimensions of space, psychology, and physiology. We examined the relationship between each dimension so as to objectively and comprehensively reflect the landscape value characteristics of the waterfront green space in the study area and provide a reasonable theoretical framework and practical development path for future urban waterfront green space landscape design. We obtained the following results: (1) The results of the spatial dimension research indicated that the spatial value index of the waterfront green space in the study area was three-dimensional space > vertical space > horizontal space, and the overall spatial value was low; Qianjiang Ecological Park obtained the highest value (0.5473), and Urban Balcony Park obtained the lowest value (0.4619). (2) The results of the psychological dimension indicated that people’s perceptions of the waterfront green space in the study area were relatively weak, mainly focusing on visual perception, but the waterfront green space with a relative emotional value greater than one accounted for 75%, and the overall recognition of the landscape was high. (3) The results of the behavioral dimension showed that the overall heat of the waterfront green space in the study area was insufficient (1.3719–7.1583), which was mainly concentrated in low-heat levels, and the population density was unevenly distributed (0.0014–0.0663), which was mainly concentrated in the medium-density level. The main purpose of users was to visit, and they stayed an average of 1.5 h. (4) The results of the coupling coordination analysis of the spatial–psychological–behavioral dimensions showed that the landscape value of the waterfront green space in the study area presented a form of ‘high coupling degree and low coordination degree’.

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Section: Methodsmentioning

confidence: 99%

Landscape Aesthetic Value of Waterfront Green Space Based on Space–Psychology–Behavior Dimension: A Case Study along Qiantang River (Hangzhou Section)

Xiao-jia

Chen²,

Huang

et al. 2023

IJERPH

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“…By analyzing the loss function or utility function associated with the problem, the LR algorithm strives to determine the optimal model through the optimization of said function. Traditionally, the least squares method is employed to minimize the loss function [56]. Nevertheless, the least squares method encounters limitations when applied to large datasets.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

Liu,

Wu,

Cui

et al. 2023

Remote Sensing

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Soil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, and high costs. Remote sensing technology enables the non-destructive and efficient retrieval of land information, allowing rapid soil moisture monitoring to schedule crop irrigation and evaluate the irrigation efficiency. Satellite data with different resolutions provide different observation scales. Evaluating the accuracy of estimating soil moisture based on open and free satellite data, as well as exploring the comprehensiveness and adaptability of different satellites for soil moisture temporal and spatial observations, are important research contents of current soil moisture monitoring. The study utilized three types of satellite data, namely GF-1, Landsat-8, and GF-4, with respective temporal and spatial resolutions of 16 m (every 4 days), 30 m (every 16 days), and 50 m (daily). The gray relational analysis (GRA) was employed to identify vegetation indices that selected sensitivity to soil moisture at varying depths (3 cm, 10 cm, and 20 cm). Then, this study employed random forest (RF), Extra Tree (ETr), and linear regression (LR) algorithms to estimate soil moisture at different depths with optical satellite data sources. The results showed that the accuracy of soil moisture estimation was different at different growth stages. The model accuracy exhibited an upward trend during the middle and late growth stages, coinciding with higher vegetation coverage; however, it demonstrated a decline in accuracy during the early and late growth stages due to either the absence or limited presence of vegetation. Among the three satellite images, the vegetation indices derived from GF-1 exhibited were more sensitive to vegetation characteristics and demonstrated superior soil moisture estimation accuracy (with R2 ranging 0.129–0.928, RMSE ranging 0.017–0.078), followed by Landsat-8 (with R2 ranging 0.117–0.862, RMSE ranging 0.017–0.088). The soil moisture estimation accuracy of GF-4 was the worst (with R2 ranging 0.070–0.921, RMSE ranging 0.020–0.140). Thus, GF-1 is suitable for vegetated areas. In addition, the ETr model outperformed the other models in both accuracy and stability (ETr model: R2 ranging from 0.117 to 0.928, RMSE ranging from 0.021 to 0.091; RF model: R2 ranging from 0.225 to 0.926, RMSE ranging from 0.019 to 0.085; LR model: R2 ranging from 0.048 to 0.733, RMSE ranging from 0.030 to 0.144). Utilizing GF-1 is recommended to construct the ETr model for assessing soil moisture variations in the farming land of northern China. Therefore, in cases where there are limited ground sample data, it is advisable to utilize high-spatiotemporal-resolution remote sensing data, along with machine learning algorithms such as ETr and RF, which are suitable for small samples, for soil moisture estimation.

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“…With the continuous development of uncertain regression analysis theory, an increasing number of scholars have made significant contributions in the field of uncertain regression analysis and have further advanced and improved the theory. These research achievements include studies on the uncertain regression analysis theory itself [16][17][18][19][20][21], as well as research applying uncertain regression analysis to practical problems [22]. Although the application research is relatively in its early stages, the application domain of uncertain regression analysis is quite extensive.…”

Section: Uncertain Hypothesis Testmentioning

confidence: 99%

Research on the Factors Influencing Tourism Revenue of Shandong Province in China Based on Uncertain Regression Analysis

Chen,

Ning,

Wang

et al. 2023

Mathematics

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According to the analysis of historical tourism data, it was found that tourism revenue is influenced by multiple factors, and there exists a linear relationship between these factors and tourism revenue. Therefore, this paper employs a linear regression model to investigate the factors influencing tourism revenue. However, research on tourism data has found that the disturbance term of the linear regression model is not frequency-stable. This indicates that the disturbance term should be an uncertain variable rather than a random variable. Therefore, this paper adopts an uncertain linear regression analysis model and employs the tourism data of Shandong Province in China from 2011 to 2020 as the sample to investigate the factors influencing tourism revenue. The study provides parameter estimation and residual analysis of the model, as well as predictions and confidence intervals of tourism revenue. Additionally, through an uncertain hypothesis test, it was verified that the adopted model fitted the relevant tourism data well. The results show that factors such as the number of travel agencies, railway length, domestic tourist numbers, and per capita disposable income of urban residents have a significant impact on tourism revenue. Based on the study, recommendations and measures for improving tourism revenue of Shandong Province are proposed.

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A new uncertain linear regression model based on equation deformation

Cited by 15 publications

References 17 publications

Landscape Aesthetic Value of Waterfront Green Space Based on Space–Psychology–Behavior Dimension: A Case Study along Qiantang River (Hangzhou Section)

Landscape Aesthetic Value of Waterfront Green Space Based on Space–Psychology–Behavior Dimension: A Case Study along Qiantang River (Hangzhou Section)

Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

Research on the Factors Influencing Tourism Revenue of Shandong Province in China Based on Uncertain Regression Analysis

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