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Ms. Xiaomi Wang
Hunan normal university

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0 spatial data mining
0 spatial heterogeneity
0 soil orgainic
0 near-infrared spectroscopy
0 spatial prediction

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Journal article
Published: 08 January 2021 in Applied Sciences
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Under the influence of complex environmental conditions, the spatial heterogeneity of soil organic matter (SOM) is inevitable, and the relationship between SOM and visible and near-infrared (VNIR) spectra has the potential to be nonlinear. However, conventional VNIR-based methods for soil organic matter estimation cannot simultaneously consider the potential nonlinear relationship between the explanatory variables and predictors and the spatial heterogeneity of the relationship. Thus, the regional application of existing VNIR spectra-based SOM estimation methods is limited. This study combines the proposed partial least squares–based multivariate adaptive regression spline (PLS–MARS) method and a regional multi-variable associate rule mining and Rank–Kennard-Stone method (MVARC-R-KS) to construct a nonlinear prediction model to realize local optimality considering spatial heterogeneity. First, the MVARC-R-KS method is utilized to select representative samples and alleviate the sample global underrepresentation caused by spatial heterogeneity. Second, the PLS–MARS method is proposed to construct a nonlinear VNIR spectra-based estimation model with local optimization based on selected representative samples. PLS–MARS combined with the MVARC-R-KS method is illustrated and validated through a case study of Jianghan Plain in Hubei Province, China. Results showed that the proposed method far outweighs some available methods in terms of accuracy and robustness, suggesting the reliability of the proposed prediction model.

ACS Style

Xiaomi Wang; Can Yang; Mengjie Zhou. Partial Least Squares Improved Multivariate AdaptiveRegression Splines for Visible and Near-Infrared-Based Soil Organic Matter Estimation Considering Spatial Heterogeneity. Applied Sciences 2021, 11, 566 .

AMA Style

Xiaomi Wang, Can Yang, Mengjie Zhou. Partial Least Squares Improved Multivariate AdaptiveRegression Splines for Visible and Near-Infrared-Based Soil Organic Matter Estimation Considering Spatial Heterogeneity. Applied Sciences. 2021; 11 (2):566.

Chicago/Turabian Style

Xiaomi Wang; Can Yang; Mengjie Zhou. 2021. "Partial Least Squares Improved Multivariate AdaptiveRegression Splines for Visible and Near-Infrared-Based Soil Organic Matter Estimation Considering Spatial Heterogeneity." Applied Sciences 11, no. 2: 566.

Journal article
Published: 07 January 2020 in Applied Energy
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This paper analyzes the relationship between urban form, shrinking cities, and residential carbon emissions, based on information collected for prefectural-level and above Chinese cities for the years of 2005, 2010, and 2015. After controlling for a number of urban form and socioeconomic variables (e.g., size, compactness, and polycentricity), this paper pays attention to residential carbon emissions in ‘shrinking cities’, which have experienced population loss and are a recent urban phenomenon in China. Everything else being equal, shrinking cities tend to be associated with less energy efficient than their growing counterparts, suggesting that these cities may not only be ‘battling’ with shrinking populations and economies but also need to consider the environmental issues.

ACS Style

Xingjian Liu; Mingshu Wang; Wei Qiang; Kang Wu; Xiaomi Wang. Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions. Applied Energy 2020, 261, 114409 .

AMA Style

Xingjian Liu, Mingshu Wang, Wei Qiang, Kang Wu, Xiaomi Wang. Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions. Applied Energy. 2020; 261 ():114409.

Chicago/Turabian Style

Xingjian Liu; Mingshu Wang; Wei Qiang; Kang Wu; Xiaomi Wang. 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions." Applied Energy 261, no. : 114409.

Journal article
Published: 01 September 2017 in Computers and Geotechnics
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ACS Style

Lei-Lei Liu; Yung-Ming Cheng; Xiao-Mi Wang; Shao-He Zhang; Zhong-Hu Wu. System reliability analysis and risk assessment of a layered slope in spatially variable soils considering stratigraphic boundary uncertainty. Computers and Geotechnics 2017, 89, 213 -225.

AMA Style

Lei-Lei Liu, Yung-Ming Cheng, Xiao-Mi Wang, Shao-He Zhang, Zhong-Hu Wu. System reliability analysis and risk assessment of a layered slope in spatially variable soils considering stratigraphic boundary uncertainty. Computers and Geotechnics. 2017; 89 ():213-225.

Chicago/Turabian Style

Lei-Lei Liu; Yung-Ming Cheng; Xiao-Mi Wang; Shao-He Zhang; Zhong-Hu Wu. 2017. "System reliability analysis and risk assessment of a layered slope in spatially variable soils considering stratigraphic boundary uncertainty." Computers and Geotechnics 89, no. : 213-225.

Journal article
Published: 01 July 2017 in Computers and Geotechnics
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ACS Style

Lei-Lei Liu; Yung-Ming Cheng; Shui-Hua Jiang; Shao-He Zhang; Xiao-Mi Wang; Zhong-Hu Wu. Effects of spatial autocorrelation structure of permeability on seepage through an embankment on a soil foundation. Computers and Geotechnics 2017, 87, 62 -75.

AMA Style

Lei-Lei Liu, Yung-Ming Cheng, Shui-Hua Jiang, Shao-He Zhang, Xiao-Mi Wang, Zhong-Hu Wu. Effects of spatial autocorrelation structure of permeability on seepage through an embankment on a soil foundation. Computers and Geotechnics. 2017; 87 ():62-75.

Chicago/Turabian Style

Lei-Lei Liu; Yung-Ming Cheng; Shui-Hua Jiang; Shao-He Zhang; Xiao-Mi Wang; Zhong-Hu Wu. 2017. "Effects of spatial autocorrelation structure of permeability on seepage through an embankment on a soil foundation." Computers and Geotechnics 87, no. : 62-75.

Journal article
Published: 13 April 2017 in ISPRS International Journal of Geo-Information
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Time series clustering algorithms have been widely used to mine the clustering distribution characteristics of real phenomena. However, these algorithms have several limitations. First, they depend heavily on prior knowledge. Second, the algorithms do not simultaneously consider the similarity of spatial locations, spatial-temporal attribute values, and spatial-temporal attribute trends (trends in terms of the change direction and ranges in addition and deletion over time), which are all important similarity measurements. Finally, the calculation cost based on these methods for clustering analysis is becoming increasingly computationally demanding, because the data volume of the image time series data is increasing. In view of these shortcomings, an improved density-based time series clustering method based on image resampling (DBTSC-IR) has been proposed in this paper. The proposed DBTSC-IR has two major parts. In the first part, an optimal resampling scale of the image time series data is first determined to reduce the data volume by using a new scale optimization function. In the second part, the traditional density-based time series clustering algorithm is improved by introducing a density indicator to control the clustering sequences by considering the spatial locations, spatial-temporal attribute values, and spatial-temporal attribute trends. The final clustering analysis is then performed directly on the resampled image time series data by using the improved algorithm. Finally, the effectiveness of the proposed DBTSC-IR is illustrated by experiments on both the simulated datasets and in real applications. The proposed method can effectively and adaptively recognize the spatial patterns with arbitrary shapes of image time series data with consideration of the effects of noise.

ACS Style

Yaolin Liu; Xiaomi Wang; Qiliang Liu; Yiyun Chen; Leilei Liu. An Improved Density-Based Time Series Clustering Method Based on Image Resampling: A Case Study of Surface Deformation Pattern Analysis. ISPRS International Journal of Geo-Information 2017, 6, 118 .

AMA Style

Yaolin Liu, Xiaomi Wang, Qiliang Liu, Yiyun Chen, Leilei Liu. An Improved Density-Based Time Series Clustering Method Based on Image Resampling: A Case Study of Surface Deformation Pattern Analysis. ISPRS International Journal of Geo-Information. 2017; 6 (4):118.

Chicago/Turabian Style

Yaolin Liu; Xiaomi Wang; Qiliang Liu; Yiyun Chen; Leilei Liu. 2017. "An Improved Density-Based Time Series Clustering Method Based on Image Resampling: A Case Study of Surface Deformation Pattern Analysis." ISPRS International Journal of Geo-Information 6, no. 4: 118.

Journal article
Published: 24 February 2017 in Remote Sensing
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The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank–Kennard–Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

ACS Style

Xiaomi Wang; Yiyun Chen; Long Guo; Leilei Liu. Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter. Remote Sensing 2017, 9, 201 .

AMA Style

Xiaomi Wang, Yiyun Chen, Long Guo, Leilei Liu. Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter. Remote Sensing. 2017; 9 (3):201.

Chicago/Turabian Style

Xiaomi Wang; Yiyun Chen; Long Guo; Leilei Liu. 2017. "Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter." Remote Sensing 9, no. 3: 201.

Journal article
Published: 21 November 2016 in Transactions in GIS
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Traditional dual clustering algorithms cannot adaptively perform clustering well without sufficient prior knowledge of the dataset. This article aims at accommodating both spatial and non-spatial attributes in detecting clusters without the need to set parameters by default or prior knowledge. A novel adaptive dual clustering algorithm (ADC+) is proposed to obtain satisfactory clustering results considering the spatial proximity and attribute similarity with the presence of noise and barriers. In this algorithm, Delaunay triangulation is utilized to adaptively obtain spatial proximity and spatial homogenous patterns based on particle swarm optimization (PSO). Then, a hierarchical clustering method is employed to obtain clusters with similar attributes. The hierarchical clustering method adopts a discriminating coefficient to adaptively control the depth of the hierarchical architecture. The clustering results are further refined using an optimization approach. The advantages and practicability of the ADC+ algorithm are illustrated by experiments on both simulated datasets and real-world applications. It is found that the proposed ADC+ algorithm can adaptively and accurately detect clusters with arbitrary shapes, similar attributes and densities under the consideration of barriers.

ACS Style

Yaolin Liu; Xiaomi Wang; Dianfeng Liu; Leilei Liu. An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning. Transactions in GIS 2016, 21, 916 -933.

AMA Style

Yaolin Liu, Xiaomi Wang, Dianfeng Liu, Leilei Liu. An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning. Transactions in GIS. 2016; 21 (5):916-933.

Chicago/Turabian Style

Yaolin Liu; Xiaomi Wang; Dianfeng Liu; Leilei Liu. 2016. "An adaptive dual clustering algorithm based on hierarchical structure: A case study of settlement zoning." Transactions in GIS 21, no. 5: 916-933.

Journal article
Published: 10 November 2016 in ISPRS International Journal of Geo-Information
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Current time series clustering algorithms fail to effectively mine clustering distribution characteristics of time series data without sufficient prior knowledge. Furthermore, these algorithms fail to simultaneously consider the spatial attributes, non-spatial time series attribute values, and non-spatial time series attribute trends. This paper proposes an adaptive density-based time series clustering (DTSC) algorithm that simultaneously considers the three above-mentioned attributes to relieve these limitations. In this algorithm, the Delaunay triangulation is first utilized in combination with particle swarm optimization (PSO) to adaptively obtain objects with similar spatial attributes. An improved density-based clustering strategy is then adopted to detect clusters with similar non-spatial time series attribute values and time series attribute trends. The effectiveness and efficiency of the DTSC algorithm are validated by experiments on simulated datasets and real applications. The results indicate that the proposed DTSC algorithm effectively detects time series clusters with arbitrary shapes and similar attributes and densities while considering noises.

ACS Style

Xiaomi Wang; Yaolin Liu; Yiyun Chen; Yi Liu. An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns. ISPRS International Journal of Geo-Information 2016, 5, 205 .

AMA Style

Xiaomi Wang, Yaolin Liu, Yiyun Chen, Yi Liu. An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns. ISPRS International Journal of Geo-Information. 2016; 5 (11):205.

Chicago/Turabian Style

Xiaomi Wang; Yaolin Liu; Yiyun Chen; Yi Liu. 2016. "An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns." ISPRS International Journal of Geo-Information 5, no. 11: 205.

Journal article
Published: 18 July 2016 in Landslides
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A Kriging-based surrogate model provides a logically strict and efficient tool to evaluate the system reliability of a slope. However, the constant trend function adopted in the ordinary Kriging (OK) cannot always well capture the nonlinear non-smooth properties of a slope stability problem. Although the universal Kriging (UK) with a linear or a quadratic trend function could be an alternative for some cases, a higher order nonlinear trend function is preferable for some more complicated nonlinear non-smooth cases in the slope stability analysis. To address this problem, a genetic algorithm (GA) optimized Taylor Kriging (TK) surrogate model is proposed for the system reliability analysis of soil slopes in this paper. The proposed surrogate model allows a unified framework of the Kriging, considering different extents of nonlinear properties according to the Taylor expansion order (e.g., can be as high as the fourth order). The GA is introduced to search for the optimal correlation parameters, of which the effectiveness is verified by an analytical example. The feasibility of the proposed surrogate model is then validated by two analytical examples before its application to the practical slope reliability analyses. The results show that the UK model can be incorporated into the TK model, and the TK model provides a higher accuracy and efficiency when facing the highly nonlinear slope stability problems. It is also found that the UK model cannot fully capture the potential nonlinear properties existed in a slope stability model as compared with the higher order TK model.Department of Civil and Environmental Engineering2016-2017 > Academic research: refereed > Publication in refereed journalbcr

ACS Style

Leilei Liu; Yungming Cheng; Xiaomi Wang. Genetic algorithm optimized Taylor Kriging surrogate model for system reliability analysis of soil slopes. Landslides 2016, 14, 535 -546.

AMA Style

Leilei Liu, Yungming Cheng, Xiaomi Wang. Genetic algorithm optimized Taylor Kriging surrogate model for system reliability analysis of soil slopes. Landslides. 2016; 14 (2):535-546.

Chicago/Turabian Style

Leilei Liu; Yungming Cheng; Xiaomi Wang. 2016. "Genetic algorithm optimized Taylor Kriging surrogate model for system reliability analysis of soil slopes." Landslides 14, no. 2: 535-546.