Farmers’ Water Poverty Measurement and Analysis of Endogenous Drivers

Shen, Jinlong; Li, Jiafen; Zhang, Yu; Song, Jianfeng

doi:10.1007/s11269-023-03554-5

Water Resour Manage

2023

DOI: 10.1007/s11269-023-03554-5

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Farmers’ Water Poverty Measurement and Analysis of Endogenous Drivers

Jinlong Shen,

Jiafen Li,

Yu Zhang

et al.

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Cited by 2 publications

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“…Clean water and sanitation correspond to the 6th goal of the UN Sustainable Development Goals [30]. This goal has received considerable critical attention from the scientific community to investigate and publish studies in order to help ensure the availability and the sustainable management of water resources and sanitation for the world [31][32][33][34][35].…”

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confidence: 99%

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

Marinho,

Júnior,

Dias

et al. 2023

Remote Sensing

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Dimensionality reduction is one of the most used transformations of data and plays a critical role in maintaining meaningful properties while transforming data from high- to low-dimensional spaces. Previous studies, e.g., on image analysis, comparing data from these two spaces have found that, generally, any study related to anomaly detection can achieve the same or similar results when applied to both dimensional spaces. However, there have been no studies that compare differences in these spaces related to anomaly detection strategy based on Kittler’s Taxonomy (ADS-KT). This study aims to investigate the differences between both spaces when dimensionality reduction is associated with ADS-KT while analyzing a satellite image. Our methodology starts applying the pre-processing phase of the ADS-KT to create the high-dimensional space. Next, a dimensionality reduction technique generates the low-dimensional space. Then, we analyze extracted features from both spaces based on visualizations. Finally, machine-learning approaches, in accordance with the ADS-KT, produce results for both spaces. In the results section, metrics assessing transformed data present values close to zero contrasting with the high-dimensional space. Therefore, we conclude that dimensionality reduction directly impacts the application of the ADS-KT. Future work should investigate whether dimensionality reduction impacts the ADS-KT for any set of attributes.

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confidence: 99%

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

Marinho,

Júnior,

Dias

et al. 2023

Remote Sensing

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Evaluation of rural human settlements in China based on the combined model of DPSIR and PLS-SEM

Zhang,

Liu,

Guo

2024

Environmental Impact Assessment Review

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Farmers’ Water Poverty Measurement and Analysis of Endogenous Drivers

Cited by 2 publications

References 30 publications

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

Evaluation of rural human settlements in China based on the combined model of DPSIR and PLS-SEM

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