Tanghui Qian’s research while affiliated with Yunnan Normal University and other places

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Publications (4)


Statistics of the number of research outcomes on water scarcity risk assessment in the “Web of Science Core Collection” database from January 1980 to December 2023
Distribution statistics of research outputs on water scarcity risk assessment in different countries or regions from January 1980 to June 2024 in the “Web of Science Core Collection” database
Cluster analysis of research outputs on water scarcity risk assessment from January 1980 to June 2024 in the “Web of Science Core Collection” database
A Review of Assessment Methods for Water Shortage Risk: Trend Analysis, Method Summary, and Future Research Prospects
  • Article
  • Publisher preview available

January 2025

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19 Reads

Water Conservation Science and Engineering

Tanghui Qian

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Liang Hong

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Shixiang Gu

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[...]

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Tianlin Tu

Driven by population growth and economic development, the demand for water resources continues to increase worldwide, leading to regional and seasonal water scarcity in many countries. Accurate assessment of water scarcity risks is essential for effective water resource risk management and allocation, depending on the continuous development and innovation of water scarcity risk evaluation methods. This study reviews the historical development of these methods, analyzes their trends, and summarizes the theoretical foundations, implementation processes, application scenarios, and limitations, providing insights for future research directions. Initially, we employ bibliometric statistics and keyword clustering analysis to investigate the spatiotemporal distribution of relevant studies and identify focal points and hotspots, uncovering relationships between publication numbers and factors such as national attention, population, and financial investment. We innovatively categorize primary evaluation methods into six types: single index method, indicator system evaluation, stochastic simulation, artificial intelligence, remote sensing and GIS, and virtual water economics. Theoretical foundations, implementation processes, and algorithm principles of each category are summarized, and application scenarios and limitations are discussed, facilitating a deeper understanding and expansion for readers. Finally, we explore and prospect key issues, including assessment and prediction of water scarcity risks under multi-scenario coupling, differences and accuracy of water scarcity risk assessments at various spatiotemporal scales, rural water scarcity risk evaluation, and integration of high-resolution remote sensing with drought limit water levels for water scarcity risk assessment, aiming to provide new directions and perspectives for future research.

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A Water Shortage Risk Assessment Model Based on Kernel Density Estimation and Copulas

May 2024

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88 Reads

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1 Citation

Water

Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research.



Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

October 2022

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102 Reads

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8 Citations

Sensors

The morphological changes in mountain glaciers are effective in indicating the environmental climate change in the alpine ice sheet. Aiming at the problems of single monitoring index and low prediction accuracy of mountain glacier deformation at present, this study takes Meili Mountain glacier in western China as the research object and uses InSAR technology to construct the mountain glacier deformation time series and 3D deformation field from January 2020 to December 2021. The relationship between glacier deformation and elevation, slope, aspect, glacier albedo, surface organic carbon content, and rainfall was revealed by grey correlation analysis. The GA-BP neural network prediction model is established from the perspective of multiple factors to predict the deformation of Meili Mountain glacier. The results showed that: The deformation of Meili Mountain glacier has obvious characteristics of spatio-temporal differentiation; the cumulative maximum deformation quantity of glaciers in the study period is −212.16 mm. After three-dimensional decomposition, the maximum deformation quantity of glaciers in vertical direction, north–south direction and east–west direction is −125.63 mm, −77.03 mm, and 107.98 mm, respectively. The average annual deformation rate is between −94.62 and 75.96 mm/year. The deformation of Meili Mountain glacier has a gradient effect, the absolute value of deformation quantity is larger when the elevation is below 4500 m, and the absolute value of deformation quantity is smaller when it is above 4500 m. The R2, MAPE, and RMSE of the GA-BP neural network to predict the deformation of Meili glacier are 0.86, 1.12%, and 10.38 mm, respectively. Compared with the standard BP algorithm, the prediction accuracy of the GA-BP neural network is significantly improved, and it can be used to predict the deformation of mountain glaciers.

Citations (2)


... Logistic regression [79] is particularly suitable for problems with a Bernoulli distributed dependent variable and is used to predict binary event probabilities. Kernel Density Estimation (KDE) [80,81] can fit probability distributions of discrete data based on data characteristics without assuming a specific distribution. The Copula function family is widely applied for multi-variate joint probability distribution calculation due to its ability to capture multi-variate correlation degree and structure [81][82][83][84][85]. Bayesian networks, as probabilistic graphical models, can represent causal relationships in water resource management risks and are used to simulate probabilistic dependencies between water scarcity risks and factors [86,87]. ...

Reference:

A Review of Assessment Methods for Water Shortage Risk: Trend Analysis, Method Summary, and Future Research Prospects
A Water Shortage Risk Assessment Model Based on Kernel Density Estimation and Copulas

Water

... Dun, J.W. et al. (Dun et al., 2023) identified active landslides on both banks of the river from Hulukou to Xiangbi Ling section in the Baihetan reservoir area before water storage using the SBAS-InSAR technology and SAR data. Based on the SBAS-InSAR technology, Yang, Z.R. et al. (Yang et al., 2022b) analyzed the effect of water storage factors on the deformation trend of potential landslide in the Baihetan reservoir area using the field survey of unmanned aerial vehicles and the Sentinel-1 SAR data set of ascending and descending tracks. ...

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

Sensors