Study on the pattern and driving factors of water scarcity risk transfer networks in China from the perspective of transfer value—Based on complex network methods DOI
Changfeng Shi, Jiahui Qi,

Jiaqi Zhi

et al.

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 112, P. 107752 - 107752

Published: Dec. 3, 2024

Language: Английский

Water quality prediction based on sparse dataset using enhanced machine learning DOI Creative Commons
Huang Sheng, Jun Xia, Yueling Wang

et al.

Environmental Science and Ecotechnology, Journal Year: 2024, Volume and Issue: 20, P. 100402 - 100402

Published: March 1, 2024

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water data, less attention is given to areas that lack sufficient data. Therefore, it crucial explore novel ways of managing source-oriented pollution scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction using machine learning. We investigated the efficacy traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated Load Estimator (LOADEST). The research was conducted at river-lake confluence, an area intricate hydrological patterns. found Self-Attentive LSTM (SA-LSTM) model outperformed other learning models predicting quality, achieving Nash-Sutcliffe Efficiency (NSE) scores 0.71 for CODMn and 0.57 NH3N when utilizing LOADEST-augmented (referred SA-LSTM-LOADEST model). improved upon standalone SA-LSTM by reducing Root Mean Square Error (RMSE) 24.6% 21.3% NH3N. Furthermore, maintained its predictive accuracy intervals were extended from Additionally, demonstrated capability forecast loads up ten days advance. This study shows promise improving modeling limited monitoring capabilities.

Language: Английский

Citations

9
Evaluating suitability of development and construction with of minimum cumulative resistance model for a mountain scenic area in Jinyun Xiandu, China DOI

Hanxu Fu,

Tong Zhang, Wang Jian-guo

et al.

Ecological Engineering, Journal Year: 2024, Volume and Issue: 202, P. 107240 - 107240

Published: March 30, 2024

Language: Английский

Citations

5
Hydrochemistry dynamics in a glacierized headwater catchment of Lhasa River, Tibetan Plateau DOI

Li Mingyue,

Xuejun Sun, Wei Li

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 919, P. 170810 - 170810

Published: Feb. 8, 2024

Language: Английский

Citations

4
Integrating machine learning with the Minimum Cumulative Resistance Model to assess the impact of urban land use on road waterlogging risk DOI

Xiaotian Qi,

Soon‐Thiam Khu,

Pei Yu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132842 - 132842

Published: Feb. 1, 2025

Language: Английский

Citations

0
Mineralogical and Chemical Characteristics of Sediments in the Lhasa River Basin: Implications for Weathering and Sediment Transport DOI Open Access

Haichen Zhang,

Tianning Li,

Changping Mao

et al.

Water, Journal Year: 2025, Volume and Issue: 17(4), P. 581 - 581

Published: Feb. 18, 2025

The Lhasa River, as one of the major rivers on Tibetan Plateau, is great value for study climate and environmental changes Plateau. In this paper, grain size mineralogical geochemical characteristics sediments from River were investigated. results show following: (1) average coarse (65.5% sand, 23.6% silt), sorting overall poor; skewness mostly positive, kurtosis wide, which reflects obvious river sand deposition. (2) mineral composition dominated by quartz (38.4%), feldspar, plagioclase followed clay minerals, content carbonate minerals relatively low; in illite high 83.3%, while chlorite slightly higher than kaolinite, smectite very low. chemical index less 0.4, indicating that mainly iron-rich magnesium illite. (3) weathering (CIA) low, implying are a weak–moderate state physical weathering. Comprehensive analyses further revealed process was influenced both lithology, i.e., sediment not only dry, cold but also granites exposed over large areas. can provide scientific references in-depth research climatic effects

Language: Английский

Citations

0
How much carbon storage will loss in a desertification area? Multiple policy scenario analysis from Gansu Province DOI
Jiamin Liu,

Xiutong Pei,

Wei–Jie Yu

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 913, P. 169668 - 169668

Published: Dec. 30, 2023

Language: Английский

Citations

9
Exploring the path to the sustainable development of cold chain logistics for fresh agricultural products in China DOI
Xuemei Fan, Yingdan Zhang,

Jiahui Xue

et al.

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 108, P. 107610 - 107610

Published: Aug. 3, 2024

Language: Английский

Citations

3
Hydrochemical characteristics, evolution and health risk assessment of surface water and groundwater in Lhasa, China DOI
Tao Zhang, Mingguo Wang, Jin He

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(6)

Published: March 1, 2024

Language: Английский

Citations

2
Evaluating the surface water pollution risk of mineral resource exploitation via an improved approach: a case study in Liaoning Province, Northeastern China DOI

Dong Huang -,

Tianyi Pang,

Xue Bai

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(8)

Published: July 19, 2024

Language: Английский

Citations

1
Assessment and modeling of roadside geological risks in the Qinghai-Tibetan Plateau region DOI
Hong Zhang, Xin Xu, Chi Zhang

et al.

Transportation Research Part D Transport and Environment, Journal Year: 2024, Volume and Issue: unknown, P. 104422 - 104422

Published: Sept. 1, 2024

Language: Английский

Citations

0