Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning DOI Creative Commons
Rui Zhang,

Qichao Zhao,

Mingyue Liu

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0319540 - e0319540

Published: March 20, 2025

Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structure, and topography, complicating research. This study introduces a novel approach combining InVEST modeling, spatiotemporal transfer Water Conservation Reserves (WCR), deep learning to uncover regional patterns driving mechanisms. The model evaluates Xiong’an New Area’s characteristics from 2000 2020, showing 74% average increase depth with an inverted “V” spatial distribution. Spatiotemporal analysis identifies temporal changes, WCR land use, key protection areas, revealing that Area primarily shifts lowest areas lower areas. potential enhancement are concentrated northern region. Deep quantifies data complexity, highlighting critical factors precipitation, drought influencing WC. detailed enables development personalized zones strategies, offering new insights into managing complex data.

Language: Английский

Enhanced Prediction of Energy Dissipation Rate in Hydrofoil-Crested Stepped Spillways Using Novel Advanced Hybrid Machine Learning Models DOI Creative Commons
Ehsan Afaridegan,

Nosratollah Amanian

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103985 - 103985

Published: Jan. 1, 2025

Language: Английский

Citations

2
Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning DOI Creative Commons
Rui Zhang,

Qichao Zhao,

Mingyue Liu

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0319540 - e0319540

Published: March 20, 2025

Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structure, and topography, complicating research. This study introduces a novel approach combining InVEST modeling, spatiotemporal transfer Water Conservation Reserves (WCR), deep learning to uncover regional patterns driving mechanisms. The model evaluates Xiong’an New Area’s characteristics from 2000 2020, showing 74% average increase depth with an inverted “V” spatial distribution. Spatiotemporal analysis identifies temporal changes, WCR land use, key protection areas, revealing that Area primarily shifts lowest areas lower areas. potential enhancement are concentrated northern region. Deep quantifies data complexity, highlighting critical factors precipitation, drought influencing WC. detailed enables development personalized zones strategies, offering new insights into managing complex data.

Language: Английский

Citations

0