A robust Bayesian Multi-Machine learning ensemble framework for probabilistic groundwater level forecasting DOI
Feilin Zhu, Yimeng Sun, Mingyu Han

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132567 - 132567

Published: Dec. 1, 2024

Language: Английский

Simulation of Groundwater Levels Considering Natural and Anthropologic Factors Through Deep Learning Model DOI
Shuai Li, Lin Zhu, Huili Gong

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

0

Characteristics and Mechanism of Karst Groundwater Cycle Evolution Under Large-Scale Exploitation: A Case Study of the Yanxi Karst Groundwater System DOI

Yawei Feng,

Fengxin Kang,

Fengfeng Shi

et al.

Published: Jan. 1, 2024

Study region: The Yanxi karst groundwater system in northern China.Study focus: By analyzing long timeseries monitoring data of the level, quality, and withdrawal over past 30 years, this paper aims to evaluate regime characteristics fault block guide rational exploitation utilization groundwater. Using analysis, hydrogeochemical isotope evolution under large-scale conditions is analyzed.New hydrological insights for results reveal that before after exploitation, cycle changed fundamentally, groundwaterlevel continued fall below sea table a time, main discharged from lateral runoff centralized source field. spatial distribution quality closely related surface water coal measure strata. hydrochemical components are mainly controlled by dissolution minerals Ordovician limestone and, certain extent, silicate-rock minerals. mineral precipitation concentration caused evaporation relatively weak. It urgent take series management protection measures resources curb trend environment.

Language: Английский

Citations

0

Optimizing groundwater management to prevent drawdown and sustain agricultural production using machine learning model DOI Creative Commons
Shengwei Wang,

Yu-Hsuan Kao,

Yen‐Yu Chen

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 8, 2024

Abstract This study presents a comprehensive analysis of groundwater level prediction and management using an extreme gradient boosting (XGB) model, optimized through Bayesian techniques. To address the challenge unavailable accurate pumping volume data in high-density agricultural well areas, our approach leverages power consumption as key feature for machine learning model. innovative method enables predictions based on precipitation data. mitigate significant declines during drought periods, developed XGB model offers flexible design scenarios with varying degrees extraction reduction. capability allows rapid levels, providing decision-makers powerful tool to adapt hydrological uncertainties caused by future climate change. The results testing present that increases levels 25% reduction range from 0.45 0.79 m wet season 0.99 dry season. Interestingly, percentage increases, elevations do not increase proportionally, indicating non-linear characteristics among interactions precipitation, behaviors, variations. In all three scenarios, are significantly greater than those implies appropriate reductions volumes periods can effectively prevent sharp drawdowns. Furthermore, plays crucial role formulating policies fallow subsidy programs. When considering opportunity cost labor, subsidies first second crop meet only 30% 59% economic profit, respectively. shortfall is major barrier adoption fallowing practices farmers droughts. Therefore, it enhance these make more viable attractive option farmers. conclusion, while predictive modeling robust policy decision-making, there clear need improved incentives integrated strategies.

Language: Английский

Citations

0

Has unsustainable groundwater use induced low flow regimes in the Urucuia Aquifer System? An urgent call for integrated water management DOI
André Ferreira Rodrigues, Bruno Brentan, Marta Vasconcelos Ottoni

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 370, P. 122979 - 122979

Published: Oct. 20, 2024

Language: Английский

Citations

0

A robust Bayesian Multi-Machine learning ensemble framework for probabilistic groundwater level forecasting DOI
Feilin Zhu, Yimeng Sun, Mingyu Han

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132567 - 132567

Published: Dec. 1, 2024

Language: Английский

Citations

0