Environmental planning and the evolution of inter-basin water transfers in the United States DOI Creative Commons
Sooyeon Yi,

G. Mathias Kondolf

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: Nov. 12, 2024

The uneven spatial distribution of water resources and demands across the US have motivated a wide range inter-basin transfers. By redistributing resources, Inter-basin transfer projects can lead to specific environmental changes such as altered river flows, in quality, loss ecologically important habitats, impacts which depend on project scale management. Early were undertaken prior legislation Since primary focus is not these projects, they are often documented historically. We provided comprehensive inventory (built, incomplete, proposed) US, identified patterns projects’ characteristics, analyzed growing role planning drew lessons inform future proposals. categorized historical into three periods: 1900–1930, 1930–1970, 1970–2020, analyzing over 40 km long 50 MCM/year using diverse sources, assess their development from an perspective. Results this study show that early mostly gravity-driven smaller-in-scale, grow require more pumping stations (energy-intensive) lift high elevations. California Colorado most active, transfers for first time. Federal agencies reduced funding due recognition impacts, adequately addressed projects. Environmental crucial operation recommend assessments climate change vulnerability should also be considered essential

Language: Английский

Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM DOI
Min Gan, Xijun Lai, Yan Guo

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(13), P. 5305 - 5321

Published: June 18, 2024

Language: Английский

Citations

8
Predicting water level fluctuations in glacier-fed lakes by ensembling individual models into a quad-meta model DOI Creative Commons

Shoukat Ali Shah,

Songtao Ai, Huanxin Yuan

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 8, 2025

Predicting water levels in glacier-fed lakes is vital for resource management, flood forecasting, and ecological balance. This study examines the predictive capacity of multiple climate factors affecting Blue Moon Lake Valley, fed by Baishui River glacier on Yulong Snow Mountain. The introduces a novel quad-meta (QM) ensemble model that integrates outputs from four machine learning models – extreme gradient boosting (XGB), random forest (RF), (GBM), decision tree (DT) through meta-learning to improve prediction accuracy under complex environmental conditions. High-frequency depth data, recorded every five minutes using an RBR logger, alongside variables such as temperature, wind speed, humidity, evaporation, solar radiation, rainfall, were analyzed. Temperature was identified most significant factor influencing levels, with importance score 15.69, followed atmospheric pressure (14.08) radiation (12.89), which impacted surface conditions evaporation. Relative humidity (10.24) speed (8.71) influenced lake stability mixing. QM outperformed individual models, achieved RMSE values 0.003 m (climate data) 0.001 (water data), R2 0.994 0.999, respectively. In comparison, XGB GBM exhibited higher lower scores. RF struggled 0.008 0.962, while DT performed better (RMSE: 0.006 but remained inferior proposed model. These findings demonstrate robustness approach handling particularly where fall short. highlights potential enhanced systems, recommending future research directions incorporate deep long-term forecasting expand capabilities global scale.

Language: Английский

Citations

1
Prediction of spatial-temporal flood water level in agricultural fields using advanced machine learning and deep learning approaches DOI

Adisa Hammed Akinsoji,

Bashir Adelodun, Qudus Adeyi

et al.

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Language: Английский

Citations

1
Projection of groundwater level fluctuations using deep learning and dynamic system response models in a drought affected area DOI
Dilip Roy,

Chitra Rani Paul,

Md. Panjarul Haque

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

Citations

0
Assessing groundwater level variability in response to climate change: A case study of large plain areas DOI Creative Commons

Hai Bin Wu,

Xueyan Ye,

Xinqiang Du

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 57, P. 102180 - 102180

Published: Jan. 9, 2025

Language: Английский

Citations

0
Spatial heterogeneity of groundwater depths in coastal cities and their responses to multiple factors interactions by interpretable machine learning models DOI Creative Commons

Yuming Mo,

Jing Xu,

Senlin Zhu

et al.

Geoscience Frontiers, Journal Year: 2025, Volume and Issue: unknown, P. 102033 - 102033

Published: Feb. 1, 2025

Language: Английский

Citations

0
Overviewing the Machine Learning Utilization on Groundwater Research Using Bibliometric Analysis DOI Open Access
Kayhan Bayhan, Eyyup Ensar Başakın, Ömer Ekmekcioğlu

et al.

Water, Journal Year: 2025, Volume and Issue: 17(7), P. 936 - 936

Published: March 23, 2025

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled commercial use. In recent years, due to pressures from climate change excessive urbanization, a noticeable decline in groundwater levels has been observed, particularly arid semi-arid regions. The corresponding changes have analyzed using diverse range methodologies, including data-driven modeling techniques. Recent evidence shown notable acceleration utilization such advanced techniques, demonstrating significant attention by research community. Therefore, major aim present study conduct bibliometric analysis investigate application evolution machine learning (ML) techniques research. this sense, studies published between 2000 2023 were examined terms scientific productivity, collaboration networks, themes, methods. findings revealed that ML offer high accuracy predictive capacity, especially quality, level estimation, pollution modeling. United States, China, Iran stand out as leading countries emphasizing strategic importance management. However, outcomes demonstrated low international cooperation led deficiencies solving transboundary problems. aimed encourage more widespread effective use management environmental planning processes drew transparent interpretable algorithms, with potential yield rewarding opportunities increasing adoption technologies decision-makers.

Language: Английский

Citations

0
Interaction Modeling of Surface Water and Groundwater: An Evaluation of Current and Future Issues DOI

Noureen Khurshid,

Rohitashw Kumar, Dinesh Kumar Vishwakarma

et al.

Water Conservation Science and Engineering, Journal Year: 2025, Volume and Issue: 10(1)

Published: April 1, 2025

Language: Английский

Citations

0
Impacts of climate extremes on variations in evergreen forest ecosystem carbon–water fluxes across Southern China DOI
Wanqiu Xing,

Zhiyu Feng,

Wei Jia

et al.

Global and Planetary Change, Journal Year: 2025, Volume and Issue: unknown, P. 104867 - 104867

Published: May 1, 2025

Language: Английский

Citations

0
Reservoir-based flood forecasting and warning: deep learning versus machine learning DOI Creative Commons
Sooyeon Yi, Jaeeung Yi

Applied Water Science, Journal Year: 2024, Volume and Issue: 14(11)

Published: Oct. 15, 2024

In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making support sustainable development. This study seeks improve reliability of reservoir-based ensure adequate lead time for effective measures. The main objectives are predict hourly downstream discharge at a reference point, compare predictions from single reservoir with four-hour against those three reservoirs seven-hour time, evaluate accuracy data-driven approaches. takes place in Han River Basin, located Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), vector regression (SVR)) deep (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data reservoirs, while 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R2) better than SVR, GRU 4.69% LSTM 1. 2, none models showed any outstanding performance. Based these findings, we propose two-step approach: Initial should utilize upstream long closer event, model focus more accurate prediction. work stands as significant contribution, making well-timed local administrations issue warnings execute evacuations mitigate damage casualties areas.

Language: Английский

Citations

2