High-frequency data significantly enhances the prediction ability of point and interval estimation DOI
Xin Liu,

Fu-Jun Yue,

Tian-Li Guo

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 912, P. 169289 - 169289

Published: Dec. 21, 2023

Language: Английский

Predicting and analyzing the algal population dynamics of a grass-type lake with explainable machine learning DOI
Hao Cui, Yiwen Tao, Jian Li

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120394 - 120394

Published: Feb. 26, 2024

Language: Английский

Citations

9
Tracing spatial patterns of lacustrine groundwater discharge in a closed inland lake using stable isotopes DOI

Xiaohui Ren,

Ruihong Yu, Rui Wang

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120305 - 120305

Published: Feb. 14, 2024

Language: Английский

Citations

8
Novel Groundwater Quality Index (GWQI) model: A Reliable Approach for the Assessment of Groundwater DOI Creative Commons
Abdul Majed Sajib, Apoorva Bamal, Mir Talas Mahammad Diganta

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104265 - 104265

Published: Feb. 1, 2025

Language: Английский

Citations

1
A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement DOI
Rui Xu,

Shengri Hu,

Hang Wan

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119894 - 119894

Published: Dec. 27, 2023

Language: Английский

Citations

18
Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach DOI Creative Commons
Md Galal Uddin, M. M. Shah Porun Rana, Mir Talas Mahammad Diganta

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33082 - e33082

Published: June 19, 2024

Monitoring of groundwater resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization quality index (WQI) models has proven effective monitoring resources, it faced substantial criticism due to its inconsistent outcomes, prompting need more reliable assessment methods. Therefore, this study addresses concern by employing data-driven root mean squared (RMS) evaluate Bhola district near Bay Bengal, Bangladesh. To enhance reliability RMS-WQI model, research incorporated extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For GWQ, utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3-), ammonium (NH4+), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), iron (Fe). In terms GW concentration K, Ca Mg exceeded guideline limit collected samples. The computed scores ranged from 54.3 72.1, with an average 65.2, categorizing all sampling sites' GWQ as "fair." model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) predicting accurately. Furthermore, exhibited minimal uncertainty (<1%) WQI scores. These findings implied efficacy accurately assessing areas, that would ultimately assist regional managers strategic planners sustainable management resources.

Language: Английский

Citations

8
Unraveling the impacts of multiscale landscape patterns and socioeconomic development on water quality: A case study of the National Sustainable Development Agenda Innovation Demonstration Zone in Lincang City, Southwest China DOI Creative Commons

Xuefu Pu,

Qingping Cheng

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 51, P. 101660 - 101660

Published: Jan. 19, 2024

National Sustainable Development Agenda Innovation Demonstration Zone in Lincang City, Southwest China Revealing the current water quality status rivers and reservoirs its drivers is crucial to achieving United Nations Goal 6 (SDG 6). We assessed spatial-temporal dynamics influence of natural socioeconomic factors on index (WQI) parameters using redundancy analysis (RDA) partial least squares path model (PLSPM). The results indicate following. (1) annual average values WQI City from 2018 2020 were 92.26, 92.06, 92.45, respectively. spring, summer, autumn, winter 92.48, 90.38, 92.68, 93.49, seasonal levels good or higher. However, spatial heterogeneity exists for some City. (2) highly complex. landscape composition, configuration, pollutant discharges are key affecting annually seasonally, a scale dependence observed. (3) Chemical physical directly affect WQI, particularly at small scales (100 m 500 buffer zones). have strong inhibitory effect (−0.79, −0.78) (−0.56, −0.72), whereas weak promoting (0.05–0.13). Landscape composition configuration indirectly WQI. In contrast, pollution discharge impact through their chemical factors. These findings demonstrate that social interact multiple ways, impacting reservoirs. This interaction depends scale. Therefore, it consider appropriate distance future land use planning, design planning Moreover, controlling wastewater industrial agricultural activities domestic usage vital ensuring high quality.

Language: Английский

Citations

6
Measuring the impact of responsible factors on CO2 emission using generalized additive model (GAM) DOI Creative Commons

Ruhul Amin,

Md. Sifat Ar Salan, Md. Moyazzem Hossain

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e25416 - e25416

Published: Feb. 1, 2024

The indicators of economic and sustainable development ultimately significantly depend on carbon dioxide (CO2) emissions in every country. In Bangladesh, there is an increasing trend population, industrialization, as well electricity demand generated from different sources, CO2 emissions. This study explores the relationship between other significant relevant indicators. Moreover, authors aimed to identify which model effective at predicting assess accuracy prediction models. secondary data 1971 2020, was collected World Bank Bangladesh Road Transport Authority's publicly accessible website. generalized additive (GAM), polynomial regression (PR), multiple linear (MLR) were used for modeling performance evaluated using Bayesian information criterion (BIC), Akaike (AIC), Root mean square error (RMSE), R-square, (MSE). Results revealed that are few multicollinearity problems datasets exhibit a nonlinear among Among models considered this study, GAM has lowest value RMSE = 0.008, MSE 0.000063, AIC −303.21, BIC −266.64 highest R-squared 0.996 compared MLR PR models, suggesting most appropriate Bangladesh. Findings total risk factors non-linear. suggests Generalized technique can be tool believed findings would helpful policymakers designing strategies areas low-carbon economy, encouraging use renewable energy focusing technological advancement reduces ensures environment

Language: Английский

Citations

6
Ecological restoration for eutrophication mitigation in urban interconnected water bodies: Evaluation, variability and strategy DOI
Linlin Wang,

Huaihao Shao,

Yuehua Guo

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 365, P. 121475 - 121475

Published: June 20, 2024

Language: Английский

Citations

6
Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model DOI
Ying Dong, Yuhuan Sun, Zhenkun Liu

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119807 - 119807

Published: Dec. 14, 2023

Language: Английский

Citations

16
Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater DOI
Huanhuan Shi, Yao Du, Yueping Li

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 358, P. 120853 - 120853

Published: April 11, 2024

Language: Английский

Citations

5