Assessing integrated water reuse efficiency towards SDG6 and influencing factors DOI
Feng Chen,

Fengping Wu,

Lina Zhang

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 373, P. 123938 - 123938

Published: Dec. 30, 2024

Language: Английский

Fostering inclusive green growth in China: Identifying the impact of the regional integration strategy of Yangtze River Economic Belt DOI
Yanchao Feng,

Mengmin Sun,

Yuxi Pan

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 358, P. 120952 - 120952

Published: April 23, 2024

Language: Английский

Citations

56
Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment DOI
Ujala Ejaz, Shujaul Mulk Khan,

Sadia Jehangir

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 450, P. 141877 - 141877

Published: March 28, 2024

Language: Английский

Citations

15
PbWO4 improved the efficient photocatalytic adsorption and degradation of tetracycline and doxycycline by Cu2O DOI
Xiaojiao Yu, Zongyang Li, Zongbin Liu

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 191, P. 2725 - 2746

Published: Oct. 11, 2024

Language: Английский

Citations

8
Assessing provincial integrated wastewater treatment efficiency and influencing factors considering Triple Bottom Line DOI
Feng Chen,

Fengping Wu,

Lina Zhang

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144724 - 144724

Published: Jan. 1, 2025

Language: Английский

Citations

1
The determining mechanism of technology catch-up in China's photovoltaic (PV) industry: Machine learning approaches DOI
Xiaohui Zhao, Xiang Cai,

Cuiting Jiang

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 450, P. 142028 - 142028

Published: March 30, 2024

Language: Английский

Citations

4
Accurate inversion of chlorophyll content based on PROSPECT-LSROGF-BAS-BP method DOI Creative Commons

Shengfan Zhu,

Jin Zhang, Dan Wang

et al.

AIP Advances, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 1, 2025

Accurate measurement of chlorophyll content in plant leaves is crucial for evaluating health. Leaf radiation transfer models are commonly used to estimate from remote sensing data. However, current methods often show limited accuracy certain scenarios. This study addresses these challenges by developing a more precise method retrieval. First, the PROSPECT model, which does not fully account optical reflection on leaf surfaces, results lower spectral simulation accuracy. To overcome this limitation, surface geometric feature factor (σ) introduced, leading PROSPECT-LSROGF model. enhanced model incorporates characteristics surface, expands range light source incident angles, and accurately describes radiative within leaf. As result, shows superior traditional PIOSL models. Next, improve retrieval BP neural networks content, Beetle Antennae Search (BAS) algorithm optimize weights thresholds network, forming BAS-BP By combining with PROSPECT-LSROGF-BAS-BP developed accurate The performance compared that gradient boosting machine PROSPECT-BAS-BP Validation conducted using LOPEX93, CABO, ANGERS datasets. achieves root mean square errors (RMSEs) 4.186, 4.258, 3.894 g/cm2, determination coefficients (R2) 0.876, 0.862, 0.903, respectively—outperforming other terms These demonstrate proposed significantly improves model’s ability

Language: Английский

Citations

0
Machine learning-driven analysis of greenhouse gas emissions from rice production in major Chinese provinces: Identifying key factors and developing reduction strategies DOI

Songhua Huan,

Xiuli Liu

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 164, P. 127536 - 127536

Published: Feb. 6, 2025

Language: Английский

Citations

0
Pbwo4 Improves the Efficient Photocatalytic Adsorption and Degradation of Tetracycline and Doxycycline by Cu2o DOI

Zong yang Li,

Xiaojiao Yu, Zongbin Liu

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Predictive analysis of Somalia’s economic indicators using advanced machine learning models DOI Creative Commons
Bashir Mohamed Osman,

Abdillahi Mohamoud Sheikh Muse

Cogent Economics & Finance, Journal Year: 2024, Volume and Issue: 12(1)

Published: Nov. 8, 2024

Accurate Gross Domestic Product (GDP) prediction is essential for economic planning and policy formulation. This paper evaluates the performance of three machine learning models—Random Forest Regression (RFR), XGBoost, Prophet—in predicting Somalia's GDP. Historical data, including GDP per capita, population, inflation rate, current account balances, were used in training testing. Among models, RFR achieved best accuracy with lowest MAE (0.6621%), MSE (1.3220%), RMSE (1.1497%), R-squared 0.89. The Diebold-Mariano p-value (0.042) confirmed its higher predictive accuracy. XGBoost performed well but slightly error, yielding an 0.85 0.063. In contrast, Prophet had highest forecast errors, 0.78 0.015. For enhanced interpretability, SHapley Additive exPlanations (SHAP) applied to RFR, identifying lagged balance, population as key predictors, along total government net lending/borrowing. SHAP plots provided insights into these features' contributions predictions. study highlights RFR's effectiveness forecasting emphasizes importance indicators.

Language: Английский

Citations

0
Assessing integrated water reuse efficiency towards SDG6 and influencing factors DOI
Feng Chen,

Fengping Wu,

Lina Zhang

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 373, P. 123938 - 123938

Published: Dec. 30, 2024

Language: Английский

Citations

0