Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: model optimization and analysis of key characteristic variables DOI

Xie Guixian,

Chi Zhu,

Chen Li

и другие.

Environmental Research, Год журнала: 2024, Номер unknown, С. 120618 - 120618

Опубликована: Дек. 1, 2024

Язык: Английский

Risk assessment of potentially toxic elements and mapping of groundwater pollution indices using soft computer models in an agricultural area, Northeast Algeria DOI
Azzeddine Reghais, Abdelmalek Drouiche, Faouzi Zahi

и другие.

Journal of Hazardous Materials, Год журнала: 2025, Номер unknown, С. 137991 - 137991

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
A time-averaged method to analyze slender rods moving in tubes DOI
Feng Wu, Ke Zhao,

Xuanlong Wu

и другие.

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 279, С. 109510 - 109510

Опубликована: Июнь 24, 2024

Язык: Английский

Процитировано

6
Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization DOI Creative Commons
Hüseyin Çağan Kılınç,

Sina Apak,

Mahmut Esad Ergin

и другие.

Acta Geophysica, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Язык: Английский

Процитировано

0
Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea DOI Creative Commons
Shuo Wang, Kehu Yang, Hui Peng

и другие.

Frontiers in Marine Science, Год журнала: 2025, Номер 12

Опубликована: Май 9, 2025

The Yellow River is the largest inflow into Bohai Sea, and its changes directly affect ecological environment marine health of Sea. Therefore, accurate prediction crucial for maintaining balance Sea protecting resources. Time decomposition algorithms, combined with machine learning, are effective tools to enhance capabilities models. However future data leakage from items was ignored in many studies. It necessary develop right method operate time avoid leakage. In this study, sea predicted based on a learning model (light gradient boosting machine, LightGBM) algorithm (seasonal trend using loess, STL), different ways STL were evaluated. results showed that overall performance STL–LightGBM better than LightGBM model. took historical 8 days as input, average NSE next 1–7 would reach 0.720. Even when forecast period 7 days, (NSE: 0.549 7-day lead time) 0.105 higher 0.444 time). We found pretreatment entire test set overestimated true STL–LightGBM. recommended preprocesses each sample study can provide help water resources management offshore environmental management.

Язык: Английский

Процитировано

0
A hybrid deep learning model based on signal decomposition and dynamic feature selection for forecasting the influent parameters of wastewater treatment plants DOI
Yinglong Chen, Hongling Zhang,

You Yang

и другие.

Environmental Research, Год журнала: 2024, Номер 266, С. 120615 - 120615

Опубликована: Дек. 13, 2024

Язык: Английский

Процитировано

3
An enhanced gene expression programming for daily water consumption forecasting with new chromosome structure DOI
Qingshuai Sun, Yingjie Zhang, Biliang Lu

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 66, С. 105873 - 105873

Опубликована: Авг. 7, 2024

Язык: Английский

Процитировано

2
Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: model optimization and analysis of key characteristic variables DOI

Xie Guixian,

Chi Zhu,

Chen Li

и другие.

Environmental Research, Год журнала: 2024, Номер unknown, С. 120618 - 120618

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

2