Chicken moth flame optimization and region-based convolution neural network for water quality prediction DOI

D. Justin Jose,

C. Helen Sulochana

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Дек. 19, 2024

Язык: Английский

Integrated PCA–RNN approach for surface water quality assessment in the Mahanadi river system DOI
Rosysmita Bikram Singh, Kanhu Charan Patra

International Journal of Environmental Science and Technology, Год журнала: 2024, Номер 21(11), С. 7701 - 7716

Опубликована: Фев. 27, 2024

Язык: Английский

Процитировано

11
Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis DOI
Mahmood Fooladi, Mohammad Reza Nikoo, Rasoul Mirghafari

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 362, С. 121259 - 121259

Опубликована: Июнь 1, 2024

Язык: Английский

Процитировано

11
AI-driven modelling approaches for predicting oxygen levels in aquatic environments DOI Creative Commons
Rosysmita Bikram Singh, Agnieszka I. Olbert, Avinash Samantra

и другие.

Journal of Water Process Engineering, Год журнала: 2024, Номер 66, С. 105940 - 105940

Опубликована: Авг. 13, 2024

Язык: Английский

Процитировано

10
Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China DOI
Wei Qing, Ju Rui Yang, Fangbing Fu

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 374, С. 124121 - 124121

Опубликована: Янв. 15, 2025

Язык: Английский

Процитировано

1
Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model DOI
Tao Hai, Iman Ahmadianfar, Bijay Halder

и другие.

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(22), С. 32382 - 32406

Опубликована: Апрель 23, 2024

Язык: Английский

Процитировано

4
Presiones antropogénicas en la Fisicoquímica del Socioecosistema Lagunar de Nuxco, Guerrero, México DOI Creative Commons
José Ángel Vences Martínez, Benjamín Castillo Elías, Enrique J. Flores-Munguía

и другие.

Ingeniería del agua, Год журнала: 2025, Номер 29(1), С. 57 - 72

Опубликована: Янв. 30, 2025

El presente estudio evaluó el estado fisicoquímico de la Laguna Nuxco, Guerrero, México, y su relación con actividades antropogénicas circundantes (agropecuarias urbanización). Se colectaron cinco muestras mensualmente durante un año, a una profundidad 15-30 cm. determinaron 5 parámetros in situ 7 en laboratorio. La laguna es catalogada como contaminada basado los datos Demanda Bioquímica Oxígeno (DBO5) Química (DQO). Asimismo, se encontró alta concentración nutrientes Nitrógeno amoniacal (0.63 mg/L), Nitratos (0.15 mg/L) Nitritos (21.64 cuales rebasan límites máximos permisibles del Acuerdo CE-CCA-001/89, lo que sugiere contaminación debido descarga aguas residuales, arrastre plaguicidas fertilizantes utilizados agricultura, así por ganadería acuacultura. Nuxco tiene dinámica fisicoquímica influenciada significativamente diversos factores antropogénicos, necesidad estrategias manejo integral.

Процитировано

0
Developing a real-time water quality simulation toolbox using machine learning and application programming interface DOI

Gi-Hun Bang,

Na-Hyeon Gwon,

Min‐Jeong Cho

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 377, С. 124719 - 124719

Опубликована: Фев. 28, 2025

Язык: Английский

Процитировано

0
A review of recent hybridized machine learning methodologies for time series forecasting on water-related variables DOI
Van Kwan Zhi Koh, Ye Li, Xing Yong Kek

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132909 - 132909

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
A Mamba-based method for multi-feature water quality prediction fusing dual denoising and attention enhancement DOI

Xianbao Tan,

Yulong Bai, Xin Yue

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133424 - 133424

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models DOI Creative Commons

Yubo Zhao,

Mo Chen

PLoS ONE, Год журнала: 2025, Номер 20(3), С. e0319256 - e0319256

Опубликована: Март 4, 2025

Too low a concentration of dissolved oxygen (DO) in river can disrupt the ecological balance, while too high may lead to eutrophication water body and threaten health aquatic environment. Therefore, accurate prediction DO is crucial for resource protection. In this study, hybrid machine learning model prediction, called DWT-KPCA-GWO-XGBoost, proposed, which combines discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), extreme gradient boosting (XGBoost). Firstly, DWT-db4 was used denoise noisy quality feature data; secondly, meteorological data were simplified into four components by KPCA; finally, features inputted GWO-optimized XGBoost as training prediction. The performance comprehensively assessed comparison with other models using MAE, MSE, MAPE, NSE, KGE WI evaluation metrics. tested at three different locations results showed that outperformed models, performing follows: 0.5925, 0.6482, 6.3322, 0.8523, 0.8902, 0.9403; 0.4933, 0.4325, 6.2351, 0.8952, 0.7928, 0.8632; 0.2912, 0.2001, 4.0523, 0.7823, 0.8425, 0.8463 PICP values exceed 95%. demonstrated significant predicting concentrations next 15 days. Compared studies, we innovatively improved accuracy significantly through noise removal introduction multi-source features.

Язык: Английский

Процитировано

0