The Use of Artificial Intelligence to Optimise Water Resources: A Comprehensive Assessment DOI

Fouad Dimane,

Yahya El Hammoudani, Lahcen Benaabidate

et al.

Lecture notes in geoinformation and cartography, Journal Year: 2024, Volume and Issue: unknown, P. 239 - 257

Published: Jan. 1, 2024

Language: Английский

Water quality prediction in the Yellow River source area based on the DeepTCN-GRU model DOI
Qingqing Tian,

Wei Luo,

Lei Guo

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 59, P. 105052 - 105052

Published: March 1, 2024

Language: Английский

Citations

21
Fine Estimation of Water Quality in the Yangtze River Basin Based on a Geographically Weighted Random Forest Regression Model DOI Creative Commons

Fuliang Deng,

Wenhui Liu,

Mei Sun

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 731 - 731

Published: Feb. 19, 2025

Water quality evaluation usually relies on limited state-controlled monitoring data, making it challenging to fully capture variations across an entire basin over time and space. The fine estimation of water in a spatial context presents promising solution this issue; however, traditional analyses often ignore non-stationarity between variables. To solve the above-mentioned problems mapping research, we took Yangtze River as our study subject attempted use geographically weighted random forest regression (GWRFR) model couple massive station observation data auxiliary carry out quality. Specifically, first utilized sections’ input for GWRFR train map six indicators at 30 m resolution. We then assessed various geographical environmental factors contributing identified differences. Our results show accurate predictions all indicators: ammonia nitrogen (NH3-N) had lowest accuracy (R2 = 0.61, RMSE 0.13), total (TN) highest 0.74, 0.48). reveal primary pollutant basin. Chemical oxygen demand permanganate index were mainly influenced by natural factors, while phosphorus impacted human activities. distribution critical influencing shows significant clustering. Overall, demonstrates provides insights into that are crucial comprehensive management environments.

Language: Английский

Citations

0
Combining POA-VMD for multi-machine learning methods to predict ammonia nitrogen in the largest freshwater lake in China (Poyang Lake) DOI
Chengming Luo,

Xihua Wang,

Y. Jun Xu

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 72, P. 107511 - 107511

Published: March 22, 2025

Language: Английский

Citations

0
Advanced Deep Learning Model for Predicting Water Pollutants Using Spectral Data and Augmentation Techniques: A Case Study of the Middle and Lower Yangtze River, China DOI
Gengxin Zhang, Cailing Wang, Hongwei Wang

et al.

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 107058 - 107058

Published: March 1, 2025

Language: Английский

Citations

0
Next-generation reservoir computing water quality prediction model based on the whale optimization algorithm DOI
Junyu Zhou, Lijun Pei, Zhiwei Zheng

et al.

International Journal of Dynamics and Control, Journal Year: 2025, Volume and Issue: 13(4)

Published: March 27, 2025

Language: Английский

Citations

0
An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction DOI Creative Commons
Fei Ding, Shilong Hao,

Mingcen Jiang

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103126 - 103126

Published: April 1, 2025

Language: Английский

Citations

0
Identifying TSM dynamics in arid inland lakes combining satellite imagery and wind speed DOI

Ashkan Noori,

Yusef Kheyruri, Ahmad Sharafati

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132423 - 132423

Published: Nov. 1, 2024

Language: Английский

Citations

2
Long-Term AI Prediction of Ammonium Levels in River Lee in London Using Transformer and Ensemble Models DOI Creative Commons
Ali J. Ali, Ashraf Ahmed

Cleaner Water, Journal Year: 2024, Volume and Issue: unknown, P. 100051 - 100051

Published: Oct. 1, 2024

Language: Английский

Citations

1
Deployment of Random Forest Algorithm for prediction of ammonia in river water DOI Creative Commons

S Soumya,

Nilufer Tamatgar, Ravilla Dilli

et al.

Published: Feb. 1, 2024

The fascinating aspect of machine learning (ML) is its diverse application. ML models are most useful when it comes to the conservation natural resources through sustainable usage. An essential resource, water vital life as we know it. Ammonia poses a serious hazard aquatic and primary source pollution in waterways. To estimate ammonia content river waters, algorithms used this study. After testing training many regression models, Flask API deploy model that fits data best. Based on values pH, DO (dissolved oxygen), COD (chemical oxygen demand), website shows amount water.

Language: Английский

Citations

0
The Use of Artificial Intelligence to Optimise Water Resources: A Comprehensive Assessment DOI

Fouad Dimane,

Yahya El Hammoudani, Lahcen Benaabidate

et al.

Lecture notes in geoinformation and cartography, Journal Year: 2024, Volume and Issue: unknown, P. 239 - 257

Published: Jan. 1, 2024

Language: Английский

Citations

0