Applications of machine learning in drinking water quality management: A critical review on water distribution system

Z Li, W Ma, D Zhong, J Ma, Q Zhang, Y Yuan… - Journal of Cleaner …, 2024 - Elsevier
As the final and crucial link in delivering clean water to consumers, the water distribution
system faces the risk of water quality deterioration. Conventional water quality parameter …

[HTML][HTML] The Impact of Climate Change and Urbanization on Compound Flood Risks in Coastal Areas: A Comprehensive Review of Methods

X Ruan, H Sun, W Shou, J Wang - Applied Sciences, 2024 - mdpi.com
Many cities worldwide are increasingly threatened by compound floods resulting from the
interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas …

Monitoring the Industrial waste polluted stream-Integrated analytics and machine learning for water quality index assessment

U Ejaz, SM Khan, S Jehangir, Z Ahmad… - Journal of Cleaner …, 2024 - Elsevier
Abstract The Water Quality Index (WQI) is a primary metric used to evaluate and categorize
surface water quality which plays a crucial role in the management of fresh water resources …

[HTML][HTML] Beach nourishment for coastal aquifers impacted by climate change and population growth using machine learning approaches

NL Kushwaha, K Sushanth, A Patel, O Kisi… - Journal of …, 2024 - Elsevier
Groundwater in coastal regions is threatened by saltwater intrusion (SWI). Beach
nourishment is used in this study to manage SWI in the Biscayne aquifer, Florida, USA …

[HTML][HTML] Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

Y Xu, P Li, M Zhang, L Xiao, B Wang, X Zhang… - Ecological …, 2024 - Elsevier
As the pace of industrialization and urbanization accelerates, water quality management
faces increasing challenges, with traditional methods for pollutant source apportionment …

[HTML][HTML] Groundwater level predictions in the Thames Basin, London over extended horizons using Transformers and advanced machine learning models

AJ Ali, AA Ahmed, MF Abbod - Journal of Cleaner Production, 2024 - Elsevier
This study breaks new ground by using the Temporal Fusion Transformer (TFT) method for
groundwater level prediction, addressing the complex dynamics of the Thames Basin …

[HTML][HTML] Long-term AI prediction of ammonium levels in rivers using transformer and ensemble models

AJ Ali, AA Ahmed - Cleaner Water, 2024 - Elsevier
This study provides a cutting-edge machine learning approach to forecast ammonium (NH
4+) levels in River Lee London. Ammonium concentrations were predicted over several time …

Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

S Toumi, S Lekmine, N Touzout, H Moussa… - Water, 2024 - hal.science
This study presents an innovative approach utilizing artificial intelligence (AI) for the
prediction and classification of water quality parameters based on physico-chemical …

Application of deep learning models with spectral data augmentation and Denoising for predicting total phosphorus concentration in water pollution

C Wang, W Xiong, G Zhang - Journal of the Taiwan Institute of Chemical …, 2025 - Elsevier
Background With the increasing severity of global water pollution, accurate prediction
models of water pollution content are critical for effective environmental management …

[HTML][HTML] Runoff simulation in data-scarce alpine regions: Comparative analysis based on LSTM and physically based models

J Yue, L Zhou, J Du, C Zhou, S Nimai, L Wu, T Ao - Water, 2024 - mdpi.com
Runoff simulation is essential for effective water resource management and plays a pivotal
role in hydrological forecasting. Improving the quality of runoff simulation and forecasting …