Machine learning in environmental research: common pitfalls and best practices

JJ Zhu, M Yang, ZJ Ren - Environmental Science & Technology, 2023 - ACS Publications
Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …

Machine learning in natural and engineered water systems

R Huang, C Ma, J Ma, X Huangfu, Q He - Water Research, 2021 - Elsevier
Water resources of desired quality and quantity are the foundation for human survival and
sustainable development. To better protect the water environment and conserve water …

Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation

F Wang, Y Wang, K Zhang, M Hu, Q Weng… - Environmental …, 2021 - Elsevier
A systematic understanding of the spatial distribution of water quality is critical for successful
watershed management; however, the limited number of physical monitoring stations has …

Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques

Y Zhou - Journal of Hydrology, 2020 - Elsevier
Quantifying the uncertainty of probabilistic water quality forecasting induced by missing input
data is fundamentally challenging. This study introduced a novel methodology for …

Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

W Wu, GC Dandy, HR Maier - Environmental Modelling & Software, 2014 - Elsevier
Abstract The application of Artificial Neural Networks (ANNs) in the field of environmental
and water resources modelling has become increasingly popular since early 1990s. Despite …

An IoT based smart water quality monitoring system using cloud

JB Ajith, R Manimegalai… - … conference on emerging …, 2020 - ieeexplore.ieee.org
The Internet of Things (IoT) is the network of physical devices, vehicles, home appliances,
and other items embedded with electronics, software, sensors, actuators and connectivity …

Hybrid modelling of water resource recovery facilities: status and opportunities

MY Schneider, W Quaghebeur, S Borzooei… - Water Science and …, 2022 - iwaponline.com
Mathematical modelling is an indispensable tool to support water resource recovery facility
(WRRF) operators and engineers with the ambition of creating a truly circular economy and …

The fourth-revolution in the water sector encounters the digital revolution

M Garrido-Baserba, L Corominas… - … science & technology, 2020 - ACS Publications
The so-called fourth revolution in the water sector will encounter the Big data and Artificial
Intelligence (AI) revolution. The current data surplus stemming from all types of devices …

[HTML][HTML] Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring

Z Li, H Liu, C Zhang, G Fu - Environmental Science and Ecotechnology, 2023 - Elsevier
Contamination events in water distribution networks (WDNs) can have a huge impact on
water supply and public health; increasingly, online water quality sensors are deployed for …

A survey of machine learning methods applied to anomaly detection on drinking-water quality data

EM Dogo, NI Nwulu, B Twala, C Aigbavboa - Urban Water Journal, 2019 - Taylor & Francis
Traditional machine learning (ML) techniques such as support vector machine, logistic
regression, and artificial neural network have been applied most frequently in water quality …