How Bayesian networks are applied in the subfields of climate change: Hotspots and evolution trends
H Shi, X Li, S Wang - Environmental Modelling & Software, 2024 - Elsevier
The ability of Bayesian networks (BNs) to model complex systems and uncertainties makes it
a perfect tool for the research on subfields related to climate change. In fact, in the past 30 …
a perfect tool for the research on subfields related to climate change. In fact, in the past 30 …
Forecasting of wastewater treatment plant key features using deep learning-based models: A case study
The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend
and predict the plant behavior to support process design and controls, improve system …
and predict the plant behavior to support process design and controls, improve system …
A data-driven approach to improve customer churn prediction based on telecom customer segmentation
Numerous valuable clients can be lost to competitors in the telecommunication industry,
leading to profit loss. Thus, understanding the reasons for client churn is vital for …
leading to profit loss. Thus, understanding the reasons for client churn is vital for …
Spatial map** of the provenance of storm dust: Application of data mining and ensemble modelling
Spatial modelling of storm dust provenance is essential to mitigate its on-site and off-site
effects in the arid and semi-arid environments of the world. Therefore, the main aim of this …
effects in the arid and semi-arid environments of the world. Therefore, the main aim of this …
[HTML][HTML] Model choice for quantitative health impact assessment and modelling: an expert consultation and narrative literature review
Background: Health impact assessment (HIA) is a widely used process that aims to identify
the health impacts, positive or negative, of a policy or intervention that is not necessarily …
the health impacts, positive or negative, of a policy or intervention that is not necessarily …
Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran
In this study, we apply six machine-learning algorithms (XGBoost, Cubist, BMARS, ANFIS,
Cforest and Elasticnet) to investigate the susceptibility of the Jazmurian Basin in …
Cforest and Elasticnet) to investigate the susceptibility of the Jazmurian Basin in …
Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory
This study undertook a comprehensive application of 15 data mining (DM) models, most of
which have, thus far, not been commonly used in environmental sciences, to predict land …
which have, thus far, not been commonly used in environmental sciences, to predict land …
Assessment of the uncertainty and interpretability of deep learning models for map** soil salinity using DeepQuantreg and game theory
This research introduces a new combined modelling approach for map** soil salinity in
the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence …
the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence …
Deep learning approach for sustainable WWTP operation: A case study on data-driven influent conditions monitoring
Wastewater treatment plants (WWTPs) are sustainable solutions to water scarcity. As initial
conditions offered to WWTPs, influent conditions (ICs) affect treatment units states, ongoing …
conditions offered to WWTPs, influent conditions (ICs) affect treatment units states, ongoing …
Data analytics determines co-occurrence of odorants in raw water and evaluates drinking water treatment removal strategies
C Wang, DL Gallagher, AM Dietrich, M Su… - Environmental …, 2021 - ACS Publications
A complex dataset with 140 sampling events was generated using triple quadrupole gas
chromatography-mass spectrometer to track the occurrence of 95 odorants in raw and …
chromatography-mass spectrometer to track the occurrence of 95 odorants in raw and …