Gradient-based optimizer (gbo): a review, theory, variants, and applications
This paper introduces a comprehensive survey of a new population-based algorithm so-
called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as …
called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as …
A review of hybrid soft computing and data pre-processing techniques to forecast freshwater quality's parameters: Current trends and future directions
ZS Khudhair, SL Zubaidi, S Ortega-Martorell… - Environments, 2022 - mdpi.com
Water quality has a significant influence on human health. As a result, water quality
parameter modelling is one of the most challenging problems in the water sector. Therefore …
parameter modelling is one of the most challenging problems in the water sector. Therefore …
Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables
Hydrogen (H 2) absorption percentage by porous carbon media (PCM) is important for
identifying efficient H 2 storage media. PCM with H 2-uptakes of greater than 5 wt% are …
identifying efficient H 2 storage media. PCM with H 2-uptakes of greater than 5 wt% are …
A new methodology for reference evapotranspiration prediction and uncertainty analysis under climate change conditions based on machine learning, multi criteria …
M Kadkhodazadeh, M Valikhan Anaraki… - Sustainability, 2022 - mdpi.com
In the present study, a new methodology for reference evapotranspiration (ETo) prediction
and uncertainty analysis under climate change and COVID-19 post-pandemic recovery …
and uncertainty analysis under climate change and COVID-19 post-pandemic recovery …
Retracted: Enhancing waste management and prediction of water quality in the sustainable urban environment using optimized algorithm of least square support …
Following an investigation into a number of papers published as part of the Urban
Groundwater Special Issue in Urban Climate, it became evident that the names of two co …
Groundwater Special Issue in Urban Climate, it became evident that the names of two co …
Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning
Awareness of uniaxial compressive strength (UCS) as a key rock formation parameter for the
design and development of gas and oil field plays. It plays an essential role in the selection …
design and development of gas and oil field plays. It plays an essential role in the selection …
A comparative study of artificial intelligence models and a statistical method for groundwater level prediction
M Poursaeid, AH Poursaeid, S Shabanlou - Water Resources …, 2022 - Springer
Today, various methods have been developed to extract drinking water resources, which
scientists use to simulate the quantitative and qualitative water resources parameters. Due …
scientists use to simulate the quantitative and qualitative water resources parameters. Due …
A hybrid spatiotemporal deep model based on CNN and LSTM for air pollution prediction
S Tsokov, M Lazarova, A Aleksieva-Petrova - Sustainability, 2022 - mdpi.com
Nowadays, air pollution is an important problem with negative impacts on human health and
on the environment. The air pollution forecast can provide important information to all …
on the environment. The air pollution forecast can provide important information to all …
Enhancing real-time prediction of effluent water quality of wastewater treatment plant based on improved feedforward neural network coupled with optimization …
To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality,
a machine learning (ML) model was developed by combining an improved feedforward …
a machine learning (ML) model was developed by combining an improved feedforward …
Predicting rainfall response to climate change and uncertainty analysis: Introducing a novel downscaling CMIP6 models technique based on the stacking ensemble …
MV Anaraki, M Kadkhodazadeh… - Journal of Water and …, 2023 - iwaponline.com
This study proposes a novel downscaling technique based on stacking ensemble machine
learning (SEML) to predict rainfall under climate change. The SEML consists of two levels …
learning (SEML) to predict rainfall under climate change. The SEML consists of two levels …