A review of the artificial neural network models for water quality prediction

Y Chen, L Song, Y Liu, L Yang, D Li - Applied Sciences, 2020 - mdpi.com
Water quality prediction plays an important role in environmental monitoring, ecosystem
sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear …

Data to intelligence: The role of data-driven models in wastewater treatment

M Bahramian, RK Dereli, W Zhao, M Giberti… - Expert Systems with …, 2023 - Elsevier
Increasing energy efficiency in wastewater treatment plants (WWTPs) is becoming more
important. An emerging approach to addressing this issue is to exploit development in data …

[HTML][HTML] A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction

L Chen, T Wu, Z Wang, X Lin, Y Cai - Ecological Indicators, 2023 - Elsevier
With the accelerated industrialization and urbanization process, water pollution in rivers is
being increasingly worsened, and has caused a series of ecological and environmental …

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

SBHS Asadollah, A Sharafati, D Motta… - Journal of environmental …, 2021 - Elsevier
Abstract The Water Quality Index (WQI) is the most common indicator to characterize surface
water quality. This study introduces a new ensemble machine learning model called Extra …

Assessment of stream quality and health risk in a subtropical Turkey river system: A combined approach using statistical analysis and water quality index

F Ustaoğlu, Y Tepe, B Taş - Ecological indicators, 2020 - Elsevier
The effect of agricultural activities and domestic pollution on water quality in the Turnasuyu
Basin was evaluated. Sampling was performed during the period of a hydrological year …

Potential toxic elements in sediment of some rivers at Giresun, Northeast Turkey: A preliminary assessment for ecotoxicological status and health risk

F Ustaoğlu, MS Islam - Ecological indicators, 2020 - Elsevier
The concentration of globally alarming potential toxic elements (PTEs) like Aluminum (Al),
chrome (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn) …

Advances in machine learning modeling reviewing hybrid and ensemble methods

S Ardabili, A Mosavi, AR Várkonyi-Kóczy - International conference on …, 2019 - Springer
The conventional machine learning (ML) algorithms are continuously advancing and
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …

Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms

JC Egbueri, JC Agbasi - Environmental Science and Pollution Research, 2022 - Springer
In recent decades, the simulation and modeling of water quality parameters have been
useful for monitoring and assessment of the quality of water resources. Moreover, the use of …

Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, **njiang, China

Y **g, F Zhang, Y He, VC Johnson, M Arikena - Ecological Indicators, 2020 - Elsevier
Remote sensing technology can objectively and quantitatively evaluate spatial-temporal
change of ecological environmental quality. This paper uses images from Landsat5 …

Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system

M Dehghani, H Riahi-Madvar, F Hooshyaripor… - Energies, 2019 - mdpi.com
Hydropower is among the cleanest sources of energy. However, the rate of hydropower
generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey …