A review of the artificial neural network models for water quality prediction
Water quality prediction plays an important role in environmental monitoring, ecosystem
sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear …
sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear …
Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects
Neurocomputing methods have contributed significantly to the advancement of modelling
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
River suspended sediment modelling using the CART model: A comparative study of machine learning techniques
Suspended sediment load (SSL) modelling is an important issue in integrated
environmental and water resources management, as sediment affects water quality and …
environmental and water resources management, as sediment affects water quality and …
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference
Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a …
Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a …
Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling
Y Yang, KR Kim, R Kou, Y Li, J Fu, L Zhao… - Process Safety and …, 2022 - Elsevier
Improving the operation, management, and consequent performance of wastewater
treatment plants (WWTPs) for conserving the water environment is crucial. Recent …
treatment plants (WWTPs) for conserving the water environment is crucial. Recent …
Forecasting groundwater levels using a hybrid of support vector regression and particle swarm optimization
S Mozaffari, S Javadi, HK Moghaddam… - Water Resources …, 2022 - Springer
Forecasting the groundwater level is crucial to managing water resources supply
sustainably. In this study, a simulation–optimization hybrid model was developed to forecast …
sustainably. In this study, a simulation–optimization hybrid model was developed to forecast …
River suspended sediment load prediction based on river discharge information: application of newly developed data mining models
Suspended sediment load (SSL) is one of the essential hydrological processes that affects
river engineering sustainability. Sediment has a major influence on the operation of dams …
river engineering sustainability. Sediment has a major influence on the operation of dams …
A novel hybrid algorithms for groundwater level prediction
Estimating groundwater levels (GWL) with accuracy and reliability, in order to maximize the
use of water resources, it is crucial to reduce water consumption. To predict GWL in the …
use of water resources, it is crucial to reduce water consumption. To predict GWL in the …
Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression
Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of
vital importance. In this study, support vector regression (SVR), Gaussian process …
vital importance. In this study, support vector regression (SVR), Gaussian process …
Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis
A Sharafati, SB Haji Seyed Asadollah… - Hydrological …, 2020 - Taylor & Francis
Ensemble machine learning models have been widely used in hydro-systems modeling as
robust prediction tools that combine multiple decision trees. In this study, three newly …
robust prediction tools that combine multiple decision trees. In this study, three newly …