A survey on river water quality modelling using artificial intelligence models: 2000–2020
There has been an unsettling rise in the river contamination due to the climate change and
anthropogenic activities. Last decades' research has immensely focussed on river basin …
anthropogenic activities. Last decades' research has immensely focussed on river basin …
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 …
The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Precise streamflow prediction is necessary for better planning and managing available
water and future water resources, especially for high altitude mountainous glacier melting …
water and future water resources, especially for high altitude mountainous glacier melting …
An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research …
Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …
applications, there remains a need to develop more reliable and intelligent expert systems …
Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
Monthly streamflow forecasting is required for short-and long-term water resources
management especially in extreme events such as flood and drought. Therefore, there is …
management especially in extreme events such as flood and drought. Therefore, there is …
Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach
In the present study, three different artificial intelligence based non-linear models, ie feed
forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support …
forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support …
Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries
Discussions about the recently identified deadly coronavirus disease (COVID-19) which
originated in Wuhan, China in December 2019 are common around the globe now. This is …
originated in Wuhan, China in December 2019 are common around the globe now. This is …
A comparative study of data-driven models for runoff, sediment, and nitrate forecasting
Effective prediction of qualitative and quantitative indicators for runoff is quite essential in
water resources planning and management. However, although several data-driven and …
water resources planning and management. However, although several data-driven and …
Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran
Abstract The accuracy of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference
System (ANFIS), wavelet-ANN and wavelet-ANFIS in predicting monthly water salinity levels …
System (ANFIS), wavelet-ANN and wavelet-ANFIS in predicting monthly water salinity levels …
[PDF][PDF] Estimation of water quality index using artificial intelligence approaches and multi-linear regression
Water quality index is a measure of water quality at a certain location and over a period of
time. High value indicates that the water is unsafe for drinking and inadequate in quality to …
time. High value indicates that the water is unsafe for drinking and inadequate in quality to …