Cargando…

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators o...

Descripción completa

Detalles Bibliográficos
Autor principal:	Zare Abyaneh, Hamid
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2014
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906747/ https://www.ncbi.nlm.nih.gov/pubmed/24456676 http://dx.doi.org/10.1186/2052-336X-12-40

Ejemplares similares

Multivariable Logistic Regression And Back Propagation Artificial Neural Network To Predict Diabetic Retinopathy
por: Yao, Litong, et al.
Publicado: (2019)

Nucleophilicity Prediction via Multivariate Linear Regression Analysis
por: Orlandi, Manuel, et al.
Publicado: (2021)

Comparison of artificial neural networks and multiple linear regression for prediction of dairy cow locomotion score
por: Norouzian, Mohammad Ali, et al.
Publicado: (2021)

Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques
por: Kittichotsatsawat, Yotsaphat, et al.
Publicado: (2022)

Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model
por: Eze, Elias, et al.
Publicado: (2023)

Predictive and mechanistic multivariate linear regression models for reaction development
por: Santiago, Celine B., et al.
Publicado: (2018)

Prediction of Electrical Resistance with Conductive Sewing Patterns by Combining Artificial Neural Networks and Multiple Linear Regressions
por: Jang, JunHyeok, et al.
Publicado: (2023)

Predictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks
por: Fernández, Angel, et al.
Publicado: (2023)

Forecasting water quality parameters using artificial neural network for irrigation purposes
por: Ubah, J. I., et al.
Publicado: (2021)

Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)
por: Kothakota, Anjineyulu, et al.
Publicado: (2021)

Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions
por: Radfard, Majid, et al.
Publicado: (2018)

Predicting relative efficiency of amide bond formation using multivariate linear regression
por: Haas, Brittany C., et al.
Publicado: (2022)

Short period PM(2.5) prediction based on multivariate linear regression model
por: Zhao, Rui, et al.
Publicado: (2018)

Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines
por: Reid, Jolene P., et al.
Publicado: (2019)

Prediction of compressibility parameters of the soils using artificial neural network
por: Kurnaz, T. Fikret, et al.
Publicado: (2016)

Prediction and assessment of drought effects on surface water quality using artificial neural networks: case study of Zayandehrud River, Iran
por: Safavi, Hamid R., et al.
Publicado: (2015)

Use of Artificial Neural Network Model for Rice Quality Prediction Based on Grain Physical Parameters
por: Sampaio, Pedro Sousa, et al.
Publicado: (2021)

Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size
por: Barmpalexis, Panagiotis, et al.
Publicado: (2020)

The evaluation of potentially toxic elements using artificial neural networks and fuzzy linear regression analysis methods in cappadocian volcanic ash soils of Turkey
por: Kılıç, Kenan, et al.
Publicado: (2023)

Application of artificial neural networks and multiple linear regression on local bond stress equation of UHPC and reinforcing steel bars
por: Amini Pishro, Ahad, et al.
Publicado: (2021)

Principal Component Regression and Artificial Neural Network: The Prediction of Air Pollution Index (API)
por: Hua, Ang Kean, et al.
Publicado: (2021)

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models
por: Viswanathan, Pavitra, et al.
Publicado: (2022)

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values
por: Huang, Hairong, et al.
Publicado: (2017)

Multivariable logistic and linear regression models for identification of clinically useful biomarkers for osteoarthritis
por: Liem, Yulia, et al.
Publicado: (2020)

Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks
por: Antonacci, Yuri, et al.
Publicado: (2020)

Forecasting Single Disease Cost of Cataract Based on Multivariable Regression Analysis and Backpropagation Neural Network
por: Qing, Fang, et al.
Publicado: (2019)

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression
por: Meng, Haobin, et al.
Publicado: (2022)

Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network
por: Masoumi, Hamid Reza Fard, et al.
Publicado: (2011)

Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression
por: Tangri, Navdeep, et al.
Publicado: (2008)

A Comparison of Logistic Regression Model and Artificial Neural Networks in Predicting of Student’s Academic Failure
por: Teshnizi, Saeed Hosseini, et al.
Publicado: (2015)

Artificial neural networks versus bivariate logistic regression in prediction diagnosis of patients with hypertension and diabetes
por: Adavi, Mehdi, et al.
Publicado: (2016)

Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients
por: Arkin, Funda Secik, et al.
Publicado: (2020)

On-line parameter estimation for linear, open-loop, multivariable systems
por: Koreman, R J L, et al.
Publicado: (1975)

Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation
por: Tobiasz, Joanna, et al.
Publicado: (2023)

The Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy
por: Yang, Wan-Ju, et al.
Publicado: (2020)

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP
por: Kim, Younghwan, et al.
Publicado: (2021)

Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research
por: Wei, Wei, et al.
Publicado: (2021)

QSAR analysis of pyrimidine derivatives as VEGFR-2 receptor inhibitors to inhibit cancer using multiple linear regression and artificial neural network
por: Masoomi Sefiddashti, Fariba, et al.
Publicado: (2021)

Multivariate linear regression model based on cross-entropy for estimating disorganisation in drone formations
por: Gackowska, Marta, et al.
Publicado: (2023)

Predictors of Stroke Outcome Extracted from Multivariate Linear Discriminant Analysis or Neural Network Analysis
por: Nezu, Tomohisa, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_lr4q1c8g3gea0ke35alhjoqg2j