On the public citation graph

A Comprehensive Assessment of Extreme Gradient Boost (Xgb) and Support Vector Regression (Svr) Performance to Predict Effluent Quality in Activated Sludge Treatment Process

https://doi.org/10.2139/ssrn.4669333
0 signed · 47 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4842-2866-1openalex-extracted
Mastering Machine Learning with Python in Six Steps
inferredcites →https://doi.org/10.1007/978-1-4842-4947-5openalex-extracted
Mastering Machine Learning with Python in Six Steps
inferredcites →https://doi.org/10.1007/bf00252030openalex-extracted
Isolation and characterization of filamentous bacteria present in bulking activated sludge
inferredcites →https://doi.org/10.1007/s00253-015-6393-8openalex-extracted
Study of 16 Portuguese activated sludge systems based on filamentous bacteria populations and their relationships with environmental parameters
inferredcites →https://doi.org/10.1007/s11270-021-05049-8openalex-extracted
Prediction of Polycyclic Aromatic Hydrocarbons (PAHs) Removal from Wastewater Treatment Sludge Using Machine Learning Methods
inferredcites →https://doi.org/10.1007/s12182-014-0010-9openalex-extracted
Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel
inferredcites →https://doi.org/10.1007/s12205-022-1870-yopenalex-extracted
Investigation of Energy Dissipation Rate of Stepped Vertical Overfall (SVO) Spillway Using Physical Modeling and Soft Computing Techniques
inferredcites →https://doi.org/10.1007/s13201-017-0526-4openalex-extracted
Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater
inferredcites →https://doi.org/10.1007/s41742-020-00274-1openalex-extracted
Estimation of effluent parameters of slaughterhouse wastewater treatment with artificial neural network and B-spline quasi interpolation
inferredcites →https://doi.org/10.1016/j.asoc.2021.107836openalex-extracted
Deep learning with small datasets: using autoencoders to address limited datasets in construction management
inferredcites →https://doi.org/10.1016/j.cej.2004.06.010openalex-extracted
Dynamic modeling of biogas production in an UASB reactor for potato processing wastewater treatment
inferredcites →https://doi.org/10.1016/j.compchemeng.2022.107738openalex-extracted
Modeling phosphorous dynamics in a wastewater treatment process using Bayesian optimized LSTM
inferredcites →https://doi.org/10.1016/j.ecoinf.2018.01.005openalex-extracted
Determining quality of water in reservoir using machine learning
inferredcites →https://doi.org/10.1016/j.engappai.2012.08.015openalex-extracted
Predicting the total suspended solids in wastewater: A data-mining approach
inferredcites →https://doi.org/10.1016/j.eswa.2008.06.006openalex-extracted
Simulation of a paper mill wastewater treatment using a fuzzy neural network
inferredcites →https://doi.org/10.1016/j.jclepro.2018.01.139openalex-extracted
Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model
inferredcites →https://doi.org/10.1016/j.jclepro.2020.125772openalex-extracted
Artificial intelligence based ensemble modeling of wastewater treatment plant using jittered data
inferredcites →https://doi.org/10.1016/j.jenvman.2016.07.047openalex-extracted
Wastewater treatment aeration process optimization: A data mining approach
inferredcites →https://doi.org/10.1016/j.jenvman.2016.11.023openalex-extracted
Descriptive and multivariate analyses of four Tunisian wastewater treatment plants: A comparison between different treatment processes and their efficiency improvement
inferredcites →https://doi.org/10.1016/j.jenvman.2018.06.092openalex-extracted
Machine learning for energy cost modelling in wastewater treatment plants
inferredcites →https://doi.org/10.1016/j.jenvman.2019.03.137openalex-extracted
Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology
inferredcites →https://doi.org/10.1016/j.jenvman.2021.113795openalex-extracted
Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant
inferredcites →https://doi.org/10.1016/j.jes.2015.01.007openalex-extracted
Prediction of effluent concentration in a wastewater treatment plant using machine learning models
inferredcites →https://doi.org/10.1016/j.jhydrol.2011.02.021openalex-extracted
Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction
inferredcites →https://doi.org/10.1016/j.jhydrol.2011.08.022openalex-extracted
Variations in AOC and microbial diversity in an advanced water treatment plant
inferredcites →https://doi.org/10.1016/j.jhydrol.2016.02.057openalex-extracted
Integrative neural networks models for stream assessment in restoration projects
inferredcites →https://doi.org/10.1016/j.jhydrol.2016.08.058openalex-extracted
Highway runoff quality models for the protection of environmentally sensitive areas
inferredcites →https://doi.org/10.1016/j.jhydrol.2016.11.033openalex-extracted
An Emotional ANN (EANN) approach to modeling rainfall-runoff process
inferredcites →https://doi.org/10.1016/j.jhydrol.2018.12.037openalex-extracted
A review of the artificial intelligence methods in groundwater level modeling
inferredcites →https://doi.org/10.1016/j.jwpe.2020.101388openalex-extracted
Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network
inferredcites →https://doi.org/10.1016/j.jwpe.2020.101389openalex-extracted
Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control
inferredcites →https://doi.org/10.1016/j.jwpe.2020.101477openalex-extracted
Artificial neural network modelling of faecal coliform removal in an intermittent cycle extended aeration system-sequential batch reactor based wastewater treatment plant
inferredcites →https://doi.org/10.1016/j.jwpe.2021.102033openalex-extracted
Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance
inferredcites →https://doi.org/10.1016/j.jwpe.2021.102380openalex-extracted
Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques
inferredcites →https://doi.org/10.1016/j.procbio.2020.06.020openalex-extracted
Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine
inferredcites →https://doi.org/10.1016/j.psep.2019.02.023openalex-extracted
Decision tree for identification and prediction of filamentous bulking at full-scale activated sludge wastewater treatment plant
inferredcites →https://doi.org/10.1016/j.psep.2019.11.014openalex-extracted
Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse
inferredcites →https://doi.org/10.1016/j.psep.2020.04.045openalex-extracted
The potential of new ensemble machine learning models for effluent quality parameters prediction and related uncertainty
inferredcites →https://doi.org/10.1016/j.scitotenv.2017.08.081openalex-extracted
Enhanced roadside drainage system for environmentally sensitive areas
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.147138openalex-extracted
A machine learning framework to improve effluent quality control in wastewater treatment plants
inferredcites →https://doi.org/10.1016/j.watres.2013.08.024openalex-extracted
EU-wide monitoring survey on emerging polar organic contaminants in wastewater treatment plant effluents
inferredcites →https://doi.org/10.1038/s41545-021-00106-5openalex-extracted
Activated sludge models at the crossroad of artificial intelligence—A perspective on advancing process modeling
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-extracted
XGBoost
inferredcites →https://doi.org/10.2139/ssrn.3416918openalex-extracted
Usefulness of Correlation Analysis
inferredcites →https://doi.org/10.2166/wst.2021.067openalex-extracted
Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study)
inferredcites →https://doi.org/10.3390/math10142466openalex-extracted
Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey
inferredcites →https://doi.org/10.3390/su11061764openalex-extracted
Influent Forecasting for Wastewater Treatment Plants in North America
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.