Enhancing wastewater treatment through artificial intelligence: A comprehensive study on nutrient removal and effluent quality prediction

[1]  D. Avisar,et al.  Analyzing the secondary wastewater-treatment process using Faster R-CNN and YOLOv5 object detection algorithms , 2023, Journal of Cleaner Production.

[2]  Xueqing Shi,et al.  A novel long short-term memory artificial neural network (LSTM)-based soft-sensor to monitor and forecast wastewater treatment performance , 2023, Journal of Water Process Engineering.

[3]  H. Mamane,et al.  Nowcasting of fecal coliform presence using an artificial neural network. , 2023, Environmental pollution.

[4]  P. Show,et al.  Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems. , 2023, Bioresource technology.

[5]  Jianpeng Zhou,et al.  Artificial Intelligence-Assisted Prediction of Effluent Phosphorus in a Full-Scale Wastewater Treatment Plant with Missing Phosphorus Input and Removal Data , 2023, ACS ES&T Water.

[6]  P. G. Asteris,et al.  Machine Learning Approach for Rapid Estimation of Five-Day Biochemical Oxygen Demand in Wastewater , 2022, Water.

[7]  Majid Bahramian,et al.  Data to intelligence: The role of data-driven models in wastewater treatment , 2022, Expert Syst. Appl..

[8]  Zhiping Huang,et al.  Research Progress on Integrated Treatment Technologies of Rural Domestic Sewage: A Review , 2022, Water.

[9]  Heba M. Ismail,et al.  Water Quality Classification Using Machine Learning Algorithms , 2022, 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA).

[10]  Keugtae Kim,et al.  Deep Learning-Based Algal Detection Model Development Considering Field Application , 2022, Water.

[11]  Quang Viet Ly,et al.  Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants. , 2022, The Science of the total environment.

[12]  Mikkel Stokholm-Bjerregaard,et al.  Modeling phosphorous dynamics in a wastewater treatment process using Bayesian optimized LSTM , 2022, Comput. Chem. Eng..

[13]  S. Waheed,et al.  Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique , 2022, Cybersecurity.

[14]  L. Venkataramana,et al.  Analysis and prediction of water quality using deep learning and auto deep learning techniques. , 2022, The Science of the total environment.

[15]  M. El-Rawy,et al.  Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques , 2021, Journal of Water Process Engineering.

[16]  Yipei Li,et al.  Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling , 2021, Process Safety and Environmental Protection.

[17]  Yangwu Chen,et al.  Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration. , 2021, Journal of environmental management.

[18]  Qihao Weng,et al.  Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation. , 2021, Environmental research.

[19]  Mohamed Khaldi,et al.  Enhancing the prediction of student performance based on the machine learning XGBoost algorithm , 2021, Interact. Learn. Environ..

[20]  J. Trygg,et al.  A machine learning framework to improve effluent quality control in wastewater treatment plants. , 2021, The Science of the total environment.

[21]  E. R. Rene,et al.  Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives , 2021, Clean Technologies and Environmental Policy.

[22]  Mi Young Lee,et al.  AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes , 2020, The Journal of Supercomputing.

[23]  M. Yaqub,et al.  Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network , 2020 .

[24]  B. A,et al.  Evaluating the effect of seasonal temperature changes on the efficiency of a rhizofiltration system in nitrogen removal from urban runoff. , 2020, Journal of environmental management.

[25]  Rung-Ching Chen,et al.  Selecting critical features for data classification based on machine learning methods , 2020, Journal of Big Data.

[26]  Farid García,et al.  A comprehensive survey on support vector machine classification: Applications, challenges and trends , 2020, Neurocomputing.

[27]  Chi Zhang,et al.  Spatial characteristics of total phosphorus loads from different sources in the Lancang River Basin. , 2020, The Science of the total environment.

[28]  J. Adamowski,et al.  Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model , 2020, Stochastic Environmental Research and Risk Assessment.

[29]  Yaqing Liu,et al.  Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient , 2020, Neural Processing Letters.

[30]  Min Zuo,et al.  Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. , 2019, Water research.

[31]  A. Callegari,et al.  Energy Recovery from Wastewater: A Study on Heating and Cooling of a Multipurpose Building with Sewage-Reclaimed Heat Energy , 2019, Sustainability.

[32]  Jizhong Zhou,et al.  Biodegradability of wastewater determines microbial assembly mechanisms in full-scale wastewater treatment plants. , 2019, Water research.

[33]  C. K. Singh,et al.  Predicting groundwater arsenic contamination: Regions at risk in highest populated state of India. , 2019, Water research.

[34]  Yoshua Bengio,et al.  Tackling Climate Change with Machine Learning , 2019, ACM Comput. Surv..

[35]  K. Gernaey,et al.  Plant-wide model-based analysis of iron dosage strategies for chemical phosphorus removal in wastewater treatment systems. , 2019, Water research.

[36]  Eric Lichtfouse,et al.  Advantages and disadvantages of techniques used for wastewater treatment , 2018, Environmental Chemistry Letters.

[37]  F. Tsai,et al.  Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model , 2018 .

[38]  P. Barak,et al.  Cost effectiveness of phosphorus removal processes in municipal wastewater treatment. , 2018, Chemosphere.

[39]  Lluís Corominas,et al.  Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques , 2017, Environ. Model. Softw..

[40]  Wenjian Wang,et al.  Error estimation based on variance analysis of k-fold cross-validation , 2017, Pattern Recognit..

[41]  B. Lee,et al.  Characteristics and Biodegradability of Wastewater Organic Matter in Municipal Wastewater Treatment Plants Collecting Domestic Wastewater and Industrial Discharge , 2017 .

[42]  Zhang-bin Niu,et al.  Characteristics of water quality of municipal wastewater treatment plants in China: implications for resources utilization and management , 2016 .

[43]  Sukalyan Sengupta,et al.  Nitrogen and Phosphorus Recovery from Wastewater , 2015, Current Pollution Reports.

[44]  A. Kazmi,et al.  The effect of seasonal temperature on pathogen removal efficacy of vermifilter for wastewater treatment. , 2015, Water research.

[45]  Hongtao Wang,et al.  Chemically enhanced primary treatment (CEPT) for removal of carbon and nutrients from municipal wastewater treatment plants: a case study of Shanghai. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[46]  Stan Szpakowicz,et al.  Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation , 2006, Australian Conference on Artificial Intelligence.

[47]  James L. Barnard,et al.  Nutrient Removal Technology in North America and the European Union: A Review , 2006 .

[48]  Tong Zhang,et al.  Microbial community analysis and performance of a phosphate-removing activated sludge. , 2005, Bioresource technology.

[49]  T. Ferryman,et al.  Data outlier detection using the Chebyshev theorem , 2005, 2005 IEEE Aerospace Conference.

[50]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[51]  Farid Kadri,et al.  Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study , 2020, IEEE Access.