Reference health
Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study
20/20 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
8 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 20 checked references that resolve
resolves10.1016/j.envsoft.2004.02.003
Integrating simulation models with a view to optimal control of urban wastewater systems
resolves10.1016/0043-1354(91)90060-4
Sludge bulking analysis and forecasting: Application of system identification and artificial neural computing technologies
resolves10.1016/0043-1354(95)93250-W
Dynamic modelling of the activated sludge process: Improving prediction using neural networks
resolves10.1080/09593332308618384
Modeling a Full-Scale Primary Sedimentation Tank Using Artificial Neural Networks
resolves10.2166/wst.2006.127
WWTP dynamic disturbance modelling–an essential module for long-term benchmarking development
resolves10.2166/wst.2006.031
Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs
resolves10.1016/S0043-1354(00)00105-6
Calibration and validation of activated sludge model no. 3 for Swiss municipal wastewater
resolves10.2166/wst.2002.0066
Real time control of the integrated urban wastewater system using simultaneously simulating surrogate models
resolves10.1007/s10098-002-0153-z
Computer aided model analysis and dynamic simulation of a wastewater treatment plant
resolves10.2166/wst.1992.0582
Dynamic Models for Clarifiers of Activated Sludge Plants with Dry and Wet Weather Flows
resolves10.2166/hydro.2002.0003
Evaluation of an ASM1 model calibration procedure on a municipal–industrial wastewater treatment plant
resolves10.1016/j.envsoft.2004.02.004
Modelling and real-time control of the integrated urban wastewater system
resolves10.1016/S0273-1223(98)00788-4
Approaches to modeling nutrient dynamics: ASM2, simplified model and neural nets
The 8 references without a DOI — listed, not checked
no DOI — not checked10.1016/j.envsoft.2006.07.003_bib3
no DOI — not checkedGernaey, K.V., Rosen, C., Benedetti, L., Jeppsson, U., 2005. Phenomenological modeling of wastewater treatment plant influent disturbance scenarios. Proc. 10th International Conference on Urban Drainage, August 21-26, Copenhagen (Denmark).
no DOI — not checked10.1016/j.envsoft.2006.07.003_bib11
no DOI — not checked10.1016/j.envsoft.2006.07.003_bib12
no DOI — not checkedLoke, E., 1999. Artificial neural networks in urban storm drainage. PhD thesis, Dept. of Environmental Science and Engineering, Technical University of Denmark, Lyngby, Denmark.
no DOI — not checkedOtterpohl, R., Raak, M., Rolfs, T., 1994. A mathematical model for the efficiency of the primary clarification. Proc. IAWQ 17th Biennial International Conference, July 24–29, Budapest, Hungary.
no DOI — not checkedPrediction of a medium-sized municipal wastewater treatment plant performance with artificial neural networks
no DOI — not checkedRáduly, B., 2005. Artificial neural network applications in the urban water quality modeling. PhD thesis, Department of Hydraulic and Environ. Engineering, University of Pavia, Pavia, Italy.
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