A bilevel data-driven method for sewer deposit prediction under uncertainty

An innovative mixture sampling strategy with uniform design: Application to global sensitivity analysis of mixture toxicity

Global sensitivity analysis combined with quantitative high-throughput screening (GSA-qHTS) uses random starting points of the trajectories in mixture design, which may lead to potential contingency and a lack of representativeness. Moreover, a scenario in which all factor levels were at stimulatory effects was not considered, thereby hindering a comprehensive understanding of GSA-qHTS. Accordingly, this study innovatively introduced an optimised experimental design, uniform design (UD), to generate non-random and representative sample points with smaller uniformity deviation as starting points of multiple trajectories. By combining UD with the previously optimised one-factor-at-a-time (OAT) method, a novel mixture design method was developed (UD-OAT). The single toxicity tests showed that three pyridinium and five imidazolium ionic liquids (ILs) exerted stimulatory effects on Vibrio qinghaiensis sp.-Q67; thus, four stimulatory effective concentrations of each IL were selected as factor levels. The UD-OAT generated 108 mixture samples with equal frequency and without repetition. High-throughput microplate toxicity analysis revealed that all 108 mixtures exhibited inhibitory effects. Among these, type B mixtures exhibited increasing toxicities that subsequently decreased, unlike type C mixtures, which consistently increased over time. GSA successfully identified three of the eight ILs as important factors influencing the toxicities of the mixtures. When individual ILs produced stimulatory effects, mixtures containing two to three ILs exhibited either stimulatory effects or none. In contrast, mixtures containing five to eight ILs exhibited inhibitory effects, while those containing four ILs showed a transition from stimulatory to inhibitory effects. This study provides a novel mixture design method for studying mixture toxicity and fills the application gap of GSA-qHTS. The phenomenon of individuals being beneficial while mixtures can be harmful challenges traditional mixture risk assessments.

A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction

Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the optimal feature subset to predict best, and the GJO-CLT achieves the best performance for different backtracking windows and prediction steps. These results demonstrate that the prediction system excels in the task of multivariate water quality time series prediction of WWTPs.

Deep learning-assisted automated sewage pipe defect detection for urban water environment management

A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.