Fares Laouacheria

Despite the high importance of coagulation process in drinking water treatment plant (DWTP), challenge remains in effectively linking raw water quality measured at the inlet of the DWTP with coagulant dosage rate. This study proposes an integral modelling framework using hybrid extreme learning machine and Bat metaheuristic algorithm (ELM-Bat) for modelling coagulant dosage rate using water temperature, pH, specific conductance, dissolved oxygen, and water turbidity. The aluminum sulphate (Al₂ (SO₄)₃.18H₂O) coagulant is determined using conventional Jar-Test procedure. Results obtained using the hybrid ELM-Bat were compared to those obtained using standalone ELM, outlier robust extreme learning machine (ORELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), and kernel extreme learning machine (KELM). First, the models have been calibrated during the training stage and in a second stage; they are validated using various statistical metrics, i.e., RMSE, MAE, the correlation coefficient (R), and the Nash–Sutcliffe model efficiency (NSE). We found that the hybrid ELM-Bat was significantly more accurate and it has yielded accuracy higher than all other models. During the validation stage, the R and NSE values calculated using the ELM-Bat were ≈0.959 and ≈0.918 exhibiting an improvement rates of approximately (≈15.26% and ≈33.82%), (≈10.35% and ≈21.92%), (≈14.98% and ≈31.89%), (≈7.63% and ≈16.35%), (≈10.99% and ≈23.05%), compared to the values obtained using the ELM, OPELM, OSELM, KELM and ORELM, respectively. Besides, the new ELM-Bat model has shown to have high predictive capabilities, which can be used optimally for calculating the optimal coagulant dosage with high accuracy. Graphical abstract

In the present study, three machine learning methods were applied for predicting seepage flow through embankment dams, namely (i) support vector regression (SVR), relevance vector machine (RVM), and Gaussian process regression (GPR). The three models were developed using seepage flow (Q: L/mn) and piezometer level (Z:m) measured at several piezometers placed in the corps body of the dam. The proposed models were calibrated and validated using a separate subset. Models evaluation and comparison was successfully achieved using various performances metrics, i.e., coefficient of correlation (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE). Experimental results showed that the proposed models are a good alternative to the in situ measured and contributed significantly in overcoming the case of missing measured seepage flow. The best performances were obtained using the RVM model with R and NSE values of ≈0.909 and ≈0.823, followed by the GPR model with R and NSE values of ≈0.891 and ≈0.767, while the SVR model was ranked as the poorest one exhibiting R and NSE values of ≈0.780 and ≈0.600, respectively. While, a growing number of investigations have focused on testing machine learning in terms of their feasibilities to accurately describe seepage flow, as well as providing important support to our understanding of the factors affecting its fluctuation, the present work was demonstrated that the combination of a wide range of variables can help in simulating seepage flow, and enhance their sensitivity which has help in developing new algorithms. Graphical abstract

Sponsored by the Environmental & Water Resources Institute (EWRI)

Flood modelling is an effective way to manage the stormwater network in cities. It aims to understand and predict the behaviour of stormwater network so that it can test and evaluate effective solutions to structural and operational problems. So simulation modelling stays a preoccupation for building a successful hydraulic modelling in urban areas. This study investigates the impact of the design rainfall on the hydraulic modelling results for the Azzaba stormwater network located in the North-East of Algeria by using the Storm Water Management Model (SWMM). Four scenarios of design rainfall events were compared for 10, 25 and 50-year return periods, where we used double triangle and composite curves for the design rainfall event definition. The results show the impact of the choice of design rainfall on the behaviour of the stormwater network, from which the results of simulation by the double triangle method for the short durations represents a great risk on the probability that the stromwater network can overflow and flood the city, with a difference in peak discharge estimated at 62.97% and 58.94% for 2 h and 3 h events compared to the peak discharge simulated by the composite rainfall method.