**4. Conclusions**

For coagulant dosage prediction, the MLP architecture (inputs, number of hidden layers, and number of neurons) has been fixed a priori. To define relevant descriptors of raw water quality affecting the coagulant dosage, a principal components analysis (PCA) is used within this framework. The number of neurons in the hidden layer has been optimized with a pruning method "weight-decay" [49, 50] in combination with the "Levenberg–Marquardt" algorithm [51], allowing the weak weights to be penalized (the connections with weak weight are eliminated). In this framework, the weights and biases of the network are assumed to be random variables with specified distributions. The regularization parameters are related to the unknown variances associated with these distributions. To take into account the uncertainly bound to the size limited of the learning set, the "Bootstrap" sampling [52] has been used to generate confidence interval for the model outputs. The results confrontation with test data of treatment plant located in Morocco [45, 46] shows that it is possible to determine online and in a very satisfactory way the optimal coagulant dose and this in various phases of functioning. To assure a good monitoring and contribute to a good operation of this process, it would be necessary to exploit all process information, such as the measurements of raw water characteristics and their evolutions resulting for example from unforeseen abnormalities, as well as the expert knowledge. For these reasons, we chose to carry the behavior monitoring of this process by using a neuro-fuzzy method, called "LAMDA" (Learning Algorithm for Multivariate Data Analysis) classification technique [53, 54], which allows aggregating this information for informing the operator by specific situations. The classification idea is the evaluation of the significant system signals (raw water quality measurements + neural coagulant dose) to recognize the factors related to such or such other situation and to help the operator to make a decision during the failure appearance. This approach was a first application that shows the utility of classification techniques in the monitoring and the surveillance of this process type. It is clear that the final objective was to spread this monitoring to other treatment processes in order to detect at the earliest a drift functioning or to identify a failure

on an upstream unit (**Figure 3**).

302 Desalination and Water Treatment

**Figure 3.** Hybrid system proposed for coagulation control and monitoring.

Water resources systems management practice, include drinking water treatment process, around the world is challenged by serious problems. Climate change and land use change are increasingly recognized as having the major impact on hydrologic variables and therefore on management of water resources. Certainly, the profession has been slow to acknowledge these changes, and that fundamentally new approaches will be required to address them. Evolutionary algorithms are becoming more prominent in the water treatment processes field. Significant advantages of evolutionary algorithms include: (1) no need for an initial solution; (2) ease of application to nonlinear problems and to complex systems; (3) production of acceptable results over longer time horizons; and (4) generation of several solutions that are very close to the optimum (and that give added flexibility to a water manager). Special attention is given to evolutionary optimization by deep neural networks to predict and capture anomalies in coagulation process, regarded as a complex and critical process. The use of deep neural networks for process modeling and control in the drinking water treatment is currently on the rise and is considered to be a key area of research. With regard to previous works, the neural approach offers the advantage of very short computational times and to be able intrinsically to describe some nonlinear relations between inputs and outputs system. In this chapter, we provided an extensive review of the most notable works to date on coagulation control and monitoring. Both classical methods and deep neural networks are ongoing hot research topics in the recent decades. There are a large number of new developing techniques and emerging models each year; here, we provide an inclusive framework for comprehensive understanding toward the key aspects of this field, clarify the most notable advancements and shed some light on future studies to promote lines of action for the work on this issue: developing intelligent systems for water process managing and optimization.

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When applying deep learning, one seeks to stack several independent neural network layers that, working together, produce better results than the already existing shallow structures. In this paper, we have reviewed some of these modules, as well the recent work that has been done by using them, found in the literature. Employing deep learning to data analysis and forecasting has yielded results in these cases that are better than the previously existing techniques, which is an evidence that this is a promising field for improvement in order to propose and develop online reliable systems to WTP monitoring and automatic control.
