**3. Results and discussion**

#### **3.1 The perspectives of AI in the water sector**

The AI accelerate the design of systems procurement, distribution, treatment and reuse of water, using an increasingly widespread use of computer technology and equipment monitoring. Advanced diagnostic tools make water management more customized and intelligent in the water sector. In addition the, in fact, a raised will allow you to overlay information and animations on real-world images with model projections arising from AI applications to help in activities on an daily management and planning and to manage the resource more efficiently. The *Virtual reality* (VR) can make "viewable" projections and modeling predictions on the trend basis and patterns of use of water resources or on climatic scenarios assumed, the *augmented reality* (AR), however, superimposes information generated by a computer to the real world, in quick time. The AI facilitates the integration of these worlds, analyses the incoming data stream, managing large information relating to the scene and superimposes to do it with big data, images or animations relevant, also in 3D. In the near future, we will be able to visualize the system we are imagining to design with the possibility of visualizing the efficiency of use of the different scenarios. Engineers, chemists, biologists, *designers,* etc. they will

#### *Artificial Intelligence and Water Cycle Management DOI: http://dx.doi.org/10.5772/intechopen.97385*

have new tools to develop collaborative projects, involving expert technicians and young professionals and evaluating the results of design choices in different scenarios of use.

In the coming years, the offer of AI directed to researchers and companies will be able to expand further thanks to programs that are simple to use, to be used in the design, promoting the so-called "fourth industrial revolution": a systemic transformation that can have direct impacts also in the management of the waters.

The AI can determine the output of the information correct exactly at the moment when it is needed, such as when it is necessary to make choices, reducing the chances of error and, increases efficiency and improves the productivity.

Contextualizing to the water sector, the intervention of the AI will be able to optimize the distribution or disposal of water, make the removal of polluted substances more efficient [16], facilitate the reuse of purified wastewater [17], providing real-time images of the areas in which criticalities occur.

In addition, in the last years, artificial intelligence has started to increase the efficiency of the design and synthesis phases of new materials that can also be used in the water sector, making applications faster, easier and more economical, for example, by reducing the use of chemicals or sludge.

In AI, evolutionary machine learning algorithms analyze all relevant experiments; both those that worked and those that failed, effectively preventing further possibilities for error. On the basis of the experiments carried out and the consequent success, the algorithms foresee potentially useful paths. There is no machine learning tool capable of doing all this alone, but AI-based technologies are also spreading in the design of systems and structures for water management such as reservoirs, adductors, lifting systems, distribution, sewer networks, purification plants, etc.

The management of the integrated water cycle is transversal to numerous scientific fields and artificial intelligence is also expanding in all life sciences because it helps to identify *patterns* in complex data sets [18]. The water sector, in particular, allows for huge amounts of data with which to train algorithms, offering significant development opportunities. In fact, artificial intelligence is very successful when there is the possibility of a *training set* of particularly relevant dimensions. The *deep learning* and artificial intelligence tools are amazingly powerful that will provide important answers, especially when interfaced to *smart Technologies* able to acquire data in multiple areas of the integrated water cycle [19].

Even more promising prospects derive from quantum computer applications, which in a few years could greatly exceed the performance of the classical ones, thanks to the significant work on specific hardware and algorithms, exploiting quantum mechanics to perform the calculations and returning greater and further the force on AI.
