SDG-6

SDG-6 Clean water and sanitation

Main
SDG-6

Targets of this Goal

By 2030, it is necessary to achieve the following targets:

Attain universal and equal access to drinking water at a feasible price and adequate sanitation and hygiene services. End open-air defecation.
Improve the quality of water (less contamination, minimizing chemical and hazardous waste, reducing untreated waste-water and increase reuse and recycling)
Efficient use of hydric resources. Reduce number of people who suffer from water shortage
Implement an integrated management of hydric resources
Protect and re-establish water-related ecosystems

We can currently advance towards the attainment of this goal:

How does technology contribute to this SDG? See what we propose and tell us what you think

Lessons Learnt

We have analyzed the literature and extracted the following ideas on how technologies are favouring this SDG.
See also what we propose and tell us what you think

L.1

Artificial Intelligence plays an important role in modeling and predicting the behavior of complex water systems

L.2

The implementation of intelligent sensor networks (IoT) provides very large and valuable information that has yet to be exploited

The union of the two previous points will allow the development of digital twins in water infrastructures in order to better understand these infrastructures (and their consequent optimization), thus helping to yield smarter cities and territories

L.4

The control and minimization of water pollution is a very current topic in which many AI and related techniques are being applied

L.5

Blockchain techniques for the democratization of access and control of water resources have not yet been developed much (but it is certainly something worth studying)

Recommendations

The proposed actions on which we introduce an opinion poll for this SDG, are described below:

Poll for SDG-6

The analyses of interrelationships between emerging technologies & AI, and the possibilities they offer to pursue the SDGs, led us to propose a series of actions and/or potential projects to develop.
Among these recommendations, which one is more important in your opinion?

Give my opinion

R6.1 - IoT technologies for monitoring all kinds of water facilities, networks and ecosystems.

The deployment of connected sensor networks (for example through wireless technologies such as 5G ) in water facilities (purification plants, treatment and purification plants, tanks, dams, etc.) as well as in distribution networks (pipes, collectors, rivers, ditches) and even in natural environments related to water (rivers, swamps, lakes, aquifers, coastal areas, etc.), it will allow monitoring in almost real time of the state of the infrastructures and the quality of the water.

R6.2 - Digital Twins on water infrastructures, on water-linked ecosystems and contamination process that allow their study, experimentation and dissemination

Many of the AI techniques allow to create models that accurately simulate the behavior of complex non-linear systems such as water systems in general. The creation of these models is an important step towards the implementation of the digital twin since they behave as a virtual replica of real systems with which we can experiment by changing their operating parameters or make predictions in future scenarios or even scenarios not previously contemplated

R6.3 - Intelligent techniques for the prediction and detection of failures and breaks in water infrastructures, as well as the detection of variables or parameters for which there is no specific sensorization

The use of techniques such as artificial neural networks or machine learning can facilitate the detection and localization of these breaks and consequent losses of water (also saving a significant amount of time and money that not having a clear location of the fault). Likewise, some variables related to water systems (such as the presence of certain polluting elements) may not be available (due to the lack or cost of certain types of sensors).

R6.4 - Management and governance of resources, infrastructures and ecosystems related to water through the use of decision support systems, blockchain as well as virtual reality and augmented reality

R6.5 - Use of machine learning techniques to improve the prediction models of existing water systems

The deployment of sensor networks connected in water facilities (purification plants, treatment plants, tanks, dams, etc.) as well as in distribution networks (pipes and sewerage, collectors, rivers, ditches) and even in natural environments related to water (rivers, swamps, lakes, aquifers, coastal areas, etc.), will allow an almost real-time monitoring of the state of infrastructures and water quality. In addition, these sensor networks will produce a massive amount of data (essential for a subsequent application of big data techniques) and time series that will be essential for the creation of much more accurate models that take into account a much larger variety of factors.