Innovative Data Analysis Methodologies in the Water Sector: Water Quality and Water Management

Dear Colleagues,

The main objective of this Special Issue is to show the scientific community how new innovative data analysis methodologies (e.g., machine learning, deep learning, artificial intelligence, blockchain, etc.) can be of great help for the management and quality of water resources, and complement classical management methodologies.

These types of methodologies can be used to predict water demands, distribution system failures, selection of treatment technologies, prediction of the behaviour of a given pollutant, and so on.

Although they are increasingly present in the water sector, their real application is still limited in certain areas such as treatment management.

The impact of global population growth, coupled with increased human activity, on the natural environment is leading to increased water stress in many parts of the world. This situation will be aggravated in the coming decades as a consequence of climate change and a more irregular water regime. For this reason, there is an increasing need for excellent water resource management to maximise the use of water resources with the least use of external resources. In recent years, the growth in technological knowledge has enabled the development of innovative tools for data analysis (e.g. artificial intelligence, machine learning or deep learning), which can become our allies in achieving optimal water resource management.

The aim of this Special Issue on “Innovative data analysis methodologies in the water sector: water quality and water management” is to present the state-of-the-art related but not limited to the application of these innovative technologies both in the urban water management and natural water resources.

We invite authors to submit research articles, reviews, communications, and concept papers that demonstrate the high potential of these methodologies in the water sector.

Prof. Dr. Jorge Rodríguez-Chueca
Prof. Dr. David J. Vicente González
Prof. Dr. Elena Torfs
Guest Editors

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