WaterWise

AI-powered platform for smart water management and decision support.

Contributes to the more efficient management and sustainable use of water resources.

What Is WaterWise

WaterWise focuses on the development of an innovative methodology and platform for rational water resources management, taking Climate Change into account. The primary objective of the WaterWise project is to develop an integrated system that enables the assessment of the current state of water resources in a specific area, the forecasting of future trends under Climate Change scenarios, and the implementation of best practices for sustainable water resources management.

Objectives

The core objective of WaterWise is to support the sustainable management of water resources, considering the increasing pressures exerted by Climate Change, urbanization, and rising demand. The emerging research question, therefore, is how an “Intelligent Water Balance Assessment System” can optimize water resources management in areas with unstable and uncertain water balances, while addressing the challenges of Climate Change.

To this end, and by viewing the entire system as dynamic and subject to fluctuating conditions (climatic, consumption, infrastructure-related, etc.), it is essential to first map out both inflows and outflows (water reserves and consumption) in detail. In this way, the final integrated solution will be capable of providing forecasts of the highest possible accuracy. Specifically, the study, recording, and utilization of two data categories are required: inflow data (available surface and groundwater in the area) and outflow data (various types of water consumption). To calculate inflows, several factors are taken into account, including:

1. Geomorphological features
2. Precipitation data
3. Evapotranspiration data
4. Soil permeability and Geology
5. Surface and Groundwater potential
6. Above-ground and Surface flow
7. Hydrological and Climate data

Coordinator

Democritus University of Thrace

Participating Organizations

Aristotle University of Thessaloniki

MSENSIS

DOTSOFT

MY COMPANY

Intelligent Platform

The comprehensive reference platform will feature a multi-level intelligent operation, oriented towards a holistic approach to the problem of water balance forecasting in reference communities/areas. Key pillars include the modeling of water balance using AI and Machine Learning (ML) models, supporting real-time data collection from automated IoT systems, developing a reference architecture for implementing prototype integrated technological water balance forecasting systems, and establishing a methodology for developing prototype water balance communities to effectively exploit the project’s results.