SMARTWINE - Towards Smart Water Distribution Networks (Development of Knowledge-Based Techniques for Real-Time Leak Detection)

• Applicants: Professor Dr.-Ing. Ulrich Dittmer; Dr.-Ing. Amin Ebrahim Bakhshipour, Ph.D.; Professor Dr. Sven Oliver Krumke
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management ; Mathematics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 544048327

Rapid urbanization and population growth have generated new problems in today's societies. These problems include scarcity of freshwater resources, difficulty in waste management, air pollution, traffic congestion, and deteriorating and aging infrastructures. In addition to the growing urgency of sustainable development, advances in mathematics and data science techniques have triggered the concept of "Smart Cities" to tackle those problems. Providing people with a safe, reliable, and cost-effective drinking water supply system is of primary importance in the social health, economy, and political sectors. Consequently, access to clean water and sanitation is included as one of the 17 goals in the 2030 Agenda for Sustainable Development of the United Nations (https://sdgs.un.org/goals). In this regard, water distribution networks (WDNs) are at the heart of any smart city and require new thoughts and developments to be more intelligently managed and operated. One of the main problems in WDNs is leaks in the system. Leaks cause a noticeable loss of clean water, which may result in inverse leakage, contamination from underground water, and severe operational difficulties. Early leak detection would save water and prevent small leaks from turning into bursts. Hence, early leak detection facilities are indispensable for any smart WDN to decrease losses and threats of leaks. SMARTWINE aims to investigate and utilize the potential of a combination of Machine Learning (ML), graph theory and optimization techniques to develop reliable, fast, and easy-to-use methods for real-time leak detection and alarming in WDNs.

• DFG Programme: Research Grants
• International Connection: Iran
• Cooperation Partner: Professor Dr. Ali Haghighi