# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 6 Conclusions and Future Work  
This study demonstrates the potential of AI and IoT in optimizing urban water networks, achieving significant water savings with a 96.62% optimization rate. Although it is apparent that nodes with fewer inhabitants have lower water consumption, the more crucial insight is the impact of distribution efficiency, leak minimization, and network optimization on overall resource savings, even for smaller populations. The following key contributions were derived from the conclusions:  
1. Methodological Framework: the integration of EPANET simulations with linear regression techniques provides a robust framework for analyzing and optimizing water distribution networks. 2. Scenario-Based Analysis: the study's scenario-based approach offers valuable insights into different urban water distribution networks' unique characteristics and optimization potentials. 3. Optimization Insights: the results provide actionable insights for water resource managers, highlighting nodes with significant optimization potential and contributing to more efficient water distribution system designs. 4. Predictive Modeling: the developed predictive models can be utilized for future planning and management, assisting in making data-driven decisions for infrastructure improvements. Overall, this research contributes to understanding water distribution dynamics and applying predictive analytics in enhancing water resource management. This study has several practical implications for urban water management and public policy. The proposed system offers an economically viable means of reducing operational costs by minimizing water wastage and enhancing resource allocation through predictive insights. Maintenance strategies can leverage real-time data for timely interventions, reducing downtime and extending infrastructure lifespan. Additionally, adopting this AI- and IoT-based architecture can assist urban policymakers in addressing water scarcity and resilience goals. For effective integration, local governments might consider phased rollouts that prioritize high-demand areas, align with existing infrastructure, and promote collaboration between public utilities and technology providers to ensure sustainable implementation. Future research will focus on conducting real-world pilot studies in collaboration with local water authorities to validate the scalability of the proposed architecture in practical settings, where it can be addressed the integration of EPANET with real-time data collection systems and upgrading tools capable of handling larger networks, such as coupling EPANET with Python-based optimization for the improvement of modeling precision. These pilots will explore the feasibility of deploying and integrating sensors into existing infrastructure, focusing on cities facing varying environmental challenges. Additionally, we plan to further develop advanced AI techniques, such as deep learning or graph-based models, to enhance prediction accuracy, particularly in more complex networks like Lugo. Exploring the potential for integrating water systems with other urban services, such as energy or waste management, will also be a key research avenue. Such integration could lead to comprehensive smart city solutions, contributing to developing more sustainable and resilient urban environments. Additionally, there are plans to deploy sensors within real-world water distribution infrastructure, enabling the validation of simulation results with real-time data to fine-tune optimization models. Moreover, we plan to explore the impact of incorporating dynamic unit_cost values, perhaps linked to environmental data or predictive trends. This adjustment could improve the model's adaptability and responsiveness to changing conditions, further aligning our optimization goals with sustainable water resource management.