This thesis investigates the integration of algorithm unrolling and genetic algorithms (GA) for optimizing pump scheduling in water distribution systems (WDS), a critical component for ensuring energy-efficient water delivery. In the context of modern civilization’s reliance on clean, affordable water for diverse uses, the operation of a WDS, particularly through energy-intensive pumps, presents significant challenges. Traditional optimization techniques often resort to hydraulic solvers like EPANET, which, while accurate, are computationally intensive for large-scale applications. Our methodology introduces a meta-model based on algorithm unrolling, building upon prior work and extending it to address pump scheduling with a multi-objective function focusing on both cost and energy efficiency. This approach significantly reduces the computational load, offering a faster alternative to EPANET while maintaining considerable accuracy. The meta-model demonstrated promising results in the Fossolo network, achieving comparable schedules 20 times faster than traditional methods. However, its applicability to more complex networks and its ability to capture detailed system behaviors are limited, highlighting the need for further enhancements in model stability and reproducibility. Despite these limitations, the study emphasizes the potential of meta-models as a complementary tool to traditional methods, especially in scenarios requiring rapid decision-making under computational constraints. This research contributes to the broader field of water utility management, offering insights into more sustainable and efficient operation strategies.

Convolutional Neural Networks are particularly vulnerable to attacks that manipulate their data, which are usually called adversarial attacks. In this paper, a method of filtering images using the Fast Fourier Transform is explored, along with its potential to be used as a defense mechanism to such attacks. The main contribution that differs from other methods that use the Fourier Transform as a filtering element in neural networks is the use of labeled data to determine how to filter the pictures. This paper concludes that, while the filtering proposed is hardly better than a simple low-pass filter, it still manages to improve resistance to adversarial attacks with a minimal drop in the standard accuracy of the network.