Water Distribution Networks (WDNs) are critical infrastructure for delivering clean and safe drinking water. As modern WDNs increasingly integrate cyber technologies, they evolve into complex cyber–physical systems (CPSs). This connectivity, however, introduces new vulnerabilities, including cyberattacks. Cybersecurity protects systems from unauthorized access, attacks, and data breaches. In this systematic review, we adopted the PRISMA 2020 reporting guideline. Predefined keyword strings were designed to extract relevant articles from Scopus and Web of Science during the period of 2014–2025. In total, 32 peer-reviewed studies were included for narrative synthesis following duplication and eligibility screening. The review protocol was not registered. This review provides a unified perspective on how Artificial Intelligence (AI) contributes to WDNs resilience. The literature is evaluated in terms of detection tasks, data modalities, learning paradigms, and model architecture. The results highlight three key findings: (a) data bias, reflected in significant reliance on specific synthetic datasets and limited use of real-world utility network data; (b) performance, with deep learning architecture, such as long-short-term memory models, achieving commendable levels of accuracy in intrusion detection, however, overall comparison with other models remain scenario-dependent; and (c) future directions, synthesized through an AI-centered perspective that emphasizes resilience and identifies research gaps in adaptive online learning, attack prediction, interpretability, federated learning and topology localization. This study concludes with recommendations for the broader integration of AI tools to support resilient WDN operation.

Accurate prediction of mean wave overtopping discharge is essential for the safe and cost-effective design of coastal defence structures. While traditional empirical, physical, and numerical models remain important, Machine Learning (ML) has recently emerged as a powerful complementary tool. This study presents a ML–based framework to predict mean wave overtopping discharge at sloped breakwaters, with a focus on both predictive accuracy and model interpretability, supported by a series of structured pre- and post-processing steps. Five ML algorithms were evaluated: two decision tree–based models, i.e., Random Forest (RF) and Gradient Boosted Decision Trees (GBDT), and three kernel-based models, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). The models were trained and validated using the EurOtop (2018) dataset on sloped breakwaters. Among them, GPR yielded the best predictive performance, achieving an R² of 0.80 and the lowest RMSE, MAE, and RAE values (0.100, 0.013, and 0.30, respectively), indicating a strong agreement with observed data. Feature importance analysis revealed that Relative Freeboard and Freeboard Deficit (FD) were the most influential parameters across the models. To enhance interpretability and practical usability, we translated the ML findings into mathematical expressions using polynomial regression and Genetic Programming (GP). A new set of simplified equations was developed to estimate mean overtopping discharge ( q ) based solely on FD, effectively modelling the relationship between FD and ln( q ) within the EurOtop dataset. The proposed formulae provide coastal engineers with a rapid, interpretable, and reliable tool for estimating mean wave overtopping, significantly enhancing design efficiency and decision-making under uncertainty. By bridging the gap between advanced data-driven techniques and practical engineering needs, this work advances the integration of ML into coastal infrastructure design and supports the development of more adaptive and climate-resilient defence systems.

Extreme weather events (EWEs) including storms, heatwaves, floods, droughts, wildfires, and cold snaps present an increasing threat to protected habitats and species (PHAS) as well as ecosystem services they provide. Under ongoing climate change, the frequency, intensity, and spatial extent of such events are projected to increase, with disproportionately severe impacts on ecologically sensitive and conservation-priority areas. Despite this growing risk, existing vulnerability assessment frameworks for PHAS have primarily focused on long-term, gradual climatic shifts, largely overlooking the episodic and often catastrophic nature of EWEs. Here, we propose a structured methodological framework to assess the vulnerability of PHAS to extreme climatic events, based on the three IPCC vulnerability components including exposure, sensitivity, and adaptive capacity (ExP-SeN-AdC). The framework introduces the Extreme Event Vulnerability Index (EVI), an integrative metric designed to quantify vulnerability of PHAS to EWEs in a transparent, standardized, and scalable manner. The framework follows an eight-step process: (1) defining the assessment scope; (2) selecting ecologically meaningful indicators; (3) evaluating data availability and acquiring data; (4) constructing component indices for ExP-SeN-AdC; (5) computing the composite vulnerability metric; (6) evaluating uncertainties through validation and sensitivity analysis; (7) communicating and refining results through stakeholder engagement; and (8) formulating science-based conservation and adaptation recommendations. Finally, we demonstrate application of the proposed framework through a case study on Atlantic Salt Meadow habitats (Glauco-Puccinellietalia Maritimae) located within Dublin Bay’s Natura 2000 sites, encompassing a mosaic of Special Areas of Conservation (SACs) and Special Protected Areas (SPAs) under the EU Habitat Directive. The framework and its methodological approach lay the foundation for the vulnerability analysis and mapping, advancing adaptation planning, and guiding targeted interventions and conservation efforts, and the development of forward-looking conservation policies in the face of escalating extreme climatic threats.