Accurate flood prediction models and effective flood preparedness rely on thoroughly understanding rainfall–runoff dynamics. Similarly, effective rainfall–runoff models account for multiple interrelated parameters for robust runoff prediction. Process-based physical models offer valuable insights into hydrological processes, but their effectiveness can be hindered by data limitations or difficulties in acquiring specific data. Motivated by the frequent flooding events and limited data availability in the East Branch DuPage watershed, Illinois, this study addresses a critical gap in research by investigating effective discharge prediction methods. In this study, two significant machine learning (ML) models, artificial neural network (ANN) and support vector machine (SVM), were employed for discharge prediction. Historical data spanning from 2006 to 2021 were utilized to assess the performance of the models. Hyperparameter tuning was performed on the models to optimize their performance, and root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and the normalized root mean squared error (NRMSE) were used as evaluation metrics. Although both machine learning models demonstrated strong performance, the analysis revealed that the ANN model emerged as the more reliable option for predicting discharge in the watershed. Crucially, the ANN model surpassed the SVM model’s performance, achieving superior accuracy in predicting peak discharge events within the study area. Our findings have the potential to assist decision-makers and communities in implementing more dependable flood mitigation strategies, particularly in regions where hydrology data are limited.

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Resilience-based decision-making for urban water distribution systems (WDSs) is a challenge when WDS sensing data contain incomplete or missing values. This study investigated the impact of missing data imputation on a WDS resilience evaluation depending on missing data percentages. Incomplete datasets for the nodal pressure of the C-town WDS were developed with 10%, 30%, and 50% missing data percentages by manipulating a true dataset for normal operation conditions produced using EPANET. This study employed multiple imputation methods including classification and regression trees, predictive mean matching, linear regression regarding model error, and linear regression using projected values. Then, resilience values were evaluated and compared using unimputed and imputed datasets. An analysis of performance indicators based on NRMSE, NMAE, NR-Square, and N-PBIAS revealed that higher missing-data percentages led to increased deviation between the true and imputed datasets. The resilience evaluation using unimputed datasets produced significant deviations from the true resilience values, which tended to increase as the missing data percentages increased. However, the imputed datasets substantially contributed to reducing the deviations. These findings underscore the contributions of data imputation to enhancing resilience evaluation in WDS decision-making and suggest insights into advancing a resilience evaluation framework for urban WDSs with more reliable data imputation approaches.

Hybrid water supply systems (WSSs) integrating centralized and decentralized water systems have gained increasing interest in recent years to enhance water service sustainability and system resilience. An example of implementing hybrid WSSs is water microgrids, inspired by energy microgrids. Water microgrids can be depicted as a network (grid) of localized networks (sub-grids) comprising local water sources and their storage and distribution systems that operate in conjunction with a central WSS. They can operate in both ‘grid-connected or ‘islanded’ mode and support interaction and demand trade-offs with centralized WSSs at varying degrees of decentralization, providing flexibility and increased control over water resources. However, the concept of water microgrids is still in its infancy, and their application is limited due to a lack of design guidance and frameworks. This paper provides a comprehensive review of water microgrids, discussing the concept, design, benefits, and potential challenges by drawing insights from energy microgrids, and also discusses the standpoint in comparison with centralized, decentralized, and hybrid WSSs. It also explores integration of decentralized and hybrid infrastructure within existing WSSs, highlighting the balance between localized optimization and systemwide sustainability. The findings aim to broaden understanding of water microgrids, assessing their applicability and operational strategies in urban settings.

Traditional centralized water systems are facing sustainability challenges due to climate and socioeconomic changes, extreme weather events, and aging infrastructure and their uncertainties. The energy sector has addressed similar challenges using the microgrid approach, which involves decentralized energy sources and their supply, improving system resilience and sustainable energy supply. This study investigated the resilience effects of water microgrids, which feature operational interactions between centralized and local systems for sustainable water supply. A lab-scale water distribution model was tested to demonstrate centralized, decentralized, and microgrid water systems under the disruption scenarios of pump shutdown, pump rate manipulation, and pipe leaks/bursts. The water microgrids integrate centralized and local systems’ operations, while the decentralized system operates independently. Then, functionality-based resilience and its attributes were evaluated for each disruption scenario. The results reveal that, overall, the microgrid configuration, with increased water supply redundancy and flexible operational adjustment based on system conditions, showed higher resilience, robustness, and recovery rate and a lower loss rate across disruption scenarios. The resilience effect of water microgrids was more evident with longer and more severe disruptions. Considering global challenges in water security under climate and socioeconomic changes, the findings suggest insights into a hybrid water system as a strategy to enhance resilience and water use efficiency and provide adaptive operations for sustainable water supply.

Urban water distribution networks (WDNs) are increasingly vulnerable to diverse disruptions, including pipe leaks/bursts and cyber–physical failures. A critical step in a resilience-based approach against these disruptions is the rapid and reliable identification of failures and their types for the timely implementation of emergency or recovery actions. This study proposes a framework for sensor placement and multiple failure type classification in WDNs. It applies a wrapper-based feature selection (recursive feature elimination) with Random Forest (RF–RFE) to find the best sensor locations and employs an Autoencoder–Random Forest (AE–RF) framework for failure type identification. The framework was tested on the C-town WDN using the failure type scenarios of pipe leakage, cyberattacks, and physical attacks, which were generated using EPANET-CPA and WNTR models. The results showed a higher performance of the framework for single failure events, with accuracy of 0.99 for leakage, 0.98 for cyberattacks, and 0.95 for physical attacks, while the performance for multiple failure classification was lower, but still acceptable, with a performance accuracy of 0.90. The reduced performance was attributed to the model’s difficulty in distinguishing failure types when they produced hydraulically similar consequences. The proposed framework combining sensor placement and multiple failure identification will contribute to advance the existing data-driven approaches and to strengthen urban WDN resilience to conventional and cyber–physical disruptions.