Enhancing inflow and infiltration detection in urban sewer networks with a new deterministic sensor placement method.

The inflow and infiltration (I&I) is an issue for many urban sewer networks (USNs), which can significantly affect system functioning. Placing sensors within the USNs is a typical approach to detect large I&I event, but deploying a limited number of sensors while achieving maximum detection reliability is challenging. While some methods are available for sensor placement, they are generally heuristic search-based methods (HSBMs) and hence the resultant sensor placement strategies (SPSs) are variable over different algorithm runs or parameterizations. This paper develops a new deterministic two-stage clustering method for SPS optimization based on information entropy. Within the first stage, the Spectral Clustering method is applied to assign USN nodes to different clusters according to their joint entropy. In the second stage, the topology structure property is considered to enable further clustering for improving detection reliability. Average I&I detection reliability is used to select clusters and the optimal SPS is identified by maximizing joint entropy of all possible solutions where a single sensor is assigned to each selected cluster. The proposed method and two existing HSBMs are applied to a real USN and their performance is compared. The results obtained show that: (i) a strong correlation coefficient R (R > 0.95) is observed between joint entropy and SPS's detection reliability, which has not been revealed before, (ii) the proposed method consistently outperforms the other two approaches in efficiently offering SPSs with about 7-15 % higher detection reliability, and (iii) the proposed method provides the optimal SPS in a deterministic manner, which makes it attractive for engineering applications.

Making Waves: Towards data-centric water engineering.

Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery. This perspective calls for the development of data-centric water engineering to tackle water challenges in a changing world. Building on the historical evolution of water engineering from empirical and theoretical paradigms to the current computational paradigm, we argue that a fourth paradigm, i.e., data-centric water engineering, is emerging driven by recent AI advances. Here we define a new framework for data-centric water engineering in which data are transformed into knowledge and insight through a data pipeline powered by AI technologies. It is proposed that data-centric water engineering embraces three principles - data-first, integration and decision making. We envision that the development of data-centric water engineering needs an interdisciplinary research community, a shift in mindset and culture in the academia and water industry, and an ethical and risk framework to guide the development and application of AI. We hope this paper could inspire research and development that will accelerate the paradigm shift towards data-centric water engineering in the water sector and fundamentally transform the planning and management of water infrastructure.

Economic assessment of nature-based solutions to reduce flood risk and enhance co-benefits.

Flooding is expected to increase due to climate change, urbanisation, and land use change. To address this issue, Nature-Based Solutions (NBSs) are often adopted as innovative and sustainable flood risk management methods. Besides the flood risk reduction benefits, NBSs offer co-benefits for the environment and society. However, these co-benefits are rarely considered in flood risk management due to the inherent complexities of incorporating them into economic assessments. This research addresses this gap by developing a comprehensive methodology that integrates the monetary analysis of co-benefits with flood risk reduction in economic assessments. In doing so, it aspires to provide a more holistic view of the impact of NBS in flood risk management. The assessment employs a framework based on life-cycle cost-benefit analysis, offering a systematic and transparent assessment of both costs and benefits over time supported by key indicators like net present value and benefit cost ratio. The methodology has been applied to the Tamnava basin in Serbia, where significant flooding occurred in 2014 and 2020. The methodology offers valuable insights for practitioners, researchers, and planners seeking to assess the co-benefits of NBS and integrate them into economic assessments. The results show that when considering flood risk reduction alone, all considered measures have higher costs than the benefits derived from avoiding flood damage. However, when incorporating co-benefits, several NBS have a net positive economic impact, including afforestation/reforestation and retention ponds with cost-benefit ratios of 3.5 and 5.6 respectively. This suggests that incorporating co-benefits into economic assessments can significantly increase the overall economic efficiency and viability of NBS.

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA.

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

Service-lines as major contributor to water quality deterioration at customer ends.

Biofilm detachment contributes to water quality deterioration. However, the contributions of biofilm detachment from different pipes have not been quantified or compared. Following the introduction of partial reverse osmosis (RO) in drinking water production, this study analyzed particles at customers' ends and tracked their origins to water distribution mains and service lines. For doing so, filter bags were installed in front of water meters to capture upstream detached particles, while biofilm from water main and service line were sampled by cutting pipe specimens. The results showed that elemental concentrations of the biofilm in mains were higher than those of service lines (54.3-268.5 vs. 27.1-44.4 µg/cm2), both dominated by Ca. Differently, filter bags were dominated by Fe/Mn (77.5-98.1%). After introducing RO, Ca significantly decreased in biofilms of mains but not service lines, but the released Fe/Mn rather than Ca arrived at customers' ends. The ATP concentrations of service lines were higher than mains, which decreased on mains but increased in service lines after introducing RO. For the core ASVs, 13/24 were shared by service lines (17), mains (21), and filter bags (17), which were assigned mainly to Nitrospira spp., Methylomagnum spp., Methylocytis spp., and IheB2-23 spp. According to source tracking results, service lines contributed more than mains to the particulate material collected by filter bags (57.6 ± 13.2% vs. 13.0 ± 11.6%). To the best of our knowledge, the present study provides the first evidence of service lines' direct and quantitative contributions to potential water quality deterioration at customers' ends. This highlights the need for the appropriate management of long-neglected service line pipes, e.g., regarding material selection, length optimization, and proper regulation.

Governance using the water-food-energy nexus and human-factor measures.

Household water food and energy (WFE) expenditures, reflect respective survival needs for which their resources and social welfare are inter-related. We developed a policy driven quantitative decision-making strategy (DMS) to address the domain geospatial entities' (nodes or administrative districts) of the WFE nexus, assumed to be information linked across the domain nodal-network. As investment in one of the inter-dependent nexus components may cause unexpected shock to the others, we refer to the WFE normalized expenditures product (Volume) as representing the nexus holistic measure. Volume rate conforms to Boltzman entropy suggesting directed information from high to low Volume nodes. Our hypothesis of causality-driven directional information is exemplified by a sharp price increase in wheat and rice, for U.S. and Thailand respectively, that manifests its impact on the temporal trend of Israel's administrative districts of the WFE expenditures. Welfare mass (WM) represents the node's Volume combined with its income and population density. Formulation is suggested for the nodal-network WM temporal balance where each node is scaled by a human-factor (HF) for subjective attitude and a superimposed nodal source/sink term manifesting policy decision. Our management tool is based on two sequential governance processes: one starting with historical data mapping the mean temporal nodal Volumes to single out extremes, and the second is followed by WM balance simulation predicting nodal-network outcome of policy driven targeting. In view of the proof of concept by model simulations in in our previous research, here HF extends the model and attention is devoted to emphasize how the current developed decision-making approach categorically differs from existing nexus related methods. The first governance process is exemplified demonstrating illustrations for Israel's districts. Findings show higher expenditures for water and lower for energy, and maps pointing to extremes in districts' mean temporal Volume. Illustrations of domain surfaces for that period enable assessment of relative inclination trends of the normalized Water, Food and Energy directions continuum assembled from time stations, and evolution trends for each of the WFE components.

An Analysis of SARS-CoV-2 in Wastewater to Evaluate the Effectiveness of Nonpharmaceutical Interventions against COVID-19 in The Netherlands.

Wastewater-based epidemiology (WBE) is increasingly being recognized as a powerful tool for detecting and monitoring SARS-CoV-2 trends at a population level. This study looked to extend the use of WBE to explore the effectiveness of nonpharmaceutical interventions (NPIs) that have been used in response to COVID-19 and compare the results to the effect of such interventions on COVID-19 hospitalizations. A data-driven approach demonstrated that trends of SARS-CoV-2 RNA in wastewater, from Amsterdam and Utrecht (The Netherlands), precede hospitalizations by at least 3-9 days. Additionally, the effect of NPIs can be seen in wastewater and hospitalizations after 20 and 24 days, respectively. Changepoint analysis indicated that the closure of schools and universities significantly reduced the level of SARS-CoV-2 RNA in wastewater and COVID-19 hospitalizations. Regression modeling suggested the stay-at-home policy is an effective intervention for reducing the level of SARS-CoV-2 RNA in wastewater, whereas the closure of workplaces significantly reduced hospitalizations in both Dutch cities. This study demonstrates how WBE can be used to inform public health decisions and anticipate future strain on healthcare facilities in major cities but also indicates a need for higher temporal resolution of wastewater sampling.

Does establishing a neurovascular unit improve the outcome after surgical clipping for aneurysmal subarachnoid hemorrhage? Results from a 5-year observational study in Kuwait.

BACKGROUND: Failure to prevent rebleeding after cerebral subarachnoid hemorrhage (SAH) is the most frequent reason for high morbidity and mortality of aneurysmal SAH. Our study aims to identify the outcome after surgical clipping of aneurysmal SAH before and after the establishment of the neurovascular unit. The clarifications of the positive turnover in the outcome will be discussed. METHODS: A retrospective cohort analysis was carried out on our experience with a controlled group of patients who underwent clipping for ruptured cerebral aneurysms (n = 61) from January 2015 to December 2019. A modified Rankin scale (mRS) was used to determine the outcome after 6 months of follow-up. RESULTS: The median mRS score (i.e., outcome) on admission was 4, whereas it was with a median score of 2 six months after clipping (P ≤ 0.001). Overall, the cases with a good outcome were 63.9% of the sample, while the poor outcome conditions were 36.1%. The most cases with an improved outcome were after introducing the neurovascular unit, representing a transition of aneurysmal clipping practice in our center. The good outcome was changed from 42% to 76.7%, and the poor outcome was changed from 58% to 23.3% (P = 0.019). The crude mortality rate was similar to the rate worldwide (18%), with a noticeable decrease after organizing a neurovascular subspecialty. CONCLUSION: The outcome after clipping of ruptured SAH can be largely affected by the surgeon's experience and postoperative intensive care. Organizing a neurovascular team is one of the major factors to achieve good outcomes.

Optimal virtual water flows for improved food security in water-scarce countries.

The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19-45%) and a decrease in WFs (2-3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.

Foul sewer model development using geotagged information and smart water meter data.

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.

Water quality modeling in sewer networks: Review and future research directions.

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.

Mapping the journey of transition: a single-center study of 170 childhood-onset GH deficiency patients.

OBJECTIVE: To analyze metabolic parameters, body composition (BC), and bone mineral density (BMD) in childhood-onset GH deficiency (COGHD) patients during the transition period (TP). DESIGN: Single- center, retrospective study was performed on 170 consecutive COGHD patients (age 19.2 ± 2.0 years, range 16-25) transferred after growth completion from two pediatric clinics to the adult endocrine unit. Two separate analyses were performed: (i) cross-sectional analysis of hormonal status, metabolic parameters, BC, and BMD at first evaluation after transfer from pediatrics to the adult department; (ii) longitudinal analysis of BC and BMD dynamics after 3 years of GH replacement therapy (rhGH) in TP. RESULTS: COGHD was of a congenital cause (CONG) in 50.6% subjects, tumor-related (TUMC) in 23.5%, and idiopathic (IDOP) in 25.9%. TUMC patients had increased insulin and lipids levels (P < 0.01) and lower Z score at L-spine (P < 0.05) compared to CONG and IDOP groups. Patients treated with rhGH in childhood demonstrated lower fat mass and increased BMD compared to the rhGH-untreated group (P < 0.01). Three years of rhGH after growth completion resulted in a significant increase in lean body mass (12.1%) and BMD at L-spine (6.9%), parallel with a decrease in FM (5.2%). CONCLUSION: The effect of rhGH in childhood is invaluable for metabolic status, BC, and BMD in transition to adulthood. Tumor-related COGHD subjects are at higher risk for metabolic abnormalities, alteration of body composition, and decreased BMD, compared to those with COGHD of other causes. Continuation of rhGH in transition is important for improving BC and BMD in patients with persistent COGHD.

Real-time foul sewer hydraulic modelling driven by water consumption data from water distribution systems.

Real-time hydraulic modelling can be used to address a wide range of issues in a foul sewer system and hence can help improve its daily operation and maintenance. However, the current bottleneck within real-time FSS modelling is the lack of spatio-temporal inflow data. To address the problem, this paper proposes a new method to develop real-time FSS models driven by water consumption data from associated water distribution systems (WDSs) as they often have a proportionally larger number of sensors. Within the proposed method, the relationship between FSS manholes and WDS water consumption nodes are determined based on their underlying physical connections. An optimization approach is subsequently proposed to identify the transfer factor k between nodal water consumption and FSS manhole inflows based on historical observations. These identified k values combined with the acquired real-time nodal water consumption data drive the FSS real-time modelling. The proposed method is applied to two real FSSs. The results obtained show that it can produce simulated sewer flows and manhole water depths matching well with observations at the monitoring locations. The proposed method achieved high R2, NSE and KGE (Kling-Gupta efficiency) values of 0.99, 0.88 and 0.92 respectively. It is anticipated that real-time models developed by the proposed method can be used for improved FSS management and operation.

Using Complex Network Analysis for Optimization of Water Distribution Networks.

The optimization of water networks supports the decision-making process by identifying the optimal trade-off between costs and performance (e.g., resilience and leakage). A major challenge in the domain of water distribution systems (WDSs) is the network (re)design. While the complex nature of WDS has already been explored with complex network analysis (CNA), literature is still lacking a CNA of optimal water networks. Based on a systematic CNA of Pareto-optimal solutions of different WDSs, several graph characteristics are identified, and a newly developed CNA design approach for WDSs is proposed. The results show that obtained designs are comparable with results found by evolutionary optimization, but the CNA approach is applicable for large networks (e.g., 150,000 pipes) with a substantially reduced computational effort (runtime reduction up to 5 orders of magnitude).

Ruptured cerebral pseudoaneurysm in an adolescent as an early onset of COVID-19 infection: case report.

The clinical manifestations of coronavirus disease 2019 (COVID-19) are non-specific and multi-inflammatory. They vary from mild to severe manifestations that can be life-threatening. The association of SARS-CoV-2 infection and pseudoaneurysm formation or rupture of an already existing aneurysm is still unexplored. Several mechanisms may be involved, including the direct destruction to the artery by the viral infection or through the release of the inflammatory cytokines. We are presenting a case of a 13-year-old girl with a ruptured cerebral pseudoaneurysm of the left middle cerebral artery (M2 segment) with severe intracerebral hemorrhage as the earliest manifestation of COVID-19 infection.

The Impact of Intraoperative Monitoring on Extent of Resection and Long-Term Neurological Outcomes: A Series of 39 Intramedullary Ependimomas.

AIM: To analyze the impact of intraoperative neurophysiological monitoring (IONM) on the extent of removal and long-term neurological outcomes in a series of grade II ependymomas. MATERIAL AND METHODS: We retrospectively reviewed 88 consecutive patients who underwent surgical resection of an intramedullary spinal cord tumor (IMSCT) at the Clinic of Neurosurgery of the Clinical Center of Serbia in Belgrade between January 2012 and December 2017. In all, 39 patients (25 males and 14 females; mean age 46.16 years) with grade II ependymomas were enrolled in this study; the mean follow-up time was 49.84 months. The modified McCormick Scale (mMCS) was used to assess the short- and long-term outcomes, and the patients were divided into two groups based on whether they underwent IONM. RESULTS: The gross-total removal rate was 89.7%, and it was not influenced by use of IONM, location or tumor size. Upon admission,43.2% of the patients were dependent (grades IV and V), while 56.8% were independent (grades I, II and III), according to the mMCS. After 3 months of follow-up, 76.9% of the patients maintained or improved their neurological status, but this percentage was reduced after long-term follow-up. CONCLUSION: Total surgical resection with good neurological outcomes can be achieved in the vast majority of patients with grade II ependymomas; it is important to emphasize that the use of IONM allows acceptable extent of resection and provides better results in terms of functional outcomes, with lower morbidity rates. Therefore, no correlation was demonstrated between the decrease in the basal amplitudes of IONM and D-waves and poor neurological outcomes.

Assessing the global resilience of water quality sensor placement strategies within water distribution systems.

Water quality sensors are often spatially distributed in water distribution systems (WDSs) to detect contamination events and monitor quality parameters (e.g., chlorine residual levels), thereby ensuring safety of a WDS. The performance of a water quality sensor placement strategy (WQSPS) is not only affected by sensor spatial deployment that has been extensively analyzed in literature, but also by possible sensor failures that have been rarely explored so far. However, enumerating all possible sensor failure scenarios is computationally infeasible for a WQSPS with a large number of sensors. To this end, this paper proposes an evolutionary algorithm (EA) based method to systematically and efficiently investigate the WQSPS' global resilience considering all likely sensor failures. First, new metrics are developed in the proposed method to assess the global resilience of a WQSPS. This is followed by a proposal of an efficient optimization approach based on an EA to identify the values of global resilience metrics. Finally, the sensors within the WQSPS are ranked to identify their relative importance in maintaining the WQSPS's detection performance. Two real-world WDSs with four WQSPSs for each case study are used to demonstrate the utility of the proposed method. Results show that: (i) compared to the traditional global resilience analysis method, the proposed EA-based approach identifies improved values of global resilience metrics, (ii) the WQSPSs that deploy sensors close to large demand users are overall more resilient in handling sensor failures relative to other design solutions, thus offering important insight to facilitate the selection of WQSPSs, and (iii) sensor rankings based on the global resilience can identify those sensors whose failure would significantly reduce the WQSPS's performance thereby providing guidance to enable effective water quality sensor management and maintenance.

Correlation between isometric strength in five muscle groups and inclination angles of spine.

PURPOSE: This study was aimed to identify correlation between maximum isometric strength in five muscle groups determined by dynamometry results of muscle tests and the inclination angles of the spine. METHODS: This predictive correlational study included 63 young healthy athletes aged 10-15. (m/f 31/32; 12.73 ± 1.58 years; 162.57 ± 12.94 cm; 52.86 ± 12.17 kg; 3.95 ± 1.70 training years; 4.05 ± 1.31 training h/week).The maximum isometric strength in five muscle groups was measured by a handheld dynamometer with external belt fixation using a portable stabilization device. The inclination angles were measured with a digital inclinometer. The data were analyzed using descriptive statistics, and correlations were estimated by Pearson's correlation coefficient (r). RESULTS: The isometric muscular strength of the muscle group of the hip extensors was in a significant correlation with the lumbar lordosis angle (LLA), r = 0.714 (p < 0.0001). The isometric muscular strength of the muscle group of the erector spinae was in a significant correlation with the LLA, r = 0.578 (p < 0.0001) and with thoracic kyphosis angle (TKA), r = 0.522 (p < 0.0001). CONCLUSION: There is a strong association between isometric strength of the muscle groups of the hip extensors and erector spinae and the inclination angles of the spine. Based on the isometric results, physical therapy can be proposed for increasing the muscular strength of those muscle groups, which can help in the prevention of more severe forms of postural deformities. These slides can be retrieved under Electronic Supplementary Material.

Flow regime identification for air valves failure evaluation in water pipelines using pressure data.

Air valve failure can cause air accumulation and result in a loss of carrying capacity, pipe vibration and even in some situations a catastrophic failure of water transmission pipelines. Air is most likely to accumulate in downward sloping pipes, leading to flow regime transition in these pipes. The flow regime identification can be used for fault diagnosis of air valves, but has received little attention in previous research. This paper develops a flow regime identification method that is based on support vector machines (SVMs) to evaluate the operational state of air valves in freshwater/potable pipelines using pressure signals. The laboratory experiments are set up to collect pressure data with respect to the four common flow regimes: bubbly flow, plug flow, blow-back flow and stratified flow. Two SVMs are constructed to identify bubbly and plug flows and validated based on the collected pressure data. The results demonstrate that pressure signals can be used for identifying flow regimes that represent the operational state (functioning or malfunctioning) of air valves. Among several signal features, Power Spectral Density and Short-Zero Crossing Rate are found to be the best indictors to classify flow regimes by SVMs. The sampling rate and time of pressure signals have significant influence on the performance of SVM classification. With optimal SVM features and pressure sampling parameters the identification accuracies exceeded 93% in the test cases. The findings of this study show that the SVM flow regime identification is a promising methodology for fault diagnosis of air valve failure in water pipelines.

Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks.

Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.