Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲	Type	Year	Authors	Title	Venue/Journal	DOI	Abstract
publications-4031	article	2020	Luo, Jiaqing and Luo, Jiaqing and Zhou, Lingyun and Zhou, Lingyun and Feng, Yunyu and Feng, Yunyu and Li, Bo and Li, Bo and B, Li and Guo, Shujin and Guo, Shujin	The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity.		10.22541/au.160639042.27429529/v1	The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and NaΓ―ve Bayes (NB) classifier, to further select effective indicators from patientsβ€™ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44\%, {Age, NEUT, LYMC} with a selection rate of 38\%, and {Age, WBC, LYMC} with a selection rate of 9\%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators.
publications-4032	article	2021	Qiu, Mengning and Salomons, Elad and Ostfeld, Avi	An Analytical Model for the Decontamination of Water Distribution Systems Using Slug-Feed Method of Disinfection	Water Resources Research	10.1029/2020wr028277	Abstract The increasing risk of intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants in water distribution systems is becoming a major concern that has significant and adverse impacts on public health. As such, it is important to have an effective, robust, and flexible plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on the strategic planning of the decontamination process of the contaminated infrastructure. This paper proposes an analytical method for modeling the slugβ€feed method of disinfection given the drainage and disinfectant dosage profiles. The efficacy of the proposed method has been demonstrated by using it as the evaluation function in a genetic algorithm optimization of two case studies. Results show the proposed method exhibits higher robustness compared to the procedures defined in the current standard and literature. The main contributions of this study are to (1) provide a robust and accurate model to describe the slugβ€feed method of disinfection, (2) offer additional resource utilization flexibility for water authorities, and (3) providing additional levels of district metered areas prioritization.
publications-4033	article	2021	PerellΓ³-Moragues, Antoni and Perello-Moragues, Antoni and Perello-Moragues, Antoni and Poch, Manel and Poch, Manel and SaurΔ±Μ, David and SaurΓ, David and Popartan, Lucia Alexandra and Popartan, Lucia Alexandra and Popartan, Lucia Alexandra and Popartan, Lucia Alexandra and Noriega, Pablo and Noriega, Pablo	Modelling Domestic Water Use in Metropolitan Areas Using Socio-Cognitive Agents	Water	10.3390/w13081024	In this paper, we present an agent-based model for exploring the interplay of basic structural and socio-cognitive factors and conventional water saving measures in the evolution of domestic water use in metropolitan areas. Using data of Barcelona, we discuss three scenarios that involve plausible demographic and cultural trends. Results show that, in the three scenarios, aggregate outcomes are consistent with available conventional modelling (while total water use grows, per capita water use declines); however, the agent-based simulation also reveals, for each scenario, the different dynamics of simple policy measures with population growth, cultural trends and social influence; thus providing unexpected insights for policy design.
publications-4034	article	2021	Karthikeyan, Akshaya and Karthikeyan, Akshaya and Garg, Akshit and Garg, Akshit and Vinod, P. K. and Vinod, P. K. and Priyakumar, U. Deva and Priyakumar, U. Deva	Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction	Frontiers in Public Health	10.3389/fpubh.2021.626697	The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP) and age helps to predict mortality with 96\% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90\% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early and reliable manner.
publications-4035	article	2021	Saberi-Movahed, Farshad and F, Saberi-Movahed and Saberi-Movahed, Farshad and Saberi-Movahed, Farid and Mohammadifard, Mahyar and Mehrpooya, Adel and Mohammadifard, Mahyar and M, Mohammadifard and Mehrpooya, Adel and Mehrpooya, Adel and Rezaei-Ravari, Mohammad and M, Rezaei-Ravari and Berahmand, Kamal and Berahmand, Kamal and Rostami, Mehrdad and Rostami, Mehrdad and Rostami, Mehrdad and Karami, Saeed and S, Karami and Karami, Saeed and Najafzadeh, Mohammad and Najafzadeh, Mohammad and Hajinezhad, Davood and Jamshidi, Mina and Hajinezhad, Davood and Jamshidi, Mina and M, Jamshidi and Abedi, Farshid and Abedi, Farshid and Mohammadifard, Mahtab and Mohammadifard, Mahtab and E, Farbod and Farbod, Elnaz and Safavi, Farinaz and Farbod, Elnaz and Safavi, Farinaz and Dorvash, Mohammadreza and M, Dorvash and Dorvash, Mohammadreza and S, Vahedi and Dorvash, Mohammadreza and Dorvash, Mohammadreza and Eftekhari, Mohammad and Eftekhari, Mahdi and Vahedi, Shahrzad and Vahedi, Shahrzad and Tavassoly, Iman and Saberi-Movahed, Farid and Tavassoly, Iman	Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods	medRxiv	10.1101/2021.07.07.21259699	One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patientsΓΆβ‚¬β„Ά characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.
publications-4036	article	2022	James, R. and James, Ryan and Rosenberg, D. E. and Rosenberg, David E.	Agentβ€Based Model to Manage Household Water Use Through Socialβ€Environmental Strategies of Encouragement and Peer Pressure	Earthβ€™s Future	10.1029/2020ef001883	Water conservation has long been an effective component of sustainable water management. However, inelastic price responses, demand hardening, and poor public awareness reduce the effectiveness of strategies. Here we identify and quantify the effects of psychological and social factors such as attitudes, peer support, opportunities to conserve, and encouragement on household water use. We link household survey, municipal billing, aerial imagery, weather, and appliance flow and duration data. We use the data to develop, populate, and partially validate an agent-based model for 270 households in Logan, Utah. Simulated indoor water use matched observed use better than outdoor use and improved over prior studies that only conceptually validated model results. Households with stronger conservation attitudes, peer support, and more opportunities saved the most water. Peer pressure saved more water than water manager encouragement because small, diverse social networks could better regulate the behavior of outlier households within the network. Combining peer pressure and encouragement saved the most water. Results suggest managers should provide platforms for households to share their water use stories and information with each other. Managers should target conservation actions to the small fraction of households who use the most water and have large potential to save water. Mangers can use the psychological and social factors to increase household adoption of water conservation actions.
publications-4037	article	1997	Vasconcelos, John J. and Vasconcelos, John J. and Rossman, Lewis A. and Rossman, Lewis A. and Grayman, Walter M. and Grayman, Walter M. and Boulos, Paul F. and Boulos, Paul F. and Clark, Robert M. and Clark, Robert M.	Kinetics of chlorine decay	Journal American Water Works Association	10.1002/j.1551-8833.1997.tb08259.x	Proper understanding, characterization, and prediction of water quality behavior in drinking water distribution systems are critical to ensure meeting regulatory requirements and customer-oriented expectations. This article investigates the factors leading to loss of chlorine residual in water distribution systems. Kinetic rate equations describing the decay of chlorine were developed, tested, and evaluated using data collected in field-sampling studies conducted at several water utility sites. Results indicated that chlorine decay in distribution systems can be characterized as a combination of first-order reactions in the bulk liquid and first-order or zero-order mass transfer-limited reactions at the pipe wall. Wall reaction kinetic constants were inversely proportional to pipe roughness coefficients. Wide variations in both bulk reaction constants and wall reaction constants were observed among the sites.
publications-4038	article	2016	Nayak, Munir Ahmad and Nayak, Munir A. and Turnquist, Mark A. and Turnquist, Mark A.	Optimal Recovery from Disruptions in Water Distribution Networks	Computer-aided Civil and Infrastructure Engineering	10.1111/mice.12200	An optimization model is developed to guide recovery of a disrupted water distribution system. The model minimizes the total cost of recovery, including the disruption cost of unmet demand during the repair process and the repair cost itself. The optimization schedules repair tasks under precedence and resource constraints and contains an embedded flow problem that optimizes the distribution of water in each time period, given the state of the network. A simulated annealing algorithm is developed for scheduling the tasks, with the embedded flow problem solved using a generalized reduced gradient method. Experiments with a test water distribution system confirm the effectiveness of the model and provide insight regarding the effects of limited resources available for recovery and of the usefulness of having multiple modes for execution of specific tasks.
publications-4039	article	2014	Janke, Robert and Tryby, Michael E. and Clark, Robert M.	Protecting Water Supply Critical Infrastructure: An Overview		10.1007/978-3-319-01092-2_2	Government planners have long been aware that urban water systems are vulnerable to threats and disasters, both manmade and natural, including water shortages and droughts, earthquakes, and storms with high winds and flooding. Since the attacks of September 11, 2001, government planners in the United States have been forced to also consider the vulnerability of the nationβ€™s critical infrastructure, including water systems, to terrorism. The Public Health Security and Bioterrorism Preparedness and Response Act of 2002 (U.S. Congress 2002) intensified the focus on water security research in the United States. Homeland Security Presidential Directive 7 (HSPD-7), signed on December 17, 2003, established a national policy for Federal departments and agencies to identify and prioritize critical infrastructure and to protect them from terrorist attacks. HSPD-7 established the Environmental Protection Agency (EPA) as the lead agency for the Water Sectorβ€™s critical infrastructure protection activities. Consequently the EPA developed a Homeland Security Strategy, which is regularly updated (U.S. EPA 2013). The intent of the act was to enhance national security and protect human health and the environment.
publications-4040	article	2012	Raciti, Massimiliano and Raciti, Massimiliano and Cucurull, Jordi and Cucurull, Jordi and Nadjmβ€“Tehrani, Simin and Nadjm-Tehrani, Simin	Anomaly detection in water management systems		10.1007/978-3-642-28920-0_6	Quality of drinking water has always been a matter of concern. Traditionally, water supplied by utilities is analysed by independent laboratories to guarantee its quality and suitability for the human consumption. Being part of a critical infrastructure, recently water quality has received attention from the security point of view. Real-time monitoring of water quality requires analysis of sensor data gathered at distributed locations and generation of alarms when changes in quality indicators indicate anomalies. The event detection system should produce accurate alarms, with low latency and few false positives. This chapter addresses the application of data mining techniques developed for information infrastructure security in a new setting. The hypothesis is that a clustering algorithm ADWICE that has earlier been successfully applied to n-dimensional data spaces in IP networks, can also be deployed for real-time anomaly detection in water management systems. The chapter describes the evaluation of the anomaly detection software when integrated in a SCADA system. The system manages water sensors and provides data for analysis within the Water Security initiative of the U.S. Environmental Protection Agency (EPA). Performance of the algorithm is illustrated and improvements to the collected data to deal with missing and inaccurate data are proposed.