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Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Anca Hangan, Costin-Gabriel Chiru, Diana Arsene, Zoltan Czako, Dragos Florin Lisman, Mariana Mocanu, Bogdan Pahontu, Alexandru Predescu, Gheorghe Sebestyen
2022 Water  
Water supply systems are essential for a modern society.  ...  The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures.  ...  [99] analyze supervised machine learning methods used for anomaly detection in quality measurements collected from water distribution networks.  ... 
doi:10.3390/w14142174 fatcat:5nrq7btikbgctigl2nkvf3uelq

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Carlos Gonzales-Inca, Mikel Calle, Danny Croghan, Ali Torabi Haghighi, Hannu Marttila, Jari Silander, Petteri Alho
2022 Water  
This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic  ...  Overall, selecting a particular GeoAI method depends on the application's objective, data availability, and user expertise.  ...  Water 2022, 14, 2211 Acknowledgments: We kindly thank the Freshwater Competence Centre (Available online: www. (accessed on 21 June 2022)).  ... 
doi:10.3390/w14142211 fatcat:tjiod5qz45f67kbmkm3f6kuv7i

Monitoring influent conditions of wastewater treatment plants by nonlinear data-based techniques

Tuoyuan Cheng, Abdelkader Dairi, Fouzi Harrou, Ying Sun, Tor Ove Leiknes
2019 IEEE Access  
This research proposed kernel machine learning models, the kernel principal components analysis based one-class support vector machine (KPCA-OCSVM) with various kernels, to learn anomaly-free training  ...  Without requiring linear, Gaussian, stationary, independent, and homo-skedastic qualities from data, the proposed flexible environmental data science approach could be transferred, rebuilt, and tuned conveniently  ...  Machine learning is a remarkable multidisciplinary field, where methods could be implemented for fault detection.  ... 
doi:10.1109/access.2019.2933616 fatcat:if4wy3tlevcavbfm72psiu4654

Empirical Comparison of Approaches for Mitigating Effects of Class imbalances in Water Quality Anomaly Detection

Eustace M. Dogo, Nnamdi I. Nwulu, Bhekisipho Twala, Clinton Aigbavboa
2020 IEEE Access  
The authors are also thankful to Professor Thomas Bartz-Beielstein for providing the Thüringer Fernwasserversorgung water utility dataset used for conducting all the experiments  ...  ACKNOWLEDGMENT The authors thank the University of Johannesburg and the Durban University of Technology for making the resources available to complete this work.  ...  Multi-objective machine learning for feature selection on support vector machine and ensemble generation on decision trees is proposed in [17] to solve online anomaly detection of drinking-water quality  ... 
doi:10.1109/access.2020.3038658 fatcat:4wh5puoqczf4rnqyogj3t3mxsq

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Melissa R. Allen-Dumas, Haowen Xu, Kuldeep R. Kurte, Deeksha Rastogi
2021 Frontiers in Water  
We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.  ...  Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic  ...  Cao and Gu, 2002; Mohanty and Mohapatra, 2018 Anomaly detection Various anomaly detection algorithms have been proposed for detecting point anomalies to improve hydrological and climate data quality,  ... 
doi:10.3389/frwa.2020.562304 fatcat:4g4x5qsljva63fzfibqyjhsdsi

Water Management in Agriculture: a Survey on Current Challenges and Technological Solutions

Abdelmadjid Saad, Abou El Hassan Benyamina, Abdoulaye Gamatie
2020 IEEE Access  
There are plenty of challenges in agriculture involving water: water pollution monitoring, water reuse, monitoring water pipeline distribution network for irrigation, drinking water for livestock, etc.  ...  By focusing on the water management challenge in general, existing approaches are aiming at optimizing water usage, and improving the quality and quantity of agricultural crops, while minimizing the need  ...  The main contribution of the authors concerns prediction, by applying a machine-learning algorithm to predict water quality via the used cloud server.  ... 
doi:10.1109/access.2020.2974977 fatcat:f5irc76rs5bb5i3mdpvuh2kwhy

Digital water developments and lessons learned from automation in the car and aircraft industries

Dragan Savić
2021 Engineering  
Effective utilization of remotely sensed weather and soil moisture data for more efficient irrigation (i.e., for food production), better detection of anomalies and faults in pipe networks using artificial  ...  The provision of water and sanitation services is a key challenge worldwide.  ...  Machine learning for anomaly detection in water distribution systems Reducing non-revenue water-that is, water that has been abstracted, treated, and pumped into distribution, but not delivered and billed  ... 
doi:10.1016/j.eng.2021.05.013 fatcat:yqv62tqh7zcnzldppt5dl4vf2i

Cloud-based Event Detection Platform for Water Distribution Networks Using Machine-learning Algorithms

Christian Kühnert, Marc Baruthio, Thomas Bernard, Claude Steinmetz, Jean-Marc Weber
2015 Procedia Engineering  
Modern water distribution networks are equipped with a large amount of sensors to monitor the drinking water quality.  ...  To overcome this challenge, this paper proposes a cloud-based event-detection and reporting platform, which provides a possibility to use machine learning algorithms.  ...  Introduction Over the past years, numerous multi-parameter sensors have been placed in Water Distribution Networks (WDN) to monitor the water quality and to raise events if, e.g. contaminants are detected  ... 
doi:10.1016/j.proeng.2015.08.963 fatcat:6yt3l7fkgrcqtcr3fjz7builai

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters

2018 Sensors  
In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively  ...  The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.  ...  Acknowledgments: This work was funded by the National Natural Science Foundation of China (No. 61573313) "Online water-quality anomaly detection, classification, and identification based on multi-source  ... 
doi:10.3390/s18040938 pmid:29565295 pmcid:PMC5948656 fatcat:wasafhwlzbfytcpz2koaim7vlq

[ICNSC 2020 Front Matter]

2020 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)  
Recently ANN influences us by the deep learning structure. We intend to talk about our research of ANN to solve problems.  ...  The double-layered ANNs consist of Hopfield machine and Boltzmann machine.  ...  Recently, researchers tried to apply the deep learning methods (e.g. RNN, CNN) for WDS anomaly detection but the results are worse than that of the traditional machine learning methods.  ... 
doi:10.1109/icnsc48988.2020.9311391 fatcat:e7vdat475ncofjhzmdwwpuy23y

Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality

Jungsu Park, Keug Tae Kim, Woo Hyoung Lee
2020 Water  
Although advanced sensing technologies, such as hyperspectral images (HSI), have been used for the areal monitoring of algal bloom, other water quality sensors for organic compounds, phosphorus (P), and  ...  Water quality control and management in water resources are important for providing clean and safe water to the public.  ...  Muharemi et al. (2019) used various machine learning models (e.g., ANNs, SVMs, and LSTMs) for the analysis of time series data and suggested machine learning for the detection of anomalies in the water  ... 
doi:10.3390/w12020510 fatcat:tz3soqantndztmaq7fwafo45ty

Human Activity Recognition in Smart-Home Environments for Health-Care Applications

Gabriele Civitarese
2019 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)  
Regarding unobtrusive sensing, we propose a machine learning technique to detect fine-grained manipulations performed by the inhabitant on household objects instrumented with tiny accelerometer sensors  ...  Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors is presented.  ...  For instance, a medicine can be taken with or without drinking water.  ... 
doi:10.1109/percomw.2019.8730719 dblp:conf/percom/Civitarese19 fatcat:oxhsok7q5fdwrdx7lmu4v3wgfa

Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

Antonio Candelieri
2017 Water  
forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks.  ...  This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different  ...  Figure 3 . 3 A schematic representation of the second stage of the approach: learning a pool of Support Vector Machine (SVM)-based regression models for each cluster to predict hourly water demand.  ... 
doi:10.3390/w9030224 fatcat:b6u3us3r7jaofcpmtak2hm747i

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Nicholas J. Watson, Alexander L. Bowler, Ahmed Rady, Oliver J. Fisher, Alessandro Simeone, Alessandro Simeone, Josep Escrig, Elliot Woolley, Akinbode A. Adedeji
2021 Frontiers in Sustainable Food Systems  
In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink  ...  The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector.  ...  If only a small number of samples is expected in one class (e.g., rejections based on a rare quality defect), an anomaly detection model may be more suitable.  ... 
doi:10.3389/fsufs.2021.642786 doaj:d6792e3117824d9c8d6b7db5b025025a fatcat:lypuxbtmcrcgdilr44k4idtpqi

IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications

Farmanullah Jan, Nasro Min-Allah, Dilek Düştegör
2021 Water  
Water quality monitoring is thus paramount, especially for domestic water. Traditionally used laboratory-based testing approaches are manual, costly, time consuming, and lack real-time feedback.  ...  In brief, this study probes into common water-quality monitoring (WQM) parameters, their safe-limits for drinking water, related smart sensors, critical review, and ratification of contemporary IoT-WQMS  ...  Acknowledgments: Authors are thankful to the Deanship of Scientific Research (DSR), Imam Abdulrahman Bin Faisal University (IAU) for the generous funding under project numbered: 2019-381-CSIT.  ... 
doi:10.3390/w13131729 fatcat:mmqxdwkaj5axpa6ui466mauxeq
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