| publications-2491 |
Peer reviewed articles |
2020 |
Nikolaos Mellios, S. Moe, Chrysi Laspidou |
Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes |
Water |
10.3390/w12041191 |
Hydrological modeling |
Precipitation & Ecological Systems |
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Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms; random forest clearly outperforms the other methods with very promising results. |
734409 |
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| publications-2492 |
Peer reviewed articles |
2020 |
Bogler, A., A. Packman, A. Furman, A. Gross, A. Kushmaro, A. Ronen, C. Dagot, C. Hill, D. Vaizel-Ohayon, E. Morgenroth, E. Bertuzzo, G. Wells, H. Raanan Kiperwas, H. Horn, I. Negev, I. Zucker, I. Bar-Or, J. Moran-Gilad, J. L. Balcazar, K. Bibby, M. Elimelech, N. Weisbrod, O. Nir, O. Sued, O. Gillor, P. J. Alvarez, S. Crameri, S. Arnon, S. Walker, S. Yaron, T. H. Nguyen, Y. Berchenko, Y. Hu, Z. Ro |
Rethinking wastewater risks and monitoring in light of the COVID-19 pandemic |
Nature sustainability |
10.1038/s41893-020-00605-2 |
Data Management & Analytics |
Uncategorized |
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No abstract available |
776816 |
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| publications-2493 |
Peer reviewed articles |
2021 |
Yuejun Yu; Andraž Šuligoja; Zac hShidlovsky; Dina Shachar; Sima Yaron; Yaron Paz |
Towards on-demand photocatalysis: controlling the operation of a photocatalytic reactor based on real-time, automatic monitoring of toxicity towards the working bacteria of a proceeding bioreactor |
Chemical Engineering J |
10.1016/j.cej.2021.133621 |
Hydrological modeling |
Irrigation Systems |
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No abstract available |
776816 |
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| publications-2494 |
Peer reviewed articles |
2021 |
TeresaFidélis, Andreia Saavedra Cardoso, Fayaz Riazi, Ana Catarina Miranda, João Abrantes, FilipeTeles, Peter C. Roebeling |
Policy narratives of circular economy in the EU – Assessing the embeddedness of water and land in national action plans |
Journal of Cleaner Production |
10.1016/j.jclepro.2020.125685 |
Hydrological modeling |
Irrigation Systems |
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No abstract available |
776816 |
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| publications-2495 |
Peer reviewed articles |
2020 |
Klemen Kenda, Jože Peternelj, Nikos Mellios, Dimitris Kofinas, Matej Čerin, Jože Rožanec |
Usage of statistical modeling techniques in surface and groundwater level prediction |
Journal of Water Supply: Research and Technology-Aqua |
10.2166/aqua.2020.143 |
Data Management & Analytics |
Irrigation Systems |
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Abstract The paper presents a thorough evaluation of the performance of different statistical modeling techniques in ground- and surface-level prediction scenarios as well as some aspects of the application of data-driven modeling in practice (feature generation, feature selection, heterogeneous data fusion, hyperparameter tuning, and model evaluation). Twenty-one different regression and classification techniques were tested. The results reveal that batch regression techniques are superior to incremental techniques in terms of accuracy and that among them gradient boosting, random forest and linear regression perform best. On the other hand, introduced incremental models are cheaper to build and update and could still yield good enough results for certain large-scale applications. |
734409 |
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| publications-2496 |
Peer reviewed articles |
2021 |
Gustavo Gonzalez-Granadillo, Rodrigo Diaz, Juan Caubet, Ignasi Garcia-Milà |
CLAP: A Cross-Layer Analytic Platform for the Correlation of Cyber and Physical Security Events Affecting Water Critical Infrastructures |
Journal of Cybersecurity and Privacy |
10.3390/jcp1020020 |
Data Management & Analytics |
Irrigation Systems |
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Water CIs are exposed to a wide number of IT challenges that go from the cooperation and alignment between physical and cyber security teams to the proliferation of new vulnerabilities and complex cyber-attacks with potential disastrous consequences. Although novel and powerful solutions are proposed in the literature, most of them lack appropriate mechanisms to detect cyber and physical attacks in real time. We propose a Cross-Layer Analytic Platform (denoted as CLAP) developed for the correlation of Cyber and Physical security events affecting water CIs. CLAP aims to improve the detection of complex attack scenarios in real time based on the correlation of cyber and physical security events. The platform assigns appropriate severity values to each correlated alarm that will guide security analysts in the decision-making process of prioritizing mitigation actions. A series of passive and active attack scenarios against the target infrastructure are presented at the end of the paper to show the mechanisms used for the detection and correlation of cyber–physical security events. Results show promising benefits in the improvement of response accuracy, false rates reduction and real-time detection of complex attacks based on cross-correlation rules. |
740610 |
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| publications-2497 |
Peer reviewed articles |
2021 |
Bramha Dutt Vishwakarma, Jinwei Zhang, Nico Sneeuw |
Downscaling GRACE total water storage change using partial least squares regression |
Scientific Data |
10.1038/s41597-021-00862-6 |
Control Systems |
Irrigation Systems |
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AbstractThe Gravity Recovery And Climate Experiment (GRACE) satellite mission recorded temporal variations in the Earth’s gravity field, which are then converted to Total Water Storage Change (TWSC) fields representing an anomaly in the water mass stored in all three physical states, on and below the surface of the Earth. GRACE provided a first global observational record of water mass redistribution at spatial scales greater than 63000 km2. This limits their usability in regional hydrological applications. In this study, we implement a statistical downscaling approach that assimilates 0.5° × 0.5° water storage fields from the WaterGAP hydrology model (WGHM), precipitation fields from 3 models, evapotranspiration and runoff from 2 models, with GRACE data to obtain TWSC at a 0.5° × 0.5° grid. The downscaled product exploits dominant common statistical modes between all the hydrological datasets to improve the spatial resolution of GRACE. We also provide open access to scripts that researchers can use to produce downscaled TWSC fields with input observations and models of their own choice. |
841407 |
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| publications-2498 |
Peer reviewed articles |
2021 |
B D Vishwakarma, P Bates, N Sneeuw, R M Westaway, J L Bamber |
Re-assessing global water storage trends from GRACE time series |
Environmental Research Letters |
10.1088/1748-9326/abd4a9 |
Simulation & Modeling |
Irrigation Systems |
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Abstract Monitoring changes in freshwater availability is critical for human society and sustainable economic development. To identify regions experiencing secular change in their water resources, many studies compute linear trends in the total water storage (TWS) anomaly derived from the Gravity Recovery and Climate Experiment (GRACE) mission data. Such analyses suggest that several major water systems are under stress (Rodell et al 2009 Nature 460 999–1002; Long et al 2013 Geophys. Res. Lett. 40 3395–401; Richey et al 2015 Water Resour. Res. 51 5217–38; Voss et al 2013 Water Resour. Res. 49 904–14; Famiglietti 2014 Nat. Clim. Change. 4 945–8; Rodell et al 2018 Nature 557 651–9). TWS varies in space and time due to low frequency natural variability, anthropogenic intervention, and climate-change (Hamlington et al 2017 Sci. Rep. 7 995; Nerem et al 2018 Proc. Natl Acad. Sci.). Therefore, linear trends from a short time series can only be interpreted in a meaningful way after accounting for natural spatiotemporal variability in TWS (Paolo et al 2015 Science 348 327–31; Edward 2012 Geophys. Res. Lett. 39 L01702). In this study, we first show that GRACE TWS trends from a short time series cannot determine conclusively if an observed change is unprecedented or severe. To address this limitation, we develop a novel metric, trend to variability ratio (TVR), that assesses the severity of TWS trends observed by GRACE from 2003 to 2015 relative to the multi-decadal climate-driven variability. We demonstrate that the TVR combined with the trend provides a more informative and complete assessment of water storage change. We show that similar trends imply markedly different severity of TWS change, depending on location. Currently more than 3.2 billion people are living in regions facing severe water storage depletion w.r.t. past decades. Furthermore, nearly 36% of hydrological catchments losing water in the last decade have suffered from unprecedented loss. Inferences from this study can better inform water resource management. |
841407 |
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| publications-2499 |
Peer reviewed articles |
2020 |
B. D. Vishwakarma, S. Royston, R. E. M. Riva, R. M. Westaway, J. L. Bamber |
Sea Level Budgets Should Account for Ocean Bottom Deformation |
Geophysical Research Letters |
10.1029/2019gl086492 |
Data Management & Analytics |
Irrigation Systems |
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AbstractThe conventional sea level budget (SLB) equates changes in sea surface height with the sum of ocean mass and steric change, where solid‐Earth movements are included as corrections but limited to the impact of glacial isostatic adjustment. However, changes in ocean mass load also deform the ocean bottom elastically. Until the early 2000s, ocean mass change was relatively small, translating into negligible elastic ocean bottom deformation (OBD), hence neglected in the SLB equation. However, recently ocean mass has increased rapidly; hence, OBD is no longer negligible and likely of similar magnitude to the deep steric sea level contribution. Here, we use a mass‐volume framework, which allows the ocean bottom to respond to mass load, to derive a SLB equation that includes OBD. We discuss the theoretical appearance of OBD in the SLB equation and its implications for the global SLB. |
841407 |
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| publications-2500 |
Peer reviewed articles |
2020 |
Georgios Moraitis, Dionysios Nikolopoulos, Dimitrios Bouziotas, Archontia Lykou, George Karavokiros, Christos Makropoulos |
Quantifying Failure for Critical Water Infrastructures under Cyber-Physical Threats |
Journal of Environmental Engineering |
10.1061/(asce)ee.1943-7870.0001765 |
Data Management & Analytics |
Precipitation & Ecological Systems |
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No abstract available |
740610 |
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