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  1. Article: Undersampling in action and at scale: application to the COVID-19 pandemic.

    Langousis, Andreas / Carsteanu, Alin Andrei

    Stochastic environmental research and risk assessment : research journal

    2020  Volume 34, Issue 8, Page(s) 1281–1283

    Abstract: It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of ... ...

    Abstract It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of undersampling is proposed, based on re-scaling of statistics that can be considered reliable, such as deaths, and epidemic properties like mortality, that may be considered comparable between countries with similar levels of health care, which would not have reached a saturation level.
    Keywords covid19
    Language English
    Publishing date 2020-06-02
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-020-01821-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Undersampling in action and at scale: application to the COVID-19 pandemic

    Langousis, Andreas / Carsteanu, Alin Andrei

    Stochastic environmental research and risk assessment. 2020 Aug., v. 34, no. 8

    2020  

    Abstract: It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of ... ...

    Abstract It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of undersampling is proposed, based on re-scaling of statistics that can be considered reliable, such as deaths, and epidemic properties like mortality, that may be considered comparable between countries with similar levels of health care, which would not have reached a saturation level.
    Keywords COVID-19 infection ; Orthocoronavirinae ; decision making ; health services ; mortality ; pandemic ; statistics
    Language English
    Dates of publication 2020-08
    Size p. 1281-1283.
    Publishing place Springer Berlin Heidelberg
    Document type Article
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-020-01821-0
    Database NAL-Catalogue (AGRICOLA)

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  3. Article: Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

    Papacharalampous, Georgia / Langousis, Andreas

    Water resources research. 2022 June, v. 58, no. 6

    2022  

    Abstract: Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression ... ...

    Abstract Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we fill this gap, thereby proposing a new family of probabilistic urban water demand forecasting algorithms. We further extensively compare seven algorithms from this family in practical one‐day ahead urban water demand forecasting settings. More precisely, we compare five individual quantile regression algorithms (i.e., the quantile regression, linear boosting, generalized random forest, gradient boosting machine and quantile regression neural network algorithms), their mean combiner and their median combiner. The comparison is conducted by exploiting a large urban water flow data set, as well as several types of hydrometeorological time series (which are considered as exogenous predictor variables in the forecasting setting). The results mostly favor the linear boosting algorithm, probably due to the presence of shifts (and perhaps trends) in the urban water flow time series. The forecasts of the mean and median combiners are also found to be skillful.
    Keywords algorithms ; assets ; data collection ; hydrometeorology ; new family ; regression analysis ; research ; time series analysis ; water ; water flow
    Language English
    Dates of publication 2022-06
    Publishing place John Wiley & Sons, Ltd
    Document type Article
    Note JOURNAL ARTICLE
    ZDB-ID 5564-5
    ISSN 1944-7973 ; 0043-1397
    ISSN (online) 1944-7973
    ISSN 0043-1397
    DOI 10.1029/2021WR030216
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

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  4. Article ; Online: Towards More Efficient Hydraulic Modeling of Water Distribution Networks Using the EPANET Software Engine

    Athanasios V. Serafeim / Anastasios Perdios / Nikolaos Th. Fourniotis / Andreas Langousis

    Environmental Sciences Proceedings, Vol 25, Iss 46, p

    2023  Volume 46

    Abstract: Hydraulic modeling of water distribution networks (WDNs) is a vital step for all water-related professionals towards the development of management practices and strategies that aim for the reduction of water losses and the associated financial cost and ... ...

    Abstract Hydraulic modeling of water distribution networks (WDNs) is a vital step for all water-related professionals towards the development of management practices and strategies that aim for the reduction of water losses and the associated financial cost and environmental footprint. In the current work, we develop an easy-to-implement methodology for the effective modeling of WDNs, which seeks to minimize the computational load without undermining the analysis’s accuracy, using the open access EPANET (Environmental Protection Agency Network Evaluation Tool) software package. The effectiveness of the proposed methodology is tested via a large-scale, real-world application for the city of Patras.
    Keywords hydraulic modeling ; hydraulic network ; EPANET ; computational nodes ; junctions ; sensitivity analysis ; Environmental sciences ; GE1-350
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Probabilistic water demand forecasting using quantile regression algorithms

    Papacharalampous, Georgia / Langousis, Andreas

    2021  

    Abstract: Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression ... ...

    Abstract Machine and statistical learning algorithms can be reliably automated and applied at scale. Therefore, they can constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression algorithms are statistical and machine learning algorithms that can provide probabilistic forecasts in a straightforward way, and have not been applied so far for urban water demand forecasting. In this work, we aim to fill this gap by automating and extensively comparing several quantile-regression-based practical systems for probabilistic one-day ahead urban water demand forecasting. For designing the practical systems, we use five individual algorithms (i.e., the quantile regression, linear boosting, generalized random forest, gradient boosting machine and quantile regression neural network algorithms), their mean combiner and their median combiner. The comparison is conducted by exploiting a large urban water flow dataset, as well as several types of hydrometeorological time series (which are considered as exogenous predictor variables in the forecasting setting). The results mostly favour the practical systems designed using the linear boosting algorithm, probably due to the presence of trends in the urban water flow time series. The forecasts of the mean and median combiners are also found to be skilful in general terms.
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning ; Statistics - Methodology
    Subject code 310
    Publishing date 2021-04-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Undersampling in action and at scale

    Langousis, Andreas / Carsteanu, Alin Andrei

    Stochastic Environmental Research and Risk Assessment

    application to the COVID-19 pandemic

    2020  Volume 34, Issue 8, Page(s) 1281–1283

    Keywords Environmental Engineering ; General Environmental Science ; Safety, Risk, Reliability and Quality ; Water Science and Technology ; Environmental Chemistry ; covid19
    Language English
    Publisher Springer Science and Business Media LLC
    Publishing country us
    Document type Article ; Online
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-020-01821-0
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  7. Article: Undersampling in action and at scale: application to the COVID-19 pandemic

    Langousis, Andreas / Carsteanu, Alin Andrei

    Stoch Environ Res Risk Assess

    Abstract: It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of ... ...

    Abstract It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of undersampling is proposed, based on re-scaling of statistics that can be considered reliable, such as deaths, and epidemic properties like mortality, that may be considered comparable between countries with similar levels of health care, which would not have reached a saturation level.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #459006
    Database COVID19

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  8. Article ; Online: The Spatiotemporal Evolution of Rainfall Extremes in a Changing Climate

    Stergios Emmanouil / Andreas Langousis / Efthymios I. Nikolopoulos / Emmanouil N. Anagnostou

    Earth's Future, Vol 10, Iss 3, Pp n/a-n/a (2022)

    A CONUS‐Wide Assessment Based on Multifractal Scaling Arguments

    2022  

    Abstract: Abstract Given the rapidly changing climate, accurate spatiotemporal information on the evolution of extreme rainfall events is required for flood risk assessment and the design of resilient infrastructure. Consequently, various research efforts have ... ...

    Abstract Abstract Given the rapidly changing climate, accurate spatiotemporal information on the evolution of extreme rainfall events is required for flood risk assessment and the design of resilient infrastructure. Consequently, various research efforts have focused on investigating the appropriateness of various parametric and non‐parametric approaches in modeling the observed changes in the frequency of extreme rainfall over time. Yet, the assumption of stationarity, or the change of model parameters when accounting for nonstationary rainfall, may magnify estimation uncertainty of rain rates associated with low exceedance probabilities. Moreover, the use of climate model results may yield inconclusive outcomes, given the existence of epistemic uncertainties in the frequency of extreme events developing on smaller spatial scales or over complex terrain. Herein, we employ a parametric approach based on multifractal scaling arguments, along with high‐resolution (4‐km) hourly precipitation estimates covering a 40‐year period over CONUS, to derive Intensity‐Duration‐Frequency curves and investigate the spatiotemporal evolution of extreme rainfall over a wide range of characteristic temporal scales and exceedance probability levels. Considering the robustness of the multifractal models even when fitted to short rainfall records, we uniquely apply the framework to sequential 10‐year segments of data, where the rainfall process can be reasonably assumed stationary. The obtained results reveal that existing infrastructure may be severely impacted by the intensification of precipitation extremes due to climate change, with the observed trends being significantly influenced by the topography and rainfall climatology of each region, while depending on the averaging durations and return periods of interest.
    Keywords climate change ; extreme rainfall ; multifractals ; intensity‐duration‐frequency curves ; rainfall intensification ; Environmental sciences ; GE1-350 ; Ecology ; QH540-549.5
    Subject code 910
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article: Revisiting the Statistical Scaling of Annual Discharge Maxima at Daily Resolution with Respect to the Basin Size in the Light of Rainfall Climatology

    Perdios, Anastasios / Langousis, Andreas

    Water. 2020 Feb. 24, v. 12, no. 2

    2020  

    Abstract: Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), ... ...

    Abstract Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), especially in cases where the data originated from regions with significantly different hydroclimatic characteristics. In this context, an important question arises on how one can effectively conclude on an approximate type of statistical scaling of annual discharge maxima with respect to the basin size. The present study aims at addressing this question, using daily discharges from 805 catchments located in different parts of the United Kingdom, with at least 30 years of recordings. To do so, we isolate the effects of the catchment area and the local rainfall climatology, and examine how the statistics of the standardized discharge maxima vary with the basin scale. The obtained results show that: (a) the local rainfall climatology is an important contributor to the observed statistics of peak annual discharges, and (b) when the effects of the local rainfall climatology are properly isolated, the scaling of the standardized annual discharge maxima with the area of the catchment closely follows that commonly met in actual rainfields, deviating significantly from the simple scaling rule. The aforementioned findings explain to a large extent the diverse results obtained by previous studies in the absence of rainfall information, shedding light on the approximate type of scaling of annual discharge maxima with the basin size.
    Keywords area ; basins ; climatology ; information ; light ; rain ; statistics ; water ; watersheds ; United Kingdom
    Language English
    Dates of publication 2020-0224
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    Note NAL-light
    ZDB-ID 2521238-2
    ISSN 2073-4441
    ISSN 2073-4441
    DOI 10.3390/w12020610
    Database NAL-Catalogue (AGRICOLA)

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  10. Article: A critical analysis of the shortcomings in spatial frequency analysis of rainfall extremes based on homogeneous regions and a comparison with a hierarchical boundaryless approach

    Deidda, Roberto / Hellies, Matteo / Langousis, Andreas

    Stochastic environmental research and risk assessment. 2021 Dec., v. 35, no. 12

    2021  

    Abstract: We investigate and discuss limitations of the approach based on homogeneous regions (hereafter referred to as regional approach) in describing the frequency distribution of annual rainfall maxima in space, and compare its performance with that of a ... ...

    Abstract We investigate and discuss limitations of the approach based on homogeneous regions (hereafter referred to as regional approach) in describing the frequency distribution of annual rainfall maxima in space, and compare its performance with that of a boundaryless approach. The latter is based on geostatistical interpolation of the at-site estimates of all distribution parameters, using kriging for uncertain data. Both approaches are implemented using a generalized extreme value theoretical distribution model to describe the frequency of annual rainfall maxima at a daily resolution, obtained from a network of 256 raingauges in Sardinia (Italy) with more than 30 years of complete recordings, and approximate density of 1 gauge per 100 km². We show that the regional approach exhibits limitations in describing local precipitation features, especially in areas characterized by complex terrain, where sharp changes to the shape and scale parameters of the fitted distribution models may occur. We also emphasize limitations and possible ambiguities arising when inferring the distribution of annual rainfall maxima at locations close to the interface of contiguous homogeneous regions. Through implementation of a leave-one-out cross-validation procedure, we evaluate and compare the performances of the regional and boundaryless approaches miming ungauged conditions, clearly showing the superiority of the boundaryless approach in describing local precipitation features, while avoiding abrupt changes of distribution parameters and associated precipitation estimates, induced by splitting the study area into contiguous homogeneous regions.
    Keywords frequency distribution ; geostatistics ; kriging ; landscapes ; models ; rain ; research ; risk assessment ; Italy ; Sardinia
    Language English
    Dates of publication 2021-12
    Size p. 2605-2628.
    Publishing place Springer Berlin Heidelberg
    Document type Article
    ZDB-ID 1481263-0
    ISSN 1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
    ISSN (online) 1436-3259 ; 1435-151X
    ISSN 1436-3240 ; 0931-1955
    DOI 10.1007/s00477-021-02008-x
    Database NAL-Catalogue (AGRICOLA)

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