Estimating Missing and False Data in Flow Meters of a Water Distribution Network

Quevedo, Joseba; Puig, Vicenç; Cembraño, Gabriela; Aguilar, José; Isaza, Claudia; Saporta, D.; Benito, Gabriel R. G.; Hedo, M.; Molina, Ana Lucía

doi:10.1016/b978-008044485-7/50199-8

Cited by 5 publications

(3 citation statements)

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“…Therefore, the operation of a DWN is strongly conditioned by the uncertain water demand, which follows a non-stationary dynamics (see Quevedo et al [2006]). In this paper, three well-established time series modelling methodologies are employed to capture the dynamics of water demand, namely a Seasonal Auto-Regressive Integrated Moving-Average (sARIMA) model, a BATS model developed by De Livera et al [2011], and a Support Vector Machine model.…”

Section: Motivationmentioning

confidence: 99%

Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study.

Sampathirao

Grosso

Sopasakis

et al. 2014

IFAC Proceedings Volumes

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Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs' operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN.

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Section: Motivationmentioning

confidence: 99%

Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study.

Sampathirao

Grosso

Sopasakis

et al. 2014

IFAC Proceedings Volumes

View full text Add to dashboard Cite

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“…The problem of missing data arises in several research areas, such as chemometrics [1], genomics [2], network inference [3], meteorology [4], engineering [5], informatics [6], and chemical [7], biochemical [8] and pharmaceutical [9] industries. Very often, missing data are directly disregarded, i.e.…”

Section: Introductionmentioning

confidence: 99%

Missing Data Imputation Toolbox for MATLAB

Folch-Fortuny

Arteaga

Ferrer

2016

Chemometrics and Intelligent Laboratory Systems

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Here we introduce a graphical user-friendly interface to deal with missing values called Missing Data Imputation (MDI) Toolbox. This MATLAB toolbox allows imputing missing values, following missing completely at random patterns, exploiting the relationships among variables. In this way, principal component analysis (PCA) models are fitted iteratively to impute the missing data until convergence. Different methods, using PCA internally, are included in the toolbox: trimmed scores regression (TSR), known data regression (KDR), KDR with principal component regression (KDR-PCR), KDR with partial least squares regression (KDR-PLS), projection to the model plane (PMP), iterative algorithm (IA), modified nonlinear iterative partial least squares regression algorithm (NIPALS) and data augmentation (DA). MDI Toolbox presents a general procedure to impute missing data, thus can be used to infer PCA models with missing data, to estimate the covariance structure of incomplete data matrices, or to impute the missing values as a preprocessing step of other methodologies.

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“…The problem of missing data arises in several research areas, such as chemometrics (see Chapter 10), genomics [325], network inference (see Chapter 11), meteorology [326], engineering [327], informatics [328], and chemical [47], biochemical [318] and pharmaceutical [329] industries. To help scientists across these research areas, we present here a GUI in MATLAB, called MDI Toolbox, devoted to fulfil incomplete data sets following MCAR.…”

Section: Introductionmentioning

confidence: 99%

Chemometric Approaches for Systems Biology

Fortuny¹

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The present Ph.D. thesis is devoted to study, develop and apply approaches commonly used in chemometrics to the emerging field of systems biology. Existing procedures and new methods are applied to solve research and industrial questions in different multidisciplinary teams. The methodologies developed in this document will enrich the plethora of procedures employed within omic sciences to understand biological organisms and will improve processes in biotechnological industries integrating biological knowledge at different levels and exploiting the software packages derived from the thesis.This dissertation is structured in four parts. The first block describes the framework in which the contributions presented here are based. The objectives of the two research projects related to this thesis are highlighted and the specific topics addressed in this document via conference presentations and research articles are introduced. A comprehensive description of omic sciences and their relationships within the systems biology paradigm is given in this part, jointly with a review of the most applied multivariate methods in chemometrics, on which the novel approaches proposed here are founded.The second part addresses many problems of data understanding within metabolomics, fluxomics, proteomics and genomics. Different alternatives are proposed in this block to understand flux data in steady state conditions. Some are based on applications of multivariate methods previously applied in other chemometrics areas. Others are novel approaches based on a bilinear decomposition using elemental metabolic pathways, from which a GNU licensed toolbox is made freely available for the scientific community. As well, a framework for metabolic data understanding is proposed for non-steady state data, using the same bilinear decomposition proposed for steady state data, but modelling the dynamics of the experiments using novel two and three-way data analysis procedures. Also, the relationships between different omic levels are assessed in this part integrating different sources of information of plant viruses in data fusion models. Finally, an example of interaction between organisms, oranges and fungi, is studied via multivariate image analysis techniques, with future application in food industries.v The third block of this thesis is a thoroughly study of different missing data problems related to chemometrics, systems biology and industrial bioprocesses. In the theoretical chapters of this part, new algorithms to obtain multivariate exploratory and regression models in the presence of missing data are proposed, which serve also as preprocessing steps of any other methodology used by practitioners. Regarding applications, this block explores the reconstruction of networks in omic sciences when missing and faulty measurements appear in databases, and how calibration models between near infrared instruments can be transferred, avoiding costs and time-consuming full recalibrations in bioindustries and research laboratories. Finally,...

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Estimating Missing and False Data in Flow Meters of a Water Distribution Network

Cited by 5 publications

References 4 publications

Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study.

Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona Case Study.

Missing Data Imputation Toolbox for MATLAB

Chemometric Approaches for Systems Biology

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