A user-driven case-based reasoning tool for infilling missing values in daily mean river flow records. (August 2016)

• Record Type: Journal Article
• Authors: Giustarini, Laura; Parisot, Olivier; Ghoniem, Mohammad; Hostache, Renaud; Trebs, Ivonne; Otjacques, Benoît
• Is Part Of: Environmental modelling & software. Volume 82(2016:Aug.)
• Page Start: 308
• Page End: 320
• Publication Date: 2016-08
• Subjects: Gap filling -- Hydrological time series -- Case-based reasoning -- Knowledge database -- Human in the loop; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.04.013
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Missing data in river flow records represent a loss of information and a serious drawback in water management. In this work, we introduce gapIt, a user-driven case-based reasoning tool for infilling gaps in daily mean river flow records. Given a set of flow time series, gapIt builds a database of artificial gaps for which it computes several flow estimates, to find the best combinations of infilling algorithm and automatically selected donor station(s), according to state-of-the-art performance indicators. We obtained satisfactory results with Nash-Sutcliffe >0.7 for more than half of the ∼5000 synthetic gaps of various lengths and positions, randomly created along the available records. gapIt was evaluated on 24 daily river discharge time series recorded in Luxembourg over seven years from 01/01/2007 to 31/12/2013. We also discuss the benefits of coupling this approach with user-expertise for an improved infilling of real data gaps. Highlights: We propose a user-driven case-based reasoning tool to infill gaps in flow records. With data analytics the tool finds the best combination: algorithm – donor stations. Satisfactory results were obtained on synthetic gaps of different characteristics. The tool bridges a gap between data-driven and user-expertise approaches.