A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes. (August 2020)

• Record Type: Journal Article
• Authors: Quilty, John; Adamowski, Jan
• Is Part Of: Environmental modelling & software. Volume 130(2020)
• Publication Date: 2020-08
• Subjects: Uncertainty -- Stochastic -- Data-driven models -- Input variable selection -- Wavelet decomposition -- Forecasting; 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.2020.104718
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Recently, a stochastic data-driven framework was introduced for forecasting uncertain multiscale hydrological and water resources processes (e.g., streamflow, urban water demand (UWD)) that uses wavelet decomposition of input data to address multiscale change and stochastics to account for input variable selection, parameter, and model output uncertainty (Quilty et al., 2019). The former study considered all sources of uncertainty together. In contrast, this study explores how input variable selection uncertainty and wavelet decomposition impact probabilistic forecasting performance by considering eight variations of this framework that either include/ignore wavelet decomposition and varying levels of uncertainty: 1) none; 2) parameter; 3) parameter and model output; and 4) input variable selection, parameter, and model output. For a daily UWD forecasting case study in Montreal (Canada), substantial improvements in forecasting performance (e.g., 16–30% improvement in the mean interval score) was achieved when input variable selection uncertainty and wavelet decomposition were included within the framework. Highlights: Two stochastic data-driven forecasting frameworks are explored. One method uses wavelet decomposition of model inputs to account for multiscale change. Both consider input variable selection, parameter, and model output uncertainty. Input variable selection uncertainty and wavelet decomposition of model inputs increase forecast accuracy and reliability.