A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting – A case study in the Awash River Basin (Ethiopia). (October 2021)

• Record Type: Journal Article
• Authors: Quilty, John; Adamowski, Jan
• Is Part Of: Environmental modelling & software. Volume 144(2021)
• Publication Date: 2021-10
• Subjects: Hydrological forecasting -- Wavelets -- Maximal overlap discrete wavelet packet transform -- Data-driven models -- Input variable selection; 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.2021.105119
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study introduces the maximal overlap discrete wavelet packet transform (MODWPT) for forecasting hydrological variables that exhibit change over multiple timescales (e.g., rainfall, streamflow). The advantage of the MODWPT over other recent wavelet decomposition methods ( à trous algorithm (AT) and the maximal overlap discrete wavelet transform (MODWT)) is that it can extract finer scale information that may be important for improving forecast performance. Multiple wavelet decomposition methods (MODWPT, AT, MODWT) are integrated within the Wavelet Data-Driven Forecasting Framework (WDDFF), applied for forecasting monthly rainfall at six meteorological stations in the Awash River Basin (Ethiopia), and compared using eight statistical performance metrics. Results demonstrate that the MODWPT can be used to generate more accurate forecasts than the AT and MODWT for the majority of stations and performance metrics. Certain settings within the WDDFF (decomposition level, wavelet filter, input variable method, and data-driven model) lead to improved performance more often than others. Highlights: MODWPT explored for the first time in hydrological forecasting. MODWPT integrated with Wavelet Data-Driven Forecasting Framework. AT, MODWT, and MODWPT compared using eight performance metrics. MODWPT outperforms AT and MODWT for majority of metrics. 'Best' model depends on adopted performance metric.