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

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 (`a 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.

Automated summary

This study introduces the maximal overlap discrete wavelet packet transform (MODWPT) for forecasting hydrological variables, which can extract finer scale information and generate more accurate forecasts compared to other wavelet decomposition methods. Results demonstrate that the MODWPT can be used to generate more accurate forecasts than the AT and MODWT for the majority of stations, and certain settings within the WDDFF lead to improved performance more often.