Abstract
Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied.
Highlights
Precipitation, besides being one of the most important variables in hydrological models, is crucial in sectors such as agriculture, tourism or even in the energy sector [1], where the absence of water can lead to the closure of nuclear plants, such as the recent case in July 2019 in France
Apart from max/min monthly temperature records (Tx and Tn, respectively), various temperature-based monthly time series were created from daily values: mean daily temperature range (DTRm ), maximum daily temperature range (DTRx ), minimum daily temperature range (DTRn ) and monthly temperature range (MTR)
Conclusions such as DTRx, DTRn, DTRm, MTR or Month of year (MOY) into wavelet neural network models (WNN) models is very useful for improving precipitation predictions one configurations month ahead, especially when therecombining is no availability of long-term datasets
Summary
Precipitation, besides being one of the most important variables in hydrological models (infiltration, soil loss, droughts, overland flow production, floods, etc.), is crucial in sectors such as agriculture, tourism or even in the energy sector [1], where the absence of water can lead to the closure of nuclear plants, such as the recent case in July 2019 in France. Because of the large number of interconnected variables that are involved in the physical modelling of precipitation, forecasting rainfall is exceptionally complicated [2]. Due to the nonlinear and dynamic characteristics of precipitation, methods like numerical weather prediction (NWP) models or even statistical models still have difficulties to provide satisfactory precipitation forecasts [3]. This is mainly due to the fact that they are subject to many uncertainties [4,5,6,7,8,9]
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