Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment

Abstract

<jats:p>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.</jats:p>

Hybrid Model for Time Series of Complex Structure with ARIMA Components

“…Traditional methods for data time series modeling and analysis (AR models, ARMA [20,21], exponential smoothing [22], stochastic approximation [13], etc.) do not allow us to describe the time series of complex structure adequately [23]. At present, hybrid approaches [16,17,19,[23][24][25][26][27][28] are widely applied.…”

“…do not allow us to describe the time series of complex structure adequately [23]. At present, hybrid approaches [16,17,19,[23][24][25][26][27][28] are widely applied. They make it possible to improve the efficiency of the procedure of data analysis in case of its complicated structure.…”

“…The 2D empirical wavelet filters developed by the authors of [30] are effective in image processing applications. Currently, neural network methods are also widely used [4,15,23,31]. They allow us to approximate complex nonlinear relationships in data and are easily automated.…”

“…For example, the authors of the paper [31] proposed a neural network structure, based on the LSTM paradigm, which allowed them to obtain an accurate forecast of time series for web traffic on a limited data set. The authors of the paper [23] considered combinations of wavelet transform with neural networks to analyze hydrological data.…”

Assessing Neural Network Approaches for Solar Radiation Estimates Using Limited Climatic Data in the Mediterranean Sea

“…In order to address this problem, several methods have been developed to estimate solar radiation: i) methods based on empirical relationships of different available meteorological parameters such as sunshine duration, air temperature, relative humidity, extraterrestrial radiation, cloud cover, among others [4][5][6][7][8][9][10][11], ii) estimations using data from nearby stations [7,12,13], iii) using satellite-based methods [14][15][16][17][18][19], iv) using Machine Learning (ML) models [16,[20][21][22][23], v) and others [24,25]. ML models efficiently extract high dimensional and complex features from the different inputs in order to map them to obtain an output [26]; this is the reason why ML models have become one of the most commonly used methodologies to estimate solar radiation and other hydrometeorological parameters [27][28][29][30]. In this term, studied the capability of Support Vector Regression (SVR) was studied for a weather station in Iran [31], showing a better performance than the empirical models and the PSO-based model tested.…”