Neural networks adaptive wavelets for predictions of the northeastern Brazil monthly rainfall anomalies time series

1996 ◽  
Author(s):  
Weigang Li ◽  
Leonardo D. Sa ◽  
G. S. Prasad ◽  
A. G. Nowosad ◽  
Mauricio Bolzan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Monthly Rainfall ◽  
Northeastern Brazil ◽  
Adaptive Wavelets ◽  
Rainfall Anomalies

scholarly journals Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2628 ◽  
Author(s):  
Teresita Canchala ◽  
Wilfredo Alfonso-Morales ◽  
Yesid Carvajal-Escobar ◽  
Wilmar L. Cerón ◽  
Eduardo Caicedo-Bravo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Monthly Rainfall ◽  
Climate Indices ◽  
Rainfall Forecasting ◽  
Exogenous Variables ◽  
Rainfall Anomalies ◽  
Artificial Neural

Improving the accuracy of rainfall forecasting is relevant for adequate water resources planning and management. This research project evaluated the performance of the combination of three Artificial Neural Networks (ANN) approaches in the forecasting of the monthly rainfall anomalies for Southwestern Colombia. For this purpose, we applied the Non-linear Principal Component Analysis (NLPCA) approach to get the main modes, a Neural Network Autoregressive Moving Average with eXogenous variables (NNARMAX) as a model, and an Inverse NLPCA approach for reconstructing the monthly rainfall anomalies forecasting in the Andean Region (AR) and the Pacific Region (PR) of Southwestern Colombia, respectively. For the model, we used monthly rainfall lagged values of the eight large-scale climate indices linked to the El Niño Southern Oscillation (ENSO) phenomenon as exogenous variables. They were cross-correlated with the main modes of the rainfall variability of AR and PR obtained using NLPCA. Subsequently, both NNARMAX models were trained from 1983 to 2014 and tested for two years (2015–2016). Finally, the reconstructed outputs from the NNARMAX models were used as inputs for the Inverse NLPCA approach. The performance of the ANN approaches was measured using three different performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation (r). The results showed suitable forecasting performance for AR and PR, and the combination of these ANN approaches demonstrated the possibility of rainfall forecasting in these sub-regions five months in advance and provided useful information for the decision-makers in Southwestern Colombia.

Complexity entropy-analysis of monthly rainfall time series in northeastern Brazil

2021 ◽  
Vol 143 ◽  
pp. 110623
Author(s):  
Antonio Samuel Alves Silva ◽  
Rômulo Simões Cezar Menezes ◽  
Osvaldo A. Rosso ◽  
Borko Stosic ◽  
Tatijana Stosic
Keyword(s):  
Time Series ◽  
Monthly Rainfall ◽  
Northeastern Brazil ◽  
Rainfall Time Series ◽  
Entropy Analysis

scholarly journals Monthly rainfall forecast study in southeastern Brazil using multi-layer perceptron (MLP) neural networks

2020 ◽  
Vol 42 ◽  
pp. e4
Author(s):  
Cleber Souza Corrêa ◽  
Diogo Machado Custodio ◽  
Haroldo De Campos Velho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Prediction Error ◽  
Monthly Rainfall ◽  
Southeastern Brazil ◽  
Multi Layer Perceptron ◽  
Mlp Neural Network ◽  
Rainfall Forecast ◽  
Mlp Neural Networks

This work uses the MLP neural network technique to make monthly rainfall forecast estimates for Guarulhos airport in southeastern Brazil using a time series of approximately 70 years. Neural network structures with two or more hidden layers showed a better result, minimizing the prediction error.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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