Forecasting Monthly Rainfall in the Western Australian Wheat-Belt up to 18-Months in Advance Using Artificial Neural Networks

2016 ◽  
pp. 71-87 ◽  
Author(s):  
John Abbot ◽  
Jennifer Marohasy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monthly Rainfall ◽  
Australian Wheat ◽  
Artificial Neural ◽  
Western Australian

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.

Monthly rainfall–runoff modelling using artificial neural networks

2011 ◽  
Vol 56 (3) ◽  
pp. 349-361 ◽  
Author(s):  
Fernando Machado ◽  
Miriam Mine ◽  
Eloy Kaviski ◽  
Heinz Fill
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monthly Rainfall ◽  
Rainfall Runoff ◽  
Artificial Neural
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