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

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.

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Monthly rainfall forecasting using temperature and climate indices through a hybrid method in Queensland, Australia

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0169.1 ◽

2021 ◽

Author(s):

Meysam Ghamariadyan ◽

Monzur A. Imteaz

Keyword(s):

Hybrid Methods ◽

Southern Oscillation ◽

Monthly Rainfall ◽

Climate Indices ◽

Ann Model ◽

Rainfall Forecasting ◽

Average Value ◽

Artificial Neural ◽

Agricultural Industries ◽

Artificial Neural Network Ann

AbstractThis paper presents applications of wavelet artificial neural networks (WANN) to forecast rainfalls one, three, six, and twelve months in advance using lagged monthly rainfall, maximum, minimum temperatures, Southern Oscillation Index (SOI), Inter-decadal Pacific Oscillation (IPO), and Nino3.4 as predictors. Eight input datasets comprised of different combinations of predictive variables were used for ten candidate climate stations in Queensland, Australia. Datasets were split as 1908 to 1999 for the training of the model and 2000 to 2016 for the verification of the model. Also, the conventional Artificial Neural Network (ANN) model was developed with the same input datasets to compare with WANN results. Moreover, the skillfulness of the WANN was investigated with the current climate prediction system used by the Australian Bureau of Meteorology (BOM), Australian Community Climate Earth-System Simulator–Seasonal (ACCESS–S) as well as climatology forecasts. The comparisons showed that the WANN achieved the lowest errors for three-month lagged prediction with an average Root Mean Square Error (RMSE) of 38.6mm. In contrast, for the same lag-period, the average RMSEs from ANN, ACCESS-S, and climatology predictions were 72.2mm, 102.7mm, and 72.2mm, respectively. It is also found that the ANN underestimates the peak values with an average value of 49%, 47%, 52%, and 53% at one, three, six, and twelve months lead times, correspondingly. However, the corresponding peak values underestimation through the WANN were 0%, 1%, 22%, and 39%, respectively. This research provides promising insights into using hybrid methods for predicting rainfall a few months in advance, which is extremely beneficial for Australia’s agricultural industries.

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Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks

Atmospheric Research ◽

10.1016/j.atmosres.2013.11.002 ◽

2014 ◽

Vol 138 ◽

pp. 166-178 ◽

Cited By ~ 86

Author(s):

John Abbot ◽

Jennifer Marohasy

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Monthly Rainfall ◽

Rainfall Forecasting ◽

Input Selection ◽

Artificial Neural

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Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Sensors ◽

10.3390/s21051770 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1770

Author(s):

Javier González-Enrique ◽

Juan Jesús Ruiz-Aguilar ◽

José Antonio Moscoso-López ◽

Daniel Urda ◽

Lipika Deka ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Short Term Memory ◽

Prediction Method ◽

Monitoring Network ◽

Exogenous Variables ◽

Prediction Horizon ◽

Artificial Neural ◽

Input Variables ◽

Analytical Tools

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.

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Teleconnections between Monthly Rainfall Variability and Large-Scale Climate Indices in Southwestern Colombia

Water ◽

10.3390/w12071863 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1863 ◽

Cited By ~ 1

Author(s):

Teresita Canchala ◽

Wilfredo Alfonso-Morales ◽

Wilmar Loaiza Cerón ◽

Yesid Carvajal-Escobar ◽

Eduardo Caicedo-Bravo

Keyword(s):

Water Resources ◽

El Niño ◽

El Nino ◽

Southern Oscillation ◽

Rainfall Variability ◽

Monthly Rainfall ◽

Wavelet Coherence ◽

Climate Indices ◽

Annual Scale ◽

Wavelet Coherence Analysis

Given that the analysis of past monthly rainfall variability is highly relevant for the adequate management of water resources, the relationship between the climate-oceanographic indices, and the variability of monthly rainfall in Southwestern Colombia at different time scales was chosen as the research topic. It should also be noted that little-to-no research has been carried out on this topic before. For the purpose of conducting this research, we identified homogeneous rainfall regions while using Non-Linear Principal Component Analysis (NLPCA) and Self-Organizing Maps (SOM). The rainfall variability modes were obtained from the NLPCA, while their teleconnection in relation to the climate indices was obtained from Pearson’s Correlations and Wavelet Transform. The regionalization process clarified that Nariño has two regions: the Andean Region (AR) and the Pacific Region (PR). The NLPCA showed two modes for the AR, and one for the PR, with an explained variance of 75% and 48%, respectively. The correlation analyses between the first nonlinear components of AR and PR regarding climate indices showed AR high significant positive correlations with Southern Oscillation Index (SOI) index and negative correlations with El Niño/Southern Oscillation (ENSO) indices. PR showed positive ones with Niño1 + 2, and Niño3, and negative correlations with Niño3.4 and Niño4, although their synchronous relationships were not statistically significant. The Wavelet Coherence analysis showed that the variability of the AR rainfall was influenced principally by the Niño3.4 index on the 3–7-year inter-annual scale, while PR rainfall were influenced by the Niño3 index on the 1.5–3-year inter-annual scale. The El Niño (EN) events lead to a decrease and increase in the monthly rainfall on AR and PR, respectively, while, in the La Niña (LN) events, the opposite occurred. These results that are not documented in previous studies are useful for the forecasting of monthly rainfall and the planning of water resources in the area of study.

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