El Niño southern-oscillation prediction using southern oscillation index and Niño3 as onset indicators: Application of artificial neural networks

Baawain, Mahad; Nour, M.H.; El‐Din, Ahmed G.; El-Din, Mohamed Gamal

doi:10.1139/s04-047

Cited by 15 publications

(11 citation statements)

References 17 publications

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“…An exception is the work of Baawain et al [59] in which very high correlations (above 0.8 for lead times between 1 and 12 months) were reported for prediction of the NINO3 index using as inputs the two surface-wind components and the SAT at four selected locations in the Pacific (thus, 12 inputs). The high forecast skill may arise from the careful and systematic determination of the ANN architecture (again a single hidden layer but with up to 16 neurons, and different activation functions), or perhaps from the choices of training and validation data sets.…”

Section: Early ML Approachesmentioning

confidence: 98%

“…The high forecast skill may arise from the careful and systematic determination of the ANN architecture (again a single hidden layer but with up to 16 neurons, and different activation functions), or perhaps from the choices of training and validation data sets. Some of the practices in Baawain et al [59], however, are rather questionable and can lead to substantial overfitting for the ENSO prediction. First, they perform the hyperparameter optimization on their test data set.…”

Section: Early ML Approachesmentioning

confidence: 99%

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The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

et al. 2019

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We review prediction efforts of El Niño events in the tropical Pacific with particular focus on using modern machine learning (ML) methods based on artificial neural networks. With current classical prediction methods using both statistical and dynamical models, the skill decreases substantially for lead times larger than about 6 months. Initial ML results have shown enhanced skill for lead times larger than 12 months. The search for optimal attributes in these methods is described, in particular those derived from complex network approaches, and a critical outlook on further developments is given.

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Section: Early ML Approachesmentioning

confidence: 98%

Section: Early ML Approachesmentioning

confidence: 99%

The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

et al. 2019

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“…Artificial neural networks (ANNs) can be used to forecasts climate indices [13,14], with correlation coefficients above 0.8 for monthly values of SOI and SSTs with lead time of 1 month [14].…”

Section: Climate Indices and Rainfall Forecastingmentioning

confidence: 99%

Using lagged and forecast climate indices with artificial intelligence to predict monthly rainfall in the brisbane catchment, Queensland, Australia

Abbot¹,

Marohasy²

2015

Int. J. SDP

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Brisbane, the capital of Queensland, Australia, has flooded periodically and catastrophically, most recently in January 2011. Official seasonal rainfall forecasts failed to predict the floods. Since winter 2013, the Australian Bureau of Meteorology uses a general circulation model, the Predictive Ocean Atmosphere Model for Australia (POAMA), to make official seasonal rainfall forecasts presented as the conditional probability of rainfall being greater or less than the long-term median rainfall. We show that a more skilful forecast can be made using an artificial neural network (ANN), a form of statistical modelling based on artificial intelligence. A Jordan recurrent neural network with one hidden layer was implemented, using genetic optimization of inputs. For the sites of Gatton and Harrisville, in the Brisbane catchment, monthly rainfall forecasts from the ANN show lower root mean square errors than forecasts from POAMA. These rainfall forecasts from the ANN model were further improved by using inputs of independently forecast values for climate indices including the Southern Oscillation Index, the Interdecadal Pacific Oscillation, Pacific sea surface temperature anomalies (Niño 3.4) and also atmospheric temperature. The results presented here represent a first attempt at independently forecasting climate indices using an ANN model for the Australian east coast.

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“…The weights of the links are adjusted to minimize the prediction errors according to the training algorithm being used. The network is considered well trained when the sum of all the errors in the network reaches a global minimum (Baawain et al, 2005).…”

Section: Model Developmentmentioning

confidence: 99%

Systematic Approach for the Prediction of Ground-Level Air Pollution (around an Industrial Port) Using an Artificial Neural Network

Baawain

Al-Serihi

2014

Aerosol Air Qual. Res.

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The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM 10 , NO, NO 2 , NO x , SO 2 , H 2 S, and O 3 , which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R 2 ) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O 3 , SO 2 , and NO x , whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO 2 , and H 2 S. PM 10 concentrations are influenced by almost all the measured meteorological parameters.

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El Niño southern-oscillation prediction using southern oscillation index and Niño3 as onset indicators: Application of artificial neural networks

Cited by 15 publications

References 17 publications

The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

Using lagged and forecast climate indices with artificial intelligence to predict monthly rainfall in the brisbane catchment, Queensland, Australia

Systematic Approach for the Prediction of Ground-Level Air Pollution (around an Industrial Port) Using an Artificial Neural Network

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