Climate Precipitation Prediction with Uncertainty Quantification by Self-configuring Neural Network

Anochi, Juliana Aparecida; Torres, Reynier Hernández; Velho, Haroldo F. Campos

doi:10.1007/978-3-030-53669-5_18

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…In this contribution, the definition of parameters and weights for the neural network, we present the MPCA metaheuristic to optimize the parameters of topology, which can be deployed effectively in low-resource settings. The self-configured NN by the MPCA metaheuristic has been used successfully in different fields, including fault diagnosis [19], atmospheric temperature profile identification [20], structural damage identification [21], inverse radiative problems [22], autonomous navigation by image processing [23], climate prediction with uncertainty quantification [24], and data assimilation [25].…”

Section: Neural Network For Climate Precipitation Predictionmentioning

confidence: 99%

Machine Learning for Climate Precipitation Prediction Modeling over South America

2021

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Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models have been developed to tackle different issues in society, but there is still a gap in applications to applied physics. Here, different machine learning models are evaluated for precipitation prediction over South America. Currently, numerical weather prediction models are unable to precisely reproduce the precipitation patterns in South America due to many factors such as the lack of region-specific parametrizations and data availability. The results are compared to the general circulation atmospheric model currently used operationally in the National Institute for Space Research (INPE: Instituto Nacional de Pesquisas Espaciais), Brazil. Machine learning models are able to produce predictions with errors under 2 mm in most of the continent in comparison to satellite-observed precipitation patterns for different climate seasons, and also outperform INPE’s model for some regions (e.g., reduction of errors from 8 to 2 mm in central South America in winter). Another advantage is the computational performance from machine learning models, running faster with much lower computer resources than models based on differential equations currently used in operational centers. Therefore, it is important to consider machine learning models for precipitation forecasts in operational centers as a way to improve forecast quality and to reduce computation costs.

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Section: Neural Network For Climate Precipitation Predictionmentioning

confidence: 99%

Machine Learning for Climate Precipitation Prediction Modeling over South America

2021

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“…[10] evaluated two types of neural networks for data assimilation using also an ensemble strategy applied to hydrological models. [11] developed a neural network model for climate prediction of precipitation with uncertainty quantification for forecasting. [12] compared three different approaches to obtain an individual (each prediction) uncertainty, testing them on 12 machine learning-physical properties.…”

Section: Introductionmentioning

confidence: 99%

“…From the variance, the confidence interval of the prediction is calculated. We also note that [11] carried out a seasonal climate prediction. Here, a monthly climate timescale is our focus.…”

Section: Introductionmentioning

confidence: 99%

“…For a specific meteorological variable or more than one of those, machine learning algorithms can be applied to perform the prediction, where some meteorological variables are used as inputs. Different from the method explored by [11] with only one neural network for predicting the precipitation and the associated forecasting variance, a decision tree scheme is adapted to do the climate precipitation prediction on South America. In addition, a second decision tree is also designed to estimate the prediction variance.…”

Section: Introductionmentioning

confidence: 99%

“…Figures 10,11, and 12 represent the prediction error, as calculated by Equation16. Figure10is the error of the AGCM/INPE prediction.…”

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confidence: 99%

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Machine learning for uncertainty quantification in climate precipitation prediction over South America

Monego,

Anochi,

Velho

2023

Preprint

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In recent times, machine learning algorithms have found applications in various fields. Nice results have been obtained by employing these algorithms, including precipitation climate prediction. Precipitation is a key meteorological variable in South America, related to the intense rainfall under different climate seasons. For Brazil, the importance is beyond agriculture, civil defense, and tourism, because most part of the electric energy is produced by hydroelectric power plants. The inputs are some meteorological variables (wind fields, temperature, and precipitation from the previous month). Decision tree is the approach applied for monthly precipitation prediction, as implemented in the Light Gradient Boosting Machine (LightGBM) framework, after the prediction, another important issue is to quantify how good is the forecasting, in other words, it is important to estimate the predictability -- or uncertainty quantification to the prediction. Therefore, two decision trees were designed to estimate the precipitation and the prediction uncertainty over South America. Optimal hyperparameters for the machine learning tools were determined by Optuna optimizer. Data from the years January-1980 up to December-2017 were addressed for algorithms training, while the years 2018 and 2019 were used for testing. Very good results were obtained for precipitation prediction, with uncertainty maps calculated for all South America territory.

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Data Assimilation Using Co-processors for Ocean Circulation

Paiva,

Sambatti,

Dias

et al. 2023

Lecture Notes in Mechanical Engineering

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Climate Precipitation Prediction with Uncertainty Quantification by Self-configuring Neural Network

Cited by 4 publications

References 12 publications

Machine Learning for Climate Precipitation Prediction Modeling over South America

Machine Learning for Climate Precipitation Prediction Modeling over South America

Machine learning for uncertainty quantification in climate precipitation prediction over South America

Data Assimilation Using Co-processors for Ocean Circulation

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