Two Geoscience Applications by Optimal Neural Network Architecture

Anochi, Juliana Aparecida; Torres, Reynier Hernández; Velho, Haroldo Fraga de Campos

doi:10.1007/s00024-019-02386-y

Cited by 6 publications

(8 citation statements)

References 33 publications

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“…Therefore, an approach to optimize this problem is to formulate such parameters as an optimization problem. Many optimization approaches appear as an efficient alternative to the topology definition problem [13,[16][17][18].…”

Section: Neural Network For Climate Precipitation Predictionmentioning

confidence: 99%

“…Therefore, it is necessary to develop effective methods for accelerating the assessment of the fitness of neural networks. Indeed, Anochi and co-authors have shown a better performance for neural networks with architecture found by an automatic configuration procedure than neural architectures designed by experts [13], where two applications were analyzed: a data assimilation example and a seasonal precipitation prediction for the South of Brazil.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Climate Precipitation Prediction Modeling over South America

2021

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“…ANN topology has a high impact on its performance. A small number of neurons may reduce the ANN learning capacity, but an excessive number of neurons may reduce its generalization capacity [39].…”

Section: Introductionmentioning

confidence: 99%

“…The MPCA optimization algorithm takes into account all relevant ANN parameters as the number of hidden layers, the number of neurons in each hidden layer, the weight values, the learning rate, the momentum constant, and the activation function [49]. MPCA has been successfully applied to climate prediction applications [39]. The present paper proposes an optimized ANN for damage detection in a plate-like structure using the MPCA.…”

Section: Introductionmentioning

confidence: 99%

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A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

et al. 2021

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A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

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

Anochi

Torres

Velho

2020

Lecture Notes in Mechanical Engineering

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Two Geoscience Applications by Optimal Neural Network Architecture

Cited by 6 publications

References 33 publications

Machine Learning for Climate Precipitation Prediction Modeling over South America

Machine Learning for Climate Precipitation Prediction Modeling over South America

A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

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

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