Overview of Artificial Neural Networks

Rosa, João P. S.; Guerra, Daniel J. D.; Horta, Nuno; Martins, Ricardo; Lourenço, Nuno

doi:10.1007/978-3-030-35743-6_3

Cited by 26 publications

(7 citation statements)

References 25 publications

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“…Training ANNs to learn predictive subseasonal signals amongst noisy climate data is a data-intensive process (Rosa et al 2020). Thus, we take advantage of historical simulations performed as part of the CESM-2 LE (CESM2-LE; Danabasoglu et al 2020).…”

Section: Climate Model Datamentioning

confidence: 99%

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Arcodia

Barnes

Mayer

et al. 2023

Environ. Res.: Climate

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Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during forecasts of opportunity. Specifically, this work quantifies subseasonal prediction skill provided by the tropics within the CESM2 Large Ensemble and assesses how this skill evolves on decadal timescales. Utilizing the networks’ confidence and explainable artificial intelligence (XAI), physically meaningful sources of predictability associated with periods of enhanced skill are identified. Using these networks, we find that tropically-driven subseasonal predictability varies on decadal timescales during forecasts of opportunity. Analysis is extended to ECMWF Reanalysis v5 data, revealing that the relationships learned within the CESM2-LE holds in modern reanalysis data. These results indicate that the neural networks are capable of identifying predictable decadal states of the climate system within CESM2 that are useful for making confident, accurate subseasonal precipitation predictions in the real world.

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Section: Climate Model Datamentioning

confidence: 99%

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Arcodia

Barnes

Mayer

et al. 2023

Environ. Res.: Climate

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“…ANN is a single perceptron composed of an input layer, an output layer, and a hidden layer 137 between the two layers (Fig. 1a) (Rosa et al, 2020). In this study, the R "nnet" package (Ripley 138 and Venables 2021a) was used, and the hyperparameters were set as follows: size (number of 139 hidden nodes)=10, maxit (maximum number of iterations)=900, and decay (parameter for weight 140 decay)=0.5.…”

Section: Artificial Neural Network 136mentioning

confidence: 99%

Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm

Kim

Wan

Chang

et al. 2022

Aerosol Air Qual. Res.

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Visibility is an important indicator of air quality and of any consequent meteorological and climate change. Therefore, visibility in Seoul, which is the most polluted city in South Korea, was estimated using machine learning (ML) algorithms based on meteorological (temperature, relative humidity, and precipitation) and particulate matter (PM10 and PM2.5) data acquired from an automatic weather station, and the estimated visibility was compared with the observed visibility. Meteorological data, observed at 1-h intervals between 2018 and 2020, were used. Through learning and validation of each ML algorithm, the extreme gradient boosting (XGB) algorithm was found to be most suitable for visibility estimations (bias=0 km, root mean square error (RMSE)=0.08 km, and r=1 for training data set). Among the meteorological and particulate matter data used for learning the XGB algorithm, the relative importance of PM2.5 and relative humidity variables were high (51% and 19%, respectively), whereas precipitation and wind speed had the low relative importance (approximately 1%). The estimation accuracy for the test dataset was good (bias=-0.11 km, RMSE=2.08 km, and r=0.94); the estimation accuracy was higher in the dry season (bias=-0.06 km, RMSE=1.79 km, and r=0.96) than in the rainy season (bias=-0.17 km, RMSE=2.34 km, and r=0.91). The results of this study indicated a higher correlation than the results of previous visibility estimation studies. The proposed method promotes accurate estimation of visibility in areas with poor visibility, and thus, it can be used to assess public health in areas with poor air quality.

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“…The artificial neural network is a data structure inspired by a framework of biological neurons in living organisms, wherein each neuron is a unit that performs a simple task characterized by responding to an input signal. However, a connected network of neurons (such as the human brain) is capable of completing complex tasks and processes with impeccable speed and accuracy [14]. Similar to the biological neural network, an ANN for airline resource capacity management during irregular operations represents a connection of nodes that are analogous to neurons that implicitly describe the physical processes of ADM. To this effect, the ANN is defined by three pertinent characteristics namely: node character, network topology, and learning rules [15].…”

Section: A Problem Formulation As An Artificial Neural Networkmentioning

confidence: 99%

Artificial Neural Network Modeling for Airline Disruption Management

Ogunsina,

Okolo

2021

Preprint

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Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

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Overview of Artificial Neural Networks

Cited by 26 publications

References 25 publications

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI

Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm

Artificial Neural Network Modeling for Airline Disruption Management

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