Cascade‐correlation learning architecture for first‐break picking and automated trace editing

Kusuma, T.; Brown, Mark

doi:10.1190/1.1821929

Cited by 7 publications

(2 citation statements)

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“…The CCNN (Fahlman & Lebiere, 1990) is a selforganizing network which begins with only input and output neurons. Every input is linked to every output, and each connection is defined by an adjustable weight.…”

Section: Ccnn Modelmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation-the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

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Section: Ccnn Modelmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

show abstract

“…The actual numbers of hidden layers and nodes in each layer are somewhat arbitrary, but do depend on external constraints from the physical problem such as the number of input and output nodes and also on the desired system error, pattern error and the nature of the training samples. There is no fixed relationship between these various factors for this type of neural network, although there are certain other network designs where this is the case (Kusuma and Brown 1992). We are guided by the knowledge that generalization is increased and memory is reduced by limiting the number of hidden nodes (Dowla et al 1990).…”

Section: Polarization Directionmentioning

confidence: 99%