Artificial neural networks analysis of São Paulo subway tunnel settlement data

Soil loss is one of the main causes of pauperization and alteration of agricultural soil properties. Various empirical models (e.g., USLE) are used to predict soil losses from climate variables which in general have to be derived from spatial interpolation of point measurements. Alternatively, Artificial Neural Networks may be used as a powerful option to obtain site-specific climate data from independent factors. This study aimed to develop an artificial neural network to estimate rainfall erosivity in the Ribeira Valley and Coastal region of the State of São Paulo. In the development of the Artificial Neural Networks the input variables were latitude, longitude, and annual rainfall and a mathematical equation of the activation function for use in the study area as the output variable. It was found among other things that the Artificial Neural Networks can be used in the interpolation of rainfall erosivity values for the Ribeira Valley and Coastal region of the State of São Paulo to a satisfactory degree of precision in the estimation of erosion. The equation performance has been demonstrated by comparison with the mathematical equation of the activation function adjusted to the specific conditions of the study area.

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Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models

PLoS ONE ◽

10.1371/journal.pone.0195065 ◽

2018 ◽

Vol 13 (4) ◽

pp. e0195065 ◽

Cited By ~ 13

Author(s):

Oswaldo Santos Baquero ◽

Lidia Maria Reis Santana ◽

Francisco Chiaravalloti-Neto

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Moving Average ◽

Generalized Additive Models ◽

Sao Paulo ◽

Additive Models ◽

São Paulo ◽

Autoregressive Integrated Moving Average ◽

Moving Average Models ◽

Artificial Neural

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Computational Principles of Supervised Learning in the Cerebellum

Annual Review of Neuroscience ◽

10.1146/annurev-neuro-080317-061948 ◽

2018 ◽

Vol 41 (1) ◽

pp. 233-253 ◽

Cited By ~ 59

Author(s):

Jennifer L. Raymond ◽

Javier F. Medina

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Supervised Learning ◽

Brain Area ◽

Future Research ◽

Brain Areas ◽

Adaptive Mechanisms ◽

Networks Analysis ◽

Artificial Neural ◽

Organizational Principles

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

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