scholarly journals Predicting Injury Severity of Angle Crashes Involving Two Vehicles at Unsignalized Intersections Using Artificial Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 3871-3880
Author(s):  
S. A. Arhin ◽  
A. Gatiba
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Injury Severity ◽  
Economic Cost ◽  
The United States ◽  
Sigmoid Function ◽  
Unsignalized Intersections ◽  
Fatal Crashes ◽  
Unit Function ◽  
Artificial Neural

In 2015, about 20% of the 52,231 fatal crashes that occurred in the United States occurred at unsignalized intersections. The economic cost of these fatalities have been estimated to be in the millions of dollars. In order to mitigate the occurrence of theses crashes, it is necessary to investigate their predictability based on the pertinent factors and circumstances that might have contributed to their occurrence. This study focuses on the development of models to predict injury severity of angle crashes at unsignalized intersections using artificial neural networks (ANNs). The models were developed based on 3,307 crashes that occurred from 2008 to 2015. Twenty-five different ANN models were developed. The most accurate model predicted the severity of an injury sustained in a crash with an accuracy of 85.62%. This model has 3 hidden layers with 5, 10, and 5 neurons, respectively. The activation functions in the hidden and output layers are the rectilinear unit function and sigmoid function, respectively.

scholarly journals Application of Artificial Neural Networks on North Atlantic Tropical Cyclogenesis Potential Index in Climate Change

2012 ◽  
Vol 29 (9) ◽  
pp. 1202-1220 ◽  
Author(s):  
Zheng Ki Yip ◽  
M. K. Yau
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Tropical Cyclone ◽  
North Atlantic ◽  
The United States ◽  
First Century ◽  
The North ◽  
Potential Index ◽  
Artificial Neural ◽  
Twenty First Century

Abstract A methodology using artificial neural networks is presented to project twenty-first-century changes in North Atlantic tropical cyclone (TC) genesis potential (GP) in a five-model ensemble of global climate models. Two types of neural networks—the self-organizing maps (SOMs) and the forward-feeding back-propagating neural networks (FBNNs)—were employed. This methodology is demonstrated to be a robust alternative to using GCM output directly for tropical cyclone projections, which generally require high-resolution simulations. By attributing the projected changes to the related environmental variables, Emanuel’s revised genesis potential index is used to measure the GP. Changes are identified in the first (P1) and second (P2) half of the twenty-first century. The early and late summer GP decreases in both the P1 and P2 periods over most of the eastern half of the basin and increases off the East Coast of the United States and the north coast of Venezuela during P1. The peak summer GP over the region of frequent TC genesis is projected to decrease more substantially in P1 than in P2. Vertical wind shear (850–200 hPa), temperature (600 hPa), and potential intensity are the most important controls of TC genesis in the North Atlantic basin (NAB) under the changing climate.

scholarly journals Wind speed forecasting using neural networks

2019 ◽  
Vol 44 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Tyler Blanchard ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
The United States ◽  
Weather Data ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.

Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States

2020 ◽  
Author(s):  
Matteo Santoni ◽  
Francesco Piva ◽  
Camillo Porta ◽  
Sergio Bracarda ◽  
Daniel Y. Heng ◽  
...  
Keyword(s):  
United States ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kidney Cancer ◽  
Cancer Incidence ◽  
The United States ◽  
Artificial Neural
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