scholarly journals Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1973
Author(s):  
Augusto Rafael Garrido-Arévalo ◽  
Luis Mauricio Agudelo-Otálora ◽  
Nelson Obregón-Neira ◽  
Victor Garrido-Arévalo ◽  
Edgar Eduardo Quiñones-Bolaños ◽  
...  
Keyword(s):  
Information Entropy ◽  
Mountainous Area ◽  
Self Organizing Maps ◽  
Model Classification ◽  
Rainfall Station ◽  
Output Layer ◽  
Lack Of Information ◽  
Historical Series ◽  
Artificial Neural

An assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into two phases: first, a classification of the meteorological stations using two-dimensional self-organizing maps; second, the evaluation of the performance of the ANN by applying concepts of information entropy. Three scenarios were raised for the classification of the meteorological stations by adjusting the number of neurons in the output layer. A high number of neurons in the output layer were obtained, causing the model to over-fit while emphasizing differences amid patterns. When comparing the results of the scenarios, the permanence of certain characteristics and features was found in the system, validating the model classification. Subsequently, the results of the first scenario were used to evaluate the entropy of the historical series. Finally, the results show that the area of study presents a lack of information due to the uncertainty associated with the probabilistic arrangement, which can be corrected with the developed model. Consequently, some recommendations for the redesign of the rainfall are provided.

scholarly journals Investigation of trends in synoptic patterns over Europe with artificial neural networks

2010 ◽  
Vol 23 ◽  
pp. 107-112 ◽  
Author(s):  
S. Michaelides ◽  
F. Tymvios ◽  
D. Charalambous
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Frequency Distribution ◽  
Reanalysis Data ◽  
Self Organizing Maps ◽  
Synoptic Patterns ◽  
Weather Forecasts ◽  
The Neural Network ◽  
Artificial Neural

Abstract. The present study is a comprehensive application of a methodology developed for the classification of synoptic situations using artificial neural networks. In this respect, the 500 hPa geopotential height patterns at 12:00 UTC (Universal Time Coordinated) determined from the reanalysis data (ERA-40 dataset) of the European Centre for Medium range Weather Forecasts (ECMWF) over Europe were used. The dataset covers a period of 45 years (1957–2002) and the neural network methodology applied is the SOM architecture (Self Organizing Maps). The classification of the synoptic scale systems was conducted by considering 9, 18, 27 and 36 synoptic patterns. The statistical analysis of the frequency distribution of the classification results for the 36 clusters over the entire 44-year period revealed significant tendencies in the frequency distribution of certain clusters, thus substantiating a possible climatic change. In the following, the database was split into two periods, the "reference" period that includes the first 30 years and the "test" period comprising the remaining 14 years.

scholarly journals Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

2014 ◽  
Vol 13 ◽  
pp. CIN.S17948 ◽  
Author(s):  
Priscyla W. Simões ◽  
Narjara B. Izumi ◽  
Ramon S. Casagrande ◽  
Ramon Venson ◽  
Carlos D. Veronezi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Analytical Study ◽  
Self Organizing Maps ◽  
Hybrid Neural Network ◽  
Artificial Neural ◽  
Validation Set ◽  
Classification Of Images ◽  
The City

Objective To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE: Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database ( n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. Results After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. Conclusion Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

Classification of ground-truth fire debris samples using artificial neural networks

2021 ◽  
Vol 23 ◽  
pp. 100313
Author(s):  
Nicholas A. Thurn ◽  
Taylor Wood ◽  
Mary R. Williams ◽  
Michael E. Sigman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Truth ◽  
Fire Debris ◽  
Artificial Neural

Scandinavian test of artificial neural network for classification of myocardial perfusion images

2000 ◽  
Vol 20 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Lindahl ◽  
Toft ◽  
Hesse ◽  
Palmer ◽  
Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Myocardial Perfusion ◽  
Artificial Neural
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