Investigation of Two Neural Network Methods in an Automatic Mapping Exercise
Dutta, Sridhar
Ganguli, Rajive
Samanta, Biswajit
10.4225/03/58006fa3f3423
https://bridges.monash.edu/articles/journal_contribution/Investigation_of_Two_Neural_Network_Methods_in_an_Automatic_Mapping_Exercise/4004433
This paper investigates the performance of two neural network (NN) methods viz. a radial basis function network (RBFN) and a multilayer feed forward network (MFFN) to predict the radioactivity levels at a given test site. A comparative evaluation of the two networks is done using Root mean square error (RMSE), Pearsons r, Mean error (ME) and Mean Absolute error (MAE). It was found that the RBFN performed marginally better compared to the other method
2016-10-14 05:39:46
monash:89913
1959.1/736794
SIC 2004
Automatic mapping
Artificial neural network
RBFN
MFFN
ANN
RBF Modeling
Feedforward Network Modeling
Genetic algorithm
Radioactivity levels
2005
collection(s) Applied GIS
text
journal article
1832-5505