10.4225/03/58006fa3f3423 Dutta, Sridhar Sridhar Dutta Ganguli, Rajive Rajive Ganguli Samanta, Biswajit Biswajit Samanta Investigation of Two Neural Network Methods in an Automatic Mapping Exercise Monash University 2016 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 2016-10-14 05:39:46 Journal contribution 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