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