I was trying to use one hidden layer neural network model to predict the observing data rainfall (target value, the red dots), which shouldn’t be negative. This result of each day contains 1000 predictions, which was calculated as probability density and shown with the different shades of blue (It’s similar to the posterior distribution, but all put together) (green dot is one of the inputs) I also tried with two layer model and several different range of mu and sdt for weights in the hidden layer but it didn’t change too much the results.

Is there any methods to limited the results not to be negative? Any information is appreciated. Thanks a lot.

Model information

structure: 7-10-1 (inputs - nodes in hidden layer - output)

w_in_1_mu, sd 0, 2

w_1_out_mu,sd 0, 2

train_samp 20000

train_tune 1000