ANSER: an Adaptive-Neuron Artificial Neural Network System for Estimating Rainfall Using Satellite Data

We propose a new neural network model ‚Äö- Neuron-Adaptive artificial neural Network (NAN) ‚Äö- is developed. A learning algorithm is derived to tune both the neuron activation function free parameters and the connection weights between neurons. We proceed to prove that a NAN can approximate any piecewise continuous function to any desired accuracy, then relate the approximation properties of NAN models to some special mathematical functions. A neuron-Adaptive artificial Neural network System for Estimating Rainfall (ANSER) which uses NAN as its basic reasoning network is described. Empirical results show that the NAN model performs about 1.8% better than artificial Neural Network Groups, and around% better than classical Artificial Neural Networks when using a rainfall estimate experimental database. The empirical results also show that by using the NAN model, ANSER plus can (i) automatically compute rainfall amounts ten times faster; and (ii) reduce average errors of rainfall estimates for the total precipitation event to less than 10 per cent.