2 ANSER TECHNIQUE 
21 The Architecture of ANSER 
The architecture of an ANSER for satellite-derived 
estimation of rainfall can be seen in Fig. 1. There are 
three parts of this architecture: (a) ANSER USER 
SYSTEM; (b) ANSER TRAINING SYSTEM; (c) ANSER 
CENTER SYSTEM. The ANSER USER SYSTEM has 
one or more user subsystem(s) based in the IBM PC for 
estimates by users. Each user subsystem consists of a 
training subsystem, weight base and estimate subsystem. 
The ANSER TRAINING SYSTEM has more than one 
training subsystems operating on the mainframe computer 
NCCF HDS 9000 for training weights of ANSER. Each 
training subsystem has first training, re-training and output 
result functions. ANSER CENTER SYSTEM receives 
satellite data based on the IBM RISC 520 for the experts 
and it has six parts: (a) display subsystem for output of 
rainfall estimates; (b) explanation subsystem that gives 
different classification to different data; (c) a reasoning 
network for rainfall estimation based on the input data 
and rule, model, and knowledge base; (d) rule bases, 
mode bases, and knowledge bases will save rule, model, 
and knowledge provided by the expert; (e) a training 
subsystem for getting suitable weights for the ANSER; (f) 
weight bases for keeping weights of ANSER. ANSER 
USER SYSTEM, ANSER TRAINING SYSTEM and 
ANSER CENTER SYSTEM communicate with each 
other using Ethernet. This architecture will be enhanced 
for application to derive estimation of rainfall from 
satellite data. 
22 Architecture of Reasoning Network 
The basic architecture of reasoning network for 1/2 hour 
satellite-derived estimation of rainfall can be seen in Fig. 
2. This is a 3 layer artificial neural network that includes 
7 input linear neurons, 30 hidden nonlinear neurons 
(divided into 2 layers) and 1 output nonlinear neuron. 
There are 345 weights in this network. 
The artificial neuron is a unit that functions similar to the 
real neuron of human (in this paper, neuron means 
artificial neuron). The ANN is a system which consists of 
artificial neurons that are connected to each other by 
weights. The function of each nonlinear neuron is sigmoid 
and is given by: 
N 
Yj-M( *exp(-5 ^ (Yi«Wij)).. 
i=0 
where: Yj - the output of the j-th artificial neuron. 
Wij - the weight connected the i-th artificial 
neuron with the j-th artificial neuron. 
Yi - the output of the i-th artificial neuron. 
When the ANN was described in this paper, the artificial 
neuron numbers of layer are different. However, three 
layers of structure are always used. Several basic 
structures of ANNs will be connected to each other to 
become reasoning networks and the basic structure of the 
ANSER system. 
3 ANSER PERFORMANCE 
3.1. Input and Output of Reasoning Network 
The input and output of the reasoning network are as 
follows: 
Input: G = (cloud top temperature + cloud growth factor) 
or (cloud top temperature + strong divergence aloft) 
RB = rain burst factor 
OS = overshooting top factor 
M = merger factor 
SE = saturated environment factor 
MC = moisture correction 
= speed of storm 
Output: 1/2 hour Satellite-derived Estimation of Rainfall. 
32 Training Algorithm of the Reasoning Network 
The training algorithm of the reasoning network for 1/2 
hour Satellite-derived Estimates of Rainfall are as follows: 
(1) Set random values to all weights of the reasoning 
neural network. 
(2 A special type or model of rainfall should be 
chosen. It means that the reasoning network will be 
trained for this special type or model of rainfall 
estimation. 
(3) One case of rainfall which has the same type or 
model rainfall mentioned in (2) is chosen to train 
the reasoning network for getting convergence 
weights. 
(a) Using Scofield/Oliver Technique: 
The inputs of reasoning network are 
the 7 factors shown in the beginning 
of this section. The output of the 
reasoning network is: S/O E=[(G or 
RB)+OS+M+SE]*MC*S. 
(b) The reasoning network is trained. 
The convergence weights are the 
weights of the 1/2 hour 
Scofield/Oliver reasoning network. 
(4) The same case of rainfall which has the same type 
or model rainfall mentioned in (2) is chosen again 
to re-train the reasoning network for getting 
convergence weights for this special type or model 
case of rainfall. 
(a) The weights of the 1/2 hour 
Scofield/Oliver reasoning network 
will be used firstly. 
(b) The inputs of reasoning network are 
the 7 factors based on the 
Scofield/Oliver Technique. 
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