kasetsart journal natural science

eservoir forecasting models A, B, C and D which
used the same levels of input data to the single
reservoir model. The multi-reservoir model
utilized more information on cross correlation
coefficients among the reservoir inflow which was
one of the advantages. However, the multireservoir
model required more computational time
than the single reservoir model for training. The
detail input variables of ANNs reservoir
forecasting models A, B, C and D are shown in
Table 3.
Training and testing of ANNs forecasting model
The multi-layer feed forward neural
networks with back propagation algorithm was
selected for this study. Sigmoid transfer function
was used in all the models. The initial weight (IW),
momentum (M) and learning rate (LR) were
initially set between 0.80-0.90. The number of
epochs for training varied between 10,000 to
100,000 depending on the performance efficiency
(R 2 ) of the training. There were no fixed rules for
designing the structures of ANNs, number of
hidden layers and number of neurons in hidden
layers. Many times the best fit model was
Kasetsart J. (Nat. Sci.) 40(1) 251
Table 1 The basic data of the four reservoirs in Upper Mun basin.
Basic data MB LC LP LK
1. Catchment area (km 2 ) 454 601 807 1,430
2. Annual rainfall (mcm) 1,047 1,039 1,112 920
3. Annual inflow (mcm) 98 218 158 223
4. Storage capacity(mcm) 141 275 110 310
5. Irrigable area (rai) 44,600 113,750 67,760 127,540
6. Municipality & downstream control (mcm/month) 0.4025 5.13 1.93 2.04
Table 2 The training and testing data set.
Reservoir Available data Training data set Testing data set
1. Mun Bon Apr 1995-Jan 2000 Apr 1995-Dec 1998 Jan 1999-Jan 2000
2. Lam Chae Jan 1999-Mar 2002 Jan 1999-Feb 2001 Mar 2001-Mar 2002
3. Lam Phra Phloeng Jul 1992-Mar 2000 Jul 1992-Dec 1998 Jan 1999-Mar 2000
4. Lam Takong Jul 1992-Mar 2000 Jul 1992-Dec 1998 Jan 1999-Mar 2000
accomplished by trial and error processes. The
optimal ANNs design was considered from the best
performance training and testing by using R 2 .
• Single reservoir forecasting model
The ANNs structures and the training
parameters of the best fit single reservoir
forecasting models are presented in Table 4. The
time series plot of the actual inflow and the
predicted or forecasted inflow were compared for
the daily, weekly and monthly models in Figures
2 to 4, respectively. The best fit models were
models A and B. The number of hidden layers
varied between one to three layers but mostly 2 to
3 layers. The number of neurons in hidden layers
varied considerably from one model to the others.
The models were trained by 80% of the data. The
training result showed that all the ANNs models
could produce an acceptable result in reservoir
inflow forecasting especially the monthly model.
The monthly model of all the reservoirs showed
the better performance than the weekly and daily
models. The R 2 of monthly, weekly and daily
models were 0.95, 0.88 and 0.83, respectively, for
Mun Bon; 0.90, 0.73 and 0.74 for Lam Chae; 0.93,
0.89 and 0.90 for Lam Phra Phloeng and 0.97, 0.96,