This document outlines a research project to develop improved models for forecasting reservoir inflows in the incremental basin of Itaipu using data-driven and hybrid techniques. The objectives are to create semi-distributed hydrological, artificial neural network, and hybrid models and evaluate their performance on short and medium term predictions. Methodologies include wavelet decomposition of inputs to ANNs, sensitivity analysis of ANN structures, and data preprocessing. Results will be made available through an internet-based platform to aid operational forecasting and allow further testing. Limitations include increased computational time for wavelet-ANN models and data availability challenges.

1. 1
DATA DRIVEN AND HYBRID TECHNIQUES TO
PREDICT THE RESERVOIR INFLOWS:
Supervisor:
Dr.Dimitri P. Solomantine
Mentor:
Dr. Gerald Corzo
Daniel Alejandro Vázquez Bado

2. Outline
 Problem definition
 Objectives
 Practical Value
 Innovation
 Methodology
 Results & Discussions
 Work Plan
2

3. 33
Study Area
BACKGROUND (1) Some Characteristis
The drainage area is 820,000 km2of
which
70% are in the upper basin,
strongly regularized by more
than 30 dams
the remaining 30%, is known
as incremental basin of Itaipu
Incremental basin area is 235,000
km2
Main rivers in this area are the Ivaí,
Piquiri and Iguazu rivers in Brazil and
Monday, Acaray rivers in Paraguay .

4. PROBLEM DEFINITION
Current State
o It is stated by the OPSH.DT (Hydrological Division of
Itaipu) the presence of a calibrated hydrological model,
however this model was never operational, probably
because of undesired performance
o Hydrological previsions are based mainly on the application
of fuzzy logic or experience/expert knowledge, however
due to the scale and spatial variability (rainfall mainly) of
the catchment is difficult to provide reliable hydrological
forecasts.
4

5. OBJECTIVES OF THE RESEARCH (1)
Main Objective
o The main objective of this research is to build a superior
inflow forecast model for the incremental basin of Itaipu,
superior in terms of performance (accuracy, generalization,
etc) for short and medium term predictions, giving
predictions.
5

6. OBJECTIVES OF THE RESEARCH (2)
Specific Objectives
o To develop the following models with the objective of
proving the hypothesis of a superior hybrid model
performance
Model A: Creation of semi-distributed hydrological
model for the incremental basin of Itaipu.
Model B: Creation of traditional artificial neural
network for the incremental basin of Itaipu.
Model C: Develop a hybrid model by using an
appropriate combination of sub-components, explore
data pre-processing methods for such analysis.
6

7. OBJECTIVES OF THE RESEARCH (3)
Specific Objectives
o Develop an internet-based (with the previous models
couple and automated) to dissertate results and allow the
model to be tested by many users, also intended for
possible real time application.
7

8. PRACTICAL VALUE & INNOVATIONS
o Important positive impact for the OPSH.DT, an additional
tool to help improving the hydrological predictions.
o GIU is going to be provided for the user/operator and
results are going to be showed on the web (internet
based).
o This platform also allows to be rigorously tested by many
users and therefore will facilitate the acceptance of such
proposed model.
8

9. 9
RESEARCH METHODOLOGY:
Meteo Data Hydro Da
Data
Validation
ANN model
Optimize structure
Hybrid Model
Pre-process
Inputs
Combine
ANN/Wavelets
Optimize structure & noise
filtering techniques
Daily
Forecast
Monthly
forecast
Show hydrological predictions
for both models
Evaluate models and select the best

10. 10
ANN
-Discharge time series (t)
-Precip(t+1)
-Precip(t)
-Precip(t+1)
to be optimized
-Discharge time series (t+1)
Regular ANN network components
APPROACH 1: ANN
Hidden layerInput layer Output layer
Figure 4.5 Schematization of a typical ANN structure

11. 11
WAVELET
ANN
-Discharge decomposition (t)
-Precip dec(t+1)
-Precip dec(t)
-Precip dec(t+1)
to be optimized
-Discharge time series (t+1)
APPROACH 2: WAVELETS DECOMPOSITIONS AS
ANN’S INPUTS
Hidden layerInput layer Output layer
Figure 4.5 Schematization of the coupled Wavelet/ANN structure

12. 12
APPROACH 3: IMPROVED WAVELET ANN

13. 3
2
1
segment 1
segment 2
Br


original signal
de-noised signal---------------
---------------
Brief analysis:
 segment 1: if we observe the original signal
along this segment [140, 260], discharge is
increasing as a rainfall response somewhere,
so errors are more prone to occur during these
period and they are going to be higher in
magnitude, this is observed in the detail part
of the signal, small values are considered as
noise and they are going to be removed by
the threshold criteria. (obs: high values in
details do not mean high error or high noise)
 segment2: during low flows and no flows
egment 1
higher reduction of noises
along the segment
original signal
denoised signal---------------
---------------
t 2
along this segment [140, 260], discharge is
increasing as a rainfall response somewhere,
so errors are more prone to occur during these
period and they are going to be higher in
magnitude, this is observed in the detail part
of the signal, small values are considered as
noise and they are going to be removed by
the threshold criteria. (obs: high values in
details do not mean high error or high noise)
 segment2: during low flows and no flows
noises are going to be smaller and they are
also reflected in the details, the threshold
criteria is not finding errors or noise to
suppress.
low or zero reduction of
noises along the segment
2nd segment

14. 14
RESULTS (1) COMPARISON BETWEEN ANN
& WANN (BAGMATI CASE ANALYSIS)
Training Verification
NSC: 0.8749 0.8199
Cor: 0.9355 0.9159
RMSE: 80.7868 142.6434
PERS: 0.7321 0.6562
Table 4.1 Performance of the regular Artificial Neural
Network
Training Verification
NSC: 0.96 0.96
Cor: 0.98 0.98
RMSE: 48.37 68.48
PERS: 0.92 0.9
Table 4.2 Performance of the Wavelet Neural
Network (db4 family, decomposition level 2)
Figure 4.6 WANN verification data set performance for different
decomposition levels and wavelet families.

15. 15
Figure 4.8 Sensitivity analysis of No of epochs Figure 4.8 Efficiency in terms of time of both models
Figure 4.7 Sensitivity analysis of No of Hidden Nodes
RESULTS (2):
SENSITIVITY ANALYSIS
OF ANN STRUCTURE

16. 16
WAVELETS IN OTHER BLACK BOX MODELS (1)
Figure 5.1 Improvement in performance for different DDMs when coupling
with neural network

17. 17
WAVELETS IN OTHER BLACK BOX MODELS (2)
Figure 5.2 Improvement in performance for different DDMs when coupling
with neural network

18. 18
OTHER ALTERNATIVES WITH WAVELETS
o Signal Denoising
osoft thresholding: all coefficients below a
certain threshold are set to zero and those
whose magnitude are greater than the
threshold are shrunk by that amount, this
method recognizes that the coefficients
contain in both signal and noise, and
attempts to isolate them.
ohard thresholding: retains just the values
above the threshold
some methods to be studied
othreshold selection rule based on
Stein's Unbiased Estimate of Risk
Minimax, uses a fixed threshold
chosen to yield minimax
performance for mean square error
against an ideal procedure
Use denoised
signal as
input for
the DDM

19. 19
Station Name City River
Área
km2
Type
Zero de la
Coordenadas
Planas
Regla (m) Este Norte
Porto São
José
São Pedro do
Paraná
Río Paraná 674 P / F STH 231.958 790,021.51 7,484,951.80
Novo Balsa
Santa Maria
Palotina Río Piquiri 21 P / F STH 234.741 830,869.84 7,320,810.80
Porto Paraíso
do Norte
Rondon Río Ivai 28,427 P / F STH 246.302 943,480.73 7,413,841.81
Porto Caiuá Naviraí Río Paraná 717,814 P / F STH 226.851 836,342.48 7,423,106.03
Porto
Ivinhema
Ivinhema
Río
Ivinhema
31,905 P / F STH 242.167 857,165.60 7,520,737.35
Flórida Juti
Río
Amambay
7,252 P / F STH 254.186 749,764.61 7,457,688.68
Estrada do
Iguatemi
Iguatemi
Río
Iguatemi
6,832 P / F STH 235.709 747,067.57 7,373,368.24
Novo Porto
Taquara
Santa Isabel
do Ivaí
Río Ivai 34,432 P / F STH 229.955 877,189.34 7,429,826.65
Novo Porto 2 Nova Aurora Río Piquiri 12,124 P / F STH 280.715 889,646.85 7,298,604.28
Balsa do
Cantu
Altamira do
Paraná
Río Cantú 2,513 P / F STH 333 934,773.82 7,255,920.82
Ubiratã Ubiratã P STH --- 906,741.30 7,278,572.93
Palmital Palmital P STH --- 984,908.22 7,239,282.54
Ubá do Sul Lidianópolis Río Ivaí 12,701 P / F STH 361.554 1,047,174.60 7,330,641.25
Tereza
Cristina
Candido de
Abreu
Río Ivaí 3,572 P / F STH 473.502 1,092,592.18 7,241,116.07
Marquinho Marquinho P STH --- 978,091.57 7,214,274.97
Barbosa
Ferraz
Barbosa
Ferraz
Río
Curumbata
y
3,294 P / F STH 303.233
1,014,277.34 7,334,612.33
Manoel Ribas Manoel Ribas P STH --- 1,040,917.16 7,278,492.04
Porto
Guarani
Altamira do
Paraná Río Piquiri 4,233
P / F STH
338.097
928,071.69 7,243,269.01
µ 82,000 0 82,00041,000 Meters
DATA COLLETION: CASE STUDY - INCREMENTAL
BASIN OF ITAIPU

20. 20
QUALITY REQUIREMENTS: QUANTIFICATION OF
DATA GAPS
Figure 6.1 & Figure 6.2 Temporal availability of data for all available
stations

21. 21
"Rejected"
QUALITY REQUIREMENTS: CONSISTENCY ANALYSIS (1)
Figure 6.3 Double mass curve consistency test for stations Santa Maria and
Estrada do Iguatemi

22. 22
QUALITY REQUIREMENTS: CONSISTENCY ANALYSIS (2)

23. 23
QUALITY REQUIREMENTS: CONSISTENCY ANALYSIS (3)

24. 24
X
X
X
X
X X
X
X Rejected
X
Rio Amambay
Rio Piquiri
Rio Ivai
Rio Iguatemi
RioParana
RioIvinhema
SELECTED STATIONS
Figure 6.4 Summary of the selected and rejected stations

25. 25
INFILLING METHODS:
o Inverse Weighted Distance (IWD)
o PSD Daily Gridded Precipitation Observations (Science
Division, Earth System Research Laboratory)
o PSD Daily Gridded Precipitation Observations unbiased
o Power law transformation 𝑃∗
= 𝑎𝑃 𝑏
Figure 6.8 Infilling Methods performance

26. 26
Figure 6.8 Time series generated by the infilling procedures used compared
with the ground station

27. NEXT STEPS
27
o Make use of filled precipitation data as input for the
proposed forecast models in addition with the flow records
of the available stations. (Itaipu case study)
o Daily forecast
o Monthly forecast
o Seasonal forecast
o Develop an internet based model in order to dissertate the
results for other users to test

28. 28
LIMITATIONS:
o Although wavelet decomposition helps to increase model
performance it also increases the computational time
needed to run the model
o Rainfall network is considered poor, therefore data
imputations (infilling of gaps) are also inaccurate,
alternatives such as remote sensing can be tested and
then evaluated.
o The river is highly regulated, as a result the system
becomes highly nonlinear and consequently harder to
forecast