dawn lalmansingh, philippe tissot, richard hay texas a&m university – corpus christi
DESCRIPTION
Neural Network Modeling of Spring Levels Linked to a Karst Aquifer: Case Study of the Comal Springs. Dawn Lalmansingh, Philippe Tissot, Richard Hay Texas A&M University – Corpus Christi. Introduction. Importance of modeling Comal Springs baseflow What is neural network modeling? - PowerPoint PPT PresentationTRANSCRIPT
Neural Network Modeling of Spring Levels Linked to a Karst Aquifer: Case Study of the Comal Springs
Dawn Lalmansingh, Philippe Tissot, Richard Hay
Texas A&M University – Corpus Christi
Introduction
Importance of modeling Comal Springs baseflow
What is neural network modeling?
Neural Network Design strategy
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#
Study Area
NEW BRAUNFELS
COMAL
GUADALUPE
Gua
d alu
pe
R
iver
NEW BRAUNFELS
08169000
Legend
# stream gages
" precipitation stationsUrban Areas
County Boundary
Drainage Area
Recharge Zone
Artesian Zone
streamsHighways
0 1 2 3 40.5
Miles
§̈¦35
Comal County
Comal Springs
Comal River
³G
uadalupe River
12
3
Inputs to Model
Baseflow from gage station
Precipitation from Stations 1, 2, and 3
Historical Background
Edwards Aquifer – principal source of
water for ~ 2 million people
Comal Springs – largest spring system
in aquifer Declining flow over past 3 decades
Provides habitat for 4 endangered species
Survival depends on sustaining minimum flow
Background cont’d
Current groundwater models forecast
dry spells for different periods of time. Major and minor droughts
Lead to extinction of endangered species
Alternate water resources
Neural Network – alternative model Accurately forecast spring levels
Neural Network Modeling
Started in the 60’sKey innovation in the late 80’s: Backpropagation learning algorithmsNumber of applications has grown rapidly in the 90’s especially financial applicationsGrowing number of publications presenting environmental applications
Neural Network Features
Non linear modeling capability
Generic modeling capability
Robustness to noisy data
Ability for dynamic learning
Requires availability of high density of data
Neural Network Forecasting of Spring
Levels
Philippe Tissot - 2000
H (t+i)
Output LayerHidden Layer
Precipitation 1 History
Baseflow History
Precipitation 3 History
Precipitation 2 History
Input Layer
Spring Level Forecast
(a1,ixi)
b1
b2
(X1+b1)
b3
(X2+b2)
(X3+b3)
(a2,ixi)
(a3,ixi)
Data
Daily baseflow and precipitation data
spanning 50 years (01/01/1950-12/31/2000)
HYSEP – separated streamflow data into
baseflow and surface runoff components
Separate into 5 sets of 10 years
Train on 1 set and test on other 4
Compare results to persistence model
Neural Network Design Strategy
Find optimum number of previous days
of baseflow as inputs to model.
Find optimum number of previous days
of precipitation as inputs to model.
Find how many neurons provide the
best neural network performance.
Target Error Criterion and Root Mean Square Error
Target Error Criterion – percentage of model’s results that fall within 20% of the measured baseflow values
Root Mean Square Error (RMSE) – overall error of a sampling distribution of a group with n cases in a group
Target Error Criterion with Increasing Previous Baseflow Days
82 %
84 %
86 %
88 %
90 %
92 %
94 %
0 10 20 30 40 50 60
# of Days
Tar
get
Err
or
Cri
teri
on
(20
%)
10-Day Forecast
30-Day Forecast
RMSE with Increasing Previous Baseflow Days
0
5
10
15
20
25
30
35
40
0 10 20 30 40 50 60
# of Days
RM
SE
(cf
s)
10-Day Forecast
30-Day Forecast
Target Error Criterion with Addition of Precipitation Station
84 %85 %86 %87 %88 %89 %90 %91 %92 %93 %94 %
0 10 20 30 40 50 60
# of Days
Tar
get
Err
or
Cri
teri
on
(20
%)
10-Day Forecast
30-Day Forecast
RMSE with Addition of Precipitation Station
0
5
10
15
20
25
30
35
40
0 10 20 30 40 50 60
# of Days
RM
SE
(cf
s)
10-Day Forecast
30-Day Forecast
Target Error Criterion with Increasing Neurons in Hidden Layer
82 %
84 %
86 %
88 %
90 %
92 %
94 %
96 %
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5# of Neurons
Tar
get
Err
or
Cri
teri
on
(20
%)
10-Day Forecast
30-Day Forecast
RMSE with Increasing Neurons in Hidden Layer
0
50
100
150
200
250
0 1 2 3 4 5
# of Neurons
RM
SE
(cf
s)
10-Day Forecast
30-Day Forecast
Comparison of Target Error Criterion
0 %
20 %
40 %
60 %
80 %
100 %
120 %
0 10 20 30 40 50 60
Forecast Days
Tar
get
Err
or
Cri
teri
on
(2
0%)
ANN Model
Persistence Model
RMSE Comparison of ANN and Persistence Models
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Forecast Days
RM
SE
(cf
s)
ANN Model
Persistence Model
Comparison of Target Error Criterion and RMSE through Neural Network
Design Process
10-Day Optimization 30-Day OptimizationBaseflow 93.28 84.60
Precipitation 93.48 84.55Neurons 94.30 85.29
Persistence 95.62 86.93
Target Error Criterion
10-Day Optimization 30-Day OptimizationBaseflow 21.83 34.73
Precipitation 21.58 35.23Neurons 20.01 67.03
Persistence 20.19 32.99
RMSE
Conclusion
Trained & tested ANN over 50 years of data
Best model – 3 days baseflow, 5 days
precipitation, and 2 neurons
Main problem – spikes
Slightly underperforming persistence model
Explore other inputs