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(864) 201-8679, www.advdmi.com
Visualize Complexity, Discover Solutions,
Shatter Limits
Data Mining an Data Mining an Expansive Expansive
Groundwater SystemGroundwater System
Presents…..!Presents…..!
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Advanced Data Mining (ADMAdvanced Data Mining (ADMii) has ) has developed unique Data Mining developed unique Data Mining technology for modeling natural technology for modeling natural systems. This video demonstrates its systems. This video demonstrates its application to an expansive application to an expansive groundwater system.groundwater system.
Data Mining extracts valuable Data Mining extracts valuable knowledge from large amounts of knowledge from large amounts of data. It employs advanced methods data. It employs advanced methods from several scientific disciplines.from several scientific disciplines.
The groundwater The groundwater system of interest is system of interest is the Upper Floridian the Upper Floridian Aquifer in the Aquifer in the Suwannee River Suwannee River ValleyValley
This system is approximately 100 x This system is approximately 100 x 120 miles with a maximum surface 120 miles with a maximum surface elevation of 220 feet.elevation of 220 feet.
The following illustration shows its The following illustration shows its topography. Land elevation is topography. Land elevation is indicated by the key at left. The indicated by the key at left. The path of the Suwannee River can be path of the Suwannee River can be readily seen near the center.readily seen near the center.
Suwannee River Valley
This groundwater resource is This groundwater resource is managed by the Suwannee managed by the Suwannee River Management District in River Management District in Live Oak, Florida.Live Oak, Florida.
They maintain a network of They maintain a network of several hundred wells that several hundred wells that provide data about the provide data about the behavior of the aquifer.behavior of the aquifer.
The following shows the The following shows the locations of wells for which locations of wells for which there are significant amounts of there are significant amounts of data.data.
Note that some areas have Note that some areas have several wells clustered together several wells clustered together and that others have few or and that others have few or none. none.
Gulf of MexicoGulf of Mexico
Histories for a few wells go back to Histories for a few wells go back to the 1940’s, however, the record the 1940’s, however, the record prior to 1982 is sparse. prior to 1982 is sparse.
The vertical blue streaks in the The vertical blue streaks in the following 3D image show the following 3D image show the historical range of individual wells. historical range of individual wells. Together they show the dynamic Together they show the dynamic range of the aquifer. range of the aquifer.
Elevation above Sea Level
N
WS
EGulf of Mexico
Collectively, these data Collectively, these data comprise a vast, but unwieldy comprise a vast, but unwieldy source of potentially valuable source of potentially valuable knowledge.knowledge.
We researched how Data We researched how Data Mining could be used to extract Mining could be used to extract knowledge about this complex knowledge about this complex system and others like it. system and others like it.
Computer models of groundwater Computer models of groundwater systems are important tools for systems are important tools for learning how these invaluable learning how these invaluable resources are affected by weather, resources are affected by weather, pumping and land development.pumping and land development.
Our goal was to use Data Mining to Our goal was to use Data Mining to create an accurate model of the create an accurate model of the aquifer’s water level.aquifer’s water level.
The following is a 25 x 30 mile The following is a 25 x 30 mile detail from near the center of detail from near the center of the system. It shows the the system. It shows the positions of 22 wells and their positions of 22 wells and their histories since 1982.histories since 1982.
Note that the two groups of Note that the two groups of circled wells clearly behave circled wells clearly behave differently from each other.differently from each other.
350000
370000
390000
410000
430000
450000
470000
490000
2360000 2380000 2400000 2420000 2440000 2460000 2480000 2500000
25
mile
s
30 milesS
uw
ann
ee River
Because the wells exhibited so Because the wells exhibited so many different behaviors, it was many different behaviors, it was necessary to group them into necessary to group them into “classes”. Wells assigned to a “classes”. Wells assigned to a particular class behave similarly.particular class behave similarly.
Data Mining Data Mining optimallyoptimally determined determined the number of classes and how the number of classes and how the wells would be assigned.the wells would be assigned.
The following shows that 12 The following shows that 12 classes were used and how the classes were used and how the wells were assigned. The classes wells were assigned. The classes are numbered 1 to 12.are numbered 1 to 12.
It was surprising how some It was surprising how some classes are distributed over a classes are distributed over a broad area and are intermingled broad area and are intermingled with other classes.with other classes.
Closer inspection showed that Closer inspection showed that Data Mining did indeed optimally Data Mining did indeed optimally assign the wells.assign the wells.
The following shows the The following shows the “normalized” histories of wells “normalized” histories of wells for two of the classes.for two of the classes.
Note the seasonal variability.Note the seasonal variability.
History from April 1982 to October 1998
The next Data Mining task was to The next Data Mining task was to assign aquifer locations to the 12 assign aquifer locations to the 12 classes.classes.
Locations were optimally assigned Locations were optimally assigned based on their topological based on their topological characteristics and proximity to characteristics and proximity to wells whose classes were known. wells whose classes were known.
Results are shown in the following.Results are shown in the following.
The next Data Mining task was to The next Data Mining task was to create a water level model for create a water level model for each class. Every location was each class. Every location was assigned to a class, and therefore, assigned to a class, and therefore, a model.a model.
Inputs to each model were the Inputs to each model were the characteristics of a location and characteristics of a location and water levels of selected wells. The water levels of selected wells. The output was the predicted water output was the predicted water level of the location. level of the location.
The models are very accurate. The models are very accurate. Accuracy can be checked at Accuracy can be checked at locations where there are well locations where there are well histories.histories.
The following compares The following compares predictions to actual histories for predictions to actual histories for wells of four different classes. The wells of four different classes. The water levels are normalized to water levels are normalized to land surface elevation. land surface elevation.
History from April 1982 to October 1998No
rma
lize
d W
ate
r L
eve
l abo
ve S
ea
Le
vel
Actual Prediction
Class 1Class 1
History from April 1982 to October 1998No
rma
lize
d W
ate
r L
eve
l abo
ve S
ea
Le
vel
Class 3Class 3Actual Prediction
History from April 1982 to October 1998No
rma
lize
d W
ate
r L
eve
l abo
ve S
ea
Le
vel
Actual Prediction
Class 6Class 6
History from April 1982 to October 1998No
rma
lize
d W
ate
r L
eve
l abo
ve S
ea
Le
vel
Actual Prediction
Class 10Class 10
The “model” of the aquifer is The “model” of the aquifer is actually a collection of models, one actually a collection of models, one for each class. A computer program for each class. A computer program was created that integrates the was created that integrates the models, a history database, and a models, a history database, and a graphical user interface.graphical user interface.
The following shows a long term The following shows a long term simulation of the aquifer’s water simulation of the aquifer’s water level generated by the model. Note level generated by the model. Note the color key at right, and that time the color key at right, and that time is reversed.is reversed.
Often multi-dimensional Often multi-dimensional visualization reveals important visualization reveals important information that would otherwise information that would otherwise go unnoticed. ADMgo unnoticed. ADMii has world- has world-class capabilities in advanced class capabilities in advanced visualization technology.visualization technology.
The following shows the model’s The following shows the model’s prediction of the upper range prediction of the upper range (ceiling) of the aquifer. The (ceiling) of the aquifer. The vertical scale is exaggerated to vertical scale is exaggerated to show details.show details.
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Gulf of Mexico
Max elevation above sea level ~ 180 feet
The following compares the The following compares the model’s prediction of the model’s prediction of the “floor” and “ceiling” of the “floor” and “ceiling” of the aquifer.aquifer.
Ceiling
Gulf of Mexico
Floor
Gulf of Mexico
Ceiling
Gulf of Mexico
Floor
Gulf of Mexico
The following shows the The following shows the predicted aquifer level for the predicted aquifer level for the period from January 1995 to period from January 1995 to October 1998.October 1998.
Note the spatially Note the spatially asynchronous motions caused asynchronous motions caused by variability in rainfall and by variability in rainfall and the Suwannee River’s stage.the Suwannee River’s stage.
Date: 01/01/95
Gulf of Mexico
Date: 02/01/95
Gulf of Mexico
Date: 03/01/95
Gulf of Mexico
Date: 04/01/95
Gulf of Mexico
Date: 05/01/95
Gulf of Mexico
Date: 06/01/95
Gulf of Mexico
Date: 07/01/95
Gulf of Mexico
Date: 08/01/95
Gulf of Mexico
Date: 09/01/95
Gulf of Mexico
Date: 10/01/95
Gulf of Mexico
Date: 11/01/95
Gulf of Mexico
Date: 12/01/95
Gulf of Mexico
Date: 01/01/96
Gulf of Mexico
Date: 01/31/96
Gulf of Mexico
Date: 03/01/96
Gulf of Mexico
Date: 03/31/96
Gulf of Mexico
Date: 04/30/96
Gulf of Mexico
Date: 05/30/96
Gulf of Mexico
Date: 06/29/96
Gulf of Mexico
Date: 07/29/96
Gulf of Mexico
Date: 08/28/96
Gulf of Mexico
Date: 10/01/96
Gulf of Mexico
Date: 11/01/96
Gulf of Mexico
Date: 12/01/96
Gulf of Mexico
Date: 01/01/97
Gulf of Mexico
Date: 02/01/97
Gulf of Mexico
Date: 03/01/97
Gulf of Mexico
Date: 04/01/97
Gulf of Mexico
Date: 05/01/97
Gulf of Mexico
Date: 06/01/97
Gulf of Mexico
Date: 07/01/97
Gulf of Mexico
Date: 08/01/97
Gulf of Mexico
Date: 09/01/97
Gulf of Mexico
Date: 10/01/97
Gulf of Mexico
Date: 11/01/97
Gulf of Mexico
Date: 12/01/97
Gulf of Mexico
Date: 01/01/98
Gulf of Mexico
Date: 02/01/98
Gulf of Mexico
Date: 03/01/98
Gulf of Mexico
Date: 04/01/98
Gulf of Mexico
Date: 05/01/98
Gulf of Mexico
Date: 06/01/98
Gulf of Mexico
Date: 07/01/98
Gulf of Mexico
Date: 08/01/98
Gulf of Mexico
Date: 09/01/98
Gulf of Mexico
Date: 10/01/98
Gulf of Mexico
This Data Mining-based model This Data Mining-based model required about 10 weeks to required about 10 weeks to develop. develop.
A conventional finite-difference A conventional finite-difference model of the same natural system model of the same natural system was developed by a government was developed by a government agency. It took over 3 years to agency. It took over 3 years to complete! It is much less accurate complete! It is much less accurate at predicting water level.at predicting water level.
ConclusionConclusionss
Data Mining is incredibly Data Mining is incredibly powerful for extracting powerful for extracting knowledge about complex knowledge about complex natural systems from databases.natural systems from databases.
The models can be more The models can be more accurate than traditional accurate than traditional approaches, and require much approaches, and require much less time to develop.less time to develop.
ConclusionConclusionss
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