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(864) 201-8679, www.advdmi.com
Visualize Complexity, Discover Solutions, Shatter Limits
Data Mining an Data Mining an Expansive Groundwater Expansive Groundwater
SystemSystem
Presents…..!Presents…..!
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Advanced Data Mining (ADMAdvanced Data Mining (ADMii) has ) has developed unique Data Mining technology developed unique Data Mining technology for modeling natural systems. This video for modeling natural systems. This video demonstrates its application to an demonstrates its application to an expansive groundwater system.expansive groundwater system.
Data Mining extracts valuable knowledge Data Mining extracts valuable knowledge from large amounts of data. It employs from large amounts of data. It employs advanced methods from several scientific advanced methods from several scientific disciplines.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 ValleySuwannee River Valley
This system is approximately 100 x 120 This system is approximately 100 x 120 miles with a maximum surface elevation miles with a maximum surface elevation of 220 feet.of 220 feet.
The following illustration shows its The following illustration shows its topography. Land elevation is indicated topography. Land elevation is indicated by the key at left. The path of the by the key at left. The path of the Suwannee River can be readily seen Suwannee River can be readily seen near the center.near the center.
Suwannee River Valley
This groundwater resource is This groundwater resource is managed by the Suwannee River managed by the Suwannee River Management District in Live Oak, Management District in Live Oak, Florida.Florida.
They maintain a network of several They maintain a network of several hundred wells that provide data hundred wells that provide data about the behavior of the aquifer.about the behavior of the aquifer.
The following shows the locations of The following shows the locations of wells for which there are significant wells for which there are significant amounts of data.amounts of data.
Note that some areas have several Note that some areas have several wells clustered together and that wells clustered together and that others have few or none. others have few or none.
Gulf of MexicoGulf of Mexico
Histories for a few wells go back to the Histories for a few wells go back to the 1940’s, however, the record prior to 1940’s, however, the record prior to 1982 is sparse. 1982 is sparse.
The vertical blue streaks in the The vertical blue streaks in the following 3D image show the historical following 3D image show the historical range of individual wells. Together they range of individual wells. Together they show the dynamic range of the aquifer. show the dynamic range of the aquifer.
Elevation above Sea Level
N
WS
EGulf of Mexico
Collectively, these data comprise a Collectively, these data comprise a vast, but unwieldy source of vast, but unwieldy source of potentially valuable knowledge.potentially valuable knowledge.
We researched how Data Mining We researched how Data Mining could be used to extract knowledge could be used to extract knowledge about this complex system and about this complex system and others like it. others like it.
Computer models of groundwater Computer models of groundwater systems are important tools for learning systems are important tools for learning how these invaluable resources are how these invaluable resources are affected by weather, pumping and land affected by weather, pumping and land development.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 the detail from near the center of the system. It shows the positions of 22 system. It shows the positions of 22 wells and their histories since 1982.wells and their histories since 1982.
Note that the two groups of circled Note that the two groups of circled wells clearly behave differently from wells clearly behave differently from each other.each other.
350000
370000
390000
410000
430000
450000
470000
490000
2360000 2380000 2400000 2420000 2440000 2460000 2480000 2500000
25 m
iles
30 milesS
uw
ann
ee River
Because the wells exhibited so many Because the wells exhibited so many different behaviors, it was necessary different behaviors, it was necessary to group them into “classes”. Wells to group them into “classes”. Wells assigned to a particular class behave assigned to a particular class behave similarly.similarly.
Data Mining Data Mining optimallyoptimally determined the determined the number of classes and how the wells number of classes and how the wells would be assigned.would be assigned.
The following shows that 12 classes The following shows that 12 classes were used and how the wells were were used and how the wells were assigned. The classes are numbered assigned. The classes are numbered 1 to 12.1 to 12.
It was surprising how some classes It was surprising how some classes are distributed over a broad area and are distributed over a broad area and are intermingled with other classes.are intermingled with other classes.
Closer inspection showed that Data Closer inspection showed that Data Mining did indeed optimally assign Mining did indeed optimally assign the wells.the wells.
The following shows the “normalized” The following shows the “normalized” histories of wells for two of the histories of wells for two of the classes.classes.
Note the seasonal variability.Note the seasonal variability.
History from April 1982 to October 1998
The next Data Mining task was to assign The next Data Mining task was to assign aquifer locations to the 12 classes.aquifer locations to the 12 classes.
Locations were optimally assigned Locations were optimally assigned based on their topological based on their topological characteristics and proximity to wells characteristics and proximity to wells whose classes were known. 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 each create a water level model for each class. Every location was assigned to class. Every location was assigned to a class, and therefore, a model.a class, and therefore, a model.
Inputs to each model were the Inputs to each model were the characteristics of a location and water characteristics of a location and water levels of selected wells. The output levels of selected wells. The output was the predicted water level of the was the predicted water level of the location. location.
The models are very accurate. The models are very accurate. Accuracy can be checked at locations Accuracy can be checked at locations where there are well histories.where there are well histories.
The following compares predictions to The following compares predictions to actual histories for wells of four actual histories for wells of four different classes. The water levels are different classes. The water levels are normalized to land surface elevation. normalized to land surface elevation.
History from April 1982 to October 1998No
rmal
ize
d W
ater
Le
vel a
bo
ve S
ea
Le
vel
Actual Prediction
Class 1Class 1
History from April 1982 to October 1998No
rmal
ize
d W
ater
Le
vel a
bo
ve S
ea
Le
vel
Class 3Class 3Actual Prediction
History from April 1982 to October 1998No
rmal
ize
d W
ater
Le
vel a
bo
ve S
ea
Le
vel
Actual Prediction
Class 6Class 6
History from April 1982 to October 1998No
rmal
ize
d W
ater
Le
vel a
bo
ve S
ea
Le
vel
Actual Prediction
Class 10Class 10
The “model” of the aquifer is actually a The “model” of the aquifer is actually a collection of models, one for each class. collection of models, one for each class. A computer program was created that A computer program was created that integrates the models, a history database, integrates the models, a history database, and a graphical user interface.and a graphical user interface.
The following shows a long term The following shows a long term simulation of the aquifer’s water level simulation of the aquifer’s water level generated by the model. Note the color generated by the model. Note the color key at right, and that time is reversed.key at right, and that time is reversed.
Often multi-dimensional visualization Often multi-dimensional visualization reveals important information that reveals important information that would otherwise go unnoticed. ADMwould otherwise go unnoticed. ADMii has world-class capabilities in has world-class capabilities in advanced visualization technology.advanced visualization technology.
The following shows the model’s The following shows the model’s prediction of the upper range (ceiling) prediction of the upper range (ceiling) of the aquifer. The vertical scale is of the aquifer. The vertical scale is exaggerated to show details.exaggerated to 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 “floor” model’s prediction of the “floor” and “ceiling” of the aquifer.and “ceiling” of the aquifer.
Ceiling
Gulf of Mexico
Floor
Gulf of Mexico
Ceiling
Gulf of Mexico
Floor
Gulf of Mexico
The following shows the predicted The following shows the predicted aquifer level for the period from aquifer level for the period from January 1995 to October 1998.January 1995 to October 1998.
Note the spatially asynchronous Note the spatially asynchronous motions caused by variability in motions caused by variability in rainfall and the Suwannee River’s rainfall and the Suwannee River’s stage.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 required This Data Mining-based model required about 10 weeks to develop. about 10 weeks to develop.
A conventional finite-difference model of A conventional finite-difference model of the same natural system was developed the same natural system was developed by a government agency. It took over 3 by a government agency. It took over 3 years to complete! It is much less years to complete! It is much less accurate at predicting water level.accurate at predicting water level.
ConclusionConclusionss
Data Mining is incredibly powerful for Data Mining is incredibly powerful for extracting knowledge about complex extracting knowledge about complex natural systems from databases.natural systems from databases.
The models can be more accurate The models can be more accurate than traditional approaches, and than traditional approaches, and require much less time to develop.require much less time to develop.
ConclusionConclusionss
(864) 676-9790, info@AdvDataMining.com
Visualize Complexity, Discover Solutions, Shatter Limits
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