The document describes how an organization used data mining techniques to create an accurate groundwater model of the Upper Floridian Aquifer in the Suwannee River Valley. Wells in the aquifer system were grouped into 12 classes based on their water level behaviors. Locations were assigned classes and 12 models were created, one for each class. The integrated model accurately predicted water levels across the system and revealed patterns over time. The data mining approach developed a more accurate model in 10 weeks compared to over 3 years for a conventional model.
Intervento del Dott. Antonio Navarra Presidente del Centro Euro-Mediterraneo per i Cambiamenti Climatici durante il convegno del 9 ottobre tenutosi a Roma presso la Prfotomoteca del Campidoglio
To prevent losing water resources and wetlands, and conserve existing wetlands
ecosystem for ecosystem and biodiversity services, good, wetlands habitats forstart
any sustainable development programs, it is necessary to detect, monitor and
inventory water resources and their surround uplands. Recently, AL-Razaza Lake
suffer from a critical situation because of the decreasing in the water level and
increase a salinity. We have propose a method to monitor and model the spatial and
multi-temporal changes of AL-Razaza Lake in the period 1992–2018. This study
includes pre-processing, processing and post-processing stages. In Addition, a
supervised classification was used to classify the satellite images. Validation result
reveals that the overall accuracies and kappa coefficients of the supervised
classifications were 88, 90.79, 95.94 and 87.67 respectively, and 82%, 86%, 93% and
79% respectively. The results showed that the percentage change was significant
during this period, such that the decreased surface area was from 1313.87 km2 in
1992 to 224.85 km2 in 201.The noticeable results show the rapidly decreasing in the
Lake area by 82.8% with area about 1089.02 km2 over the last three decades. All the
dehydration extended area of the Lake was replaced by soil.
What is seismic data? How is it collected? How much does it cost? How does it relate to oil and gas exploration? Why does a company need to collect seismic data on your land?
Learn the answers to these questions and more with A Seismic Oil and Gas Primer from Hill Geophysical Consulting.
What's Under Your Feet? Discover Now at http://HillGeo.com.
Hill Geophysical Consulting: Integrating Geophysical Technology with Geology.
Ben Kirtman, Director, Cooperative Institute for Marine & Atmospheric Studies, University of Miami, Rosentiel School for Marine and Atmospheric Science
UCAR Congressional Briefing - April 2018
DSD-INT 2017 Groundwater system analysis using isotope measurement data, San ...Deltares
Presentation by Cinthya Gómez Castro (Costa Rica) at the iMOD International User Day, during Delft Software Days - Edition 2017. Tuesday, 31 October 2017, Delft.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Why plan for growth and change, when it seems so much easier to simply react?
When there is a distinct and shared vision for your community - when residents, businesses and local government anticipate a sustainable town with cohesive and thriving neighborhoods - you have the power to conserve your beautiful natural spaces, enhance your existing downtown or Main Street, enable rural areas to be productive and prosperous, and save money through efficient use of existing infrastructure.
This is the dollars and sense of smart growth.
Success is clearly visible in Maine, from the creation of a community-built senior housing complex and health center in Fort Fairfield to conservation easements creating Forever Farms to Rockland's revitalized downtown. Communities have options. We have the power to manage our own responses to growth and change.
After all, “Planning is a process of choosing among those many options. If we do not choose to plan, then we choose to have others plan for us.” - Richard I. Winwood
And in the end, this means that our children and their children will choose to make Maine home and our economy will provide the opportunities to do so.
The Summit offers you a wonderful opportunity to be a part of the transformative change in Maine that we’ve seen these gatherings produce. We encourage you to consider the value of being actively involved in growing Maine’s economy and protecting the reasons we choose to live here.
This poster was presented at the 2015 Water Reuse Symposium in Seattle and represents ADMi's analysis of data to determine which water quality parameters have the greatest impact on RO membrane fouling.
Intervento del Dott. Antonio Navarra Presidente del Centro Euro-Mediterraneo per i Cambiamenti Climatici durante il convegno del 9 ottobre tenutosi a Roma presso la Prfotomoteca del Campidoglio
To prevent losing water resources and wetlands, and conserve existing wetlands
ecosystem for ecosystem and biodiversity services, good, wetlands habitats forstart
any sustainable development programs, it is necessary to detect, monitor and
inventory water resources and their surround uplands. Recently, AL-Razaza Lake
suffer from a critical situation because of the decreasing in the water level and
increase a salinity. We have propose a method to monitor and model the spatial and
multi-temporal changes of AL-Razaza Lake in the period 1992–2018. This study
includes pre-processing, processing and post-processing stages. In Addition, a
supervised classification was used to classify the satellite images. Validation result
reveals that the overall accuracies and kappa coefficients of the supervised
classifications were 88, 90.79, 95.94 and 87.67 respectively, and 82%, 86%, 93% and
79% respectively. The results showed that the percentage change was significant
during this period, such that the decreased surface area was from 1313.87 km2 in
1992 to 224.85 km2 in 201.The noticeable results show the rapidly decreasing in the
Lake area by 82.8% with area about 1089.02 km2 over the last three decades. All the
dehydration extended area of the Lake was replaced by soil.
What is seismic data? How is it collected? How much does it cost? How does it relate to oil and gas exploration? Why does a company need to collect seismic data on your land?
Learn the answers to these questions and more with A Seismic Oil and Gas Primer from Hill Geophysical Consulting.
What's Under Your Feet? Discover Now at http://HillGeo.com.
Hill Geophysical Consulting: Integrating Geophysical Technology with Geology.
Ben Kirtman, Director, Cooperative Institute for Marine & Atmospheric Studies, University of Miami, Rosentiel School for Marine and Atmospheric Science
UCAR Congressional Briefing - April 2018
DSD-INT 2017 Groundwater system analysis using isotope measurement data, San ...Deltares
Presentation by Cinthya Gómez Castro (Costa Rica) at the iMOD International User Day, during Delft Software Days - Edition 2017. Tuesday, 31 October 2017, Delft.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Sea level rise and storm surge tools and datasets supporting Municipal Resili...GrowSmart Maine
Why plan for growth and change, when it seems so much easier to simply react?
When there is a distinct and shared vision for your community - when residents, businesses and local government anticipate a sustainable town with cohesive and thriving neighborhoods - you have the power to conserve your beautiful natural spaces, enhance your existing downtown or Main Street, enable rural areas to be productive and prosperous, and save money through efficient use of existing infrastructure.
This is the dollars and sense of smart growth.
Success is clearly visible in Maine, from the creation of a community-built senior housing complex and health center in Fort Fairfield to conservation easements creating Forever Farms to Rockland's revitalized downtown. Communities have options. We have the power to manage our own responses to growth and change.
After all, “Planning is a process of choosing among those many options. If we do not choose to plan, then we choose to have others plan for us.” - Richard I. Winwood
And in the end, this means that our children and their children will choose to make Maine home and our economy will provide the opportunities to do so.
The Summit offers you a wonderful opportunity to be a part of the transformative change in Maine that we’ve seen these gatherings produce. We encourage you to consider the value of being actively involved in growing Maine’s economy and protecting the reasons we choose to live here.
This poster was presented at the 2015 Water Reuse Symposium in Seattle and represents ADMi's analysis of data to determine which water quality parameters have the greatest impact on RO membrane fouling.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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My slides at Nordic Testing Days 6.6.2024
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Bob Boule
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Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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2. Presents…..!
Data Mining an
Expansive Groundwater
System
3. Press your Pause key to stop/
restart this presentation at any
time.
Press your Esc key to end it.
4. Advanced Data Mining (ADMi) has
developed unique Data Mining technology
for modeling natural systems. This video
demonstrates its application to an
expansive groundwater system.
Data Mining extracts valuable knowledge
from large amounts of data. It employs
advanced methods from several scientific
disciplines.
6. This system is approximately 100 x 120
miles with a maximum surface elevation
of 220 feet.
The following illustration shows its
topography. Land elevation is indicated
by the key at left. The path of the
Suwannee River can be readily seen
near the center.
9. This groundwater resource is
managed by the Suwannee River
Management District in Live Oak,
Florida.
They maintain a network of several
hundred wells that provide data
about the behavior of the aquifer.
10. The following shows the locations of
wells for which there are significant
amounts of data.
Note that some areas have several
wells clustered together and that
others have few or none.
12. Histories for a few wells go back to the
1940’s, however, the record prior to
1982 is sparse.
The vertical blue streaks in the
following 3D image show the historical
range of individual wells. Together they
show the dynamic range of the aquifer.
14. Collectively, these data comprise a
vast, but unwieldy source of
potentially valuable knowledge.
We researched how Data Mining
could be used to extract knowledge
about this complex system and
others like it.
15. Computer models of groundwater
systems are important tools for learning
how these invaluable resources are
affected by weather, pumping and land
development.
Our goal was to use Data Mining to
create an accurate model of the
aquifer’s water level.
16. The following is a 25 x 30 mile
detail from near the center of the
system. It shows the positions of 22
wells and their histories since 1982.
Note that the two groups of circled
wells clearly behave differently from
each other.
17. 490000
470000
450000
Suw
25 miles
430000
anne
e
Rive
410000
r
390000
370000
350000
2360000 2380000 2400000 2420000 2440000 2460000 2480000 2500000
30 miles
18. Because the wells exhibited so many
different behaviors, it was necessary
to group them into “classes”. Wells
assigned to a particular class behave
similarly.
Data Mining optimally determined the
number of classes and how the wells
would be assigned.
19. The following shows that 12 classes
were used and how the wells were
assigned. The classes are numbered
1 to 12.
It was surprising how some classes
are distributed over a broad area and
are intermingled with other classes.
20.
21. Closer inspection showed that Data
Mining did indeed optimally assign
the wells.
The following shows the “normalized”
histories of wells for two of the
classes.
Note the seasonal variability.
23. The next Data Mining task was to assign
aquifer locations to the 12 classes.
Locations were optimally assigned
based on their topological
characteristics and proximity to wells
whose classes were known.
Results are shown in the following.
24.
25. The next Data Mining task was to
create a water level model for each
class. Every location was assigned to
a class, and therefore, a model.
Inputs to each model were the
characteristics of a location and water
levels of selected wells. The output
was the predicted water level of the
location.
26. The models are very accurate.
Accuracy can be checked at locations
where there are well histories.
The following compares predictions to
actual histories for wells of four
different classes. The water levels are
normalized to land surface elevation.
27. Normalized Water Level above Sea Level Class 1
Actual
Prediction
History from April 1982 to October 1998
28. Normalized Water Level above Sea Level Class 3
Actual
Prediction
History from April 1982 to October 1998
29. Normalized Water Level above Sea Level
Class 6
Actual
Prediction
History from April 1982 to October 1998
30. Normalized Water Level above Sea Level Class 10
Actual
Prediction
History from April 1982 to October 1998
31. The “model” of the aquifer is actually a
collection of models, one for each class.
A computer program was created that
integrates the models, a history database,
and a graphical user interface.
The following shows a long term
simulation of the aquifer’s water level
generated by the model. Note the color
key at right, and that time is reversed.
32.
33.
34.
35.
36.
37.
38.
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42.
43.
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46.
47.
48.
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62.
63.
64.
65.
66.
67.
68. Often multi-dimensional visualization
reveals important information that
would otherwise go unnoticed. ADMi
has world-class capabilities in
advanced visualization technology.
The following shows the model’s
prediction of the upper range (ceiling)
of the aquifer. The vertical scale is
exaggerated to show details.
85. The following shows the predicted
aquifer level for the period from
January 1995 to October 1998.
Note the spatially asynchronous
motions caused by variability in
rainfall and the Suwannee River’s
stage.
132. Conclusion
s
This Data Mining-based model required
about 10 weeks to develop.
A conventional finite-difference model of
the same natural system was developed
by a government agency. It took over 3
years to complete! It is much less
accurate at predicting water level.
133. Conclusion
s
Data Mining is incredibly powerful for
extracting knowledge about complex
natural systems from databases.
The models can be more accurate
than traditional approaches, and
require much less time to develop.