A Study on Task Scheduling in Could Data Centers for Energy Efficacy Ehsan Sharifi
Abstract: The increasing energy consumption of Physical Machines (PM) in cloud data centers is nowadays a major problem, it has a negative impact on the environment while at the same time increasing the operational costs of data centers. This fosters the development of more energy-efficient scheduling approaches. In this study, we study the barriers of knowledge in energy efficiency for cloud data centers.
Self-adaptive container monitoring with performance-aware Load-Shedding policies, by Rolando Brondolin, PhD student in System Architecture at Politecnico di Milano
Self-adaptive container monitoring with performance-aware Load-Shedding policies, by Rolando Brondolin, PhD student in System Architecture at Politecnico di Milano
RAMSES: Robust Analytic Models for Science at Extreme ScalesIan Foster
RAMSES: A new project in data-driven analytical modeling of distributed systems
RAMSES is a new DOE-funded project on the end-to-end analytical performance modeling of science workflows in extreme-scale science environments. It aims to link multiple threads of inquiry that have not, until now, been adequately connected: namely, first-principles performance modeling within individual sub-disciplines (e.g., networks, storage systems, applications), and data-driven methods for evaluating, calibrating, and synthesizing models of complex phenomena. What makes this fusion necessary is the drive to explain, predict, and optimize not just individual system components but complex end-to-end workflows. In this talk, I will introduce the goals of the project and some aspects of our technical approach.
A Study on Task Scheduling in Could Data Centers for Energy Efficacy Ehsan Sharifi
Abstract: The increasing energy consumption of Physical Machines (PM) in cloud data centers is nowadays a major problem, it has a negative impact on the environment while at the same time increasing the operational costs of data centers. This fosters the development of more energy-efficient scheduling approaches. In this study, we study the barriers of knowledge in energy efficiency for cloud data centers.
Self-adaptive container monitoring with performance-aware Load-Shedding policies, by Rolando Brondolin, PhD student in System Architecture at Politecnico di Milano
Self-adaptive container monitoring with performance-aware Load-Shedding policies, by Rolando Brondolin, PhD student in System Architecture at Politecnico di Milano
RAMSES: Robust Analytic Models for Science at Extreme ScalesIan Foster
RAMSES: A new project in data-driven analytical modeling of distributed systems
RAMSES is a new DOE-funded project on the end-to-end analytical performance modeling of science workflows in extreme-scale science environments. It aims to link multiple threads of inquiry that have not, until now, been adequately connected: namely, first-principles performance modeling within individual sub-disciplines (e.g., networks, storage systems, applications), and data-driven methods for evaluating, calibrating, and synthesizing models of complex phenomena. What makes this fusion necessary is the drive to explain, predict, and optimize not just individual system components but complex end-to-end workflows. In this talk, I will introduce the goals of the project and some aspects of our technical approach.
It is rather surprising that in software engineering, standard measurement units have yet to be
widely accepted and used. Every other engineering discipline has their own. By and large, effort
is the most commonly used parameter for measuring software initiatives. The problem of
course is that effort is not an independent variable – it depends on who is doing the work and
how it is done. This presentation looks at an approach that has been used to convert the large
amount of effort data usually collected in an organization into something that can meaningfully
be used for estimation and comparison purposes.
Keep Calm and React with Foresight: Strategies for Low-Latency and Energy-Eff...Tiziano De Matteis
This talk has been given at PPoPP 2016 (Barcelona)
The paper addresses the problem of designing control strategies for elastic stream processing applications. Elasticity allows applications to rapidly change their configuration (e.g. the number of used resources) on-the-fly, in response to fluctuations of their workload. In this work we face this problem by adopting the Model Predictive Control technique, a control-theoretic method aimed at finding the optimal application configuration along a limited prediction horizon by solving an online optimization problem. Our control strategies are designed to address latency constraints, by using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) function of modern multi-core CPUs. The proactive capabilities, in addition to the latency- and energy-awareness, represent the novel features of our approach. Experiments performed using a high-frequency trading application show the effectiveness compared with state-of-the-art techniques.
A full version of the slides (with transitions) is available at: https://docs.google.com/presentation/d/1VZ3y3RQDLFi_xA7Rl0Vj1iqBdoerxCMG4y53uMz9Ziw/edit?usp=sharing
It is rather surprising that in software engineering, standard measurement units have yet to be
widely accepted and used. Every other engineering discipline has their own. By and large, effort
is the most commonly used parameter for measuring software initiatives. The problem of
course is that effort is not an independent variable – it depends on who is doing the work and
how it is done. This presentation looks at an approach that has been used to convert the large
amount of effort data usually collected in an organization into something that can meaningfully
be used for estimation and comparison purposes.
Keep Calm and React with Foresight: Strategies for Low-Latency and Energy-Eff...Tiziano De Matteis
This talk has been given at PPoPP 2016 (Barcelona)
The paper addresses the problem of designing control strategies for elastic stream processing applications. Elasticity allows applications to rapidly change their configuration (e.g. the number of used resources) on-the-fly, in response to fluctuations of their workload. In this work we face this problem by adopting the Model Predictive Control technique, a control-theoretic method aimed at finding the optimal application configuration along a limited prediction horizon by solving an online optimization problem. Our control strategies are designed to address latency constraints, by using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) function of modern multi-core CPUs. The proactive capabilities, in addition to the latency- and energy-awareness, represent the novel features of our approach. Experiments performed using a high-frequency trading application show the effectiveness compared with state-of-the-art techniques.
A full version of the slides (with transitions) is available at: https://docs.google.com/presentation/d/1VZ3y3RQDLFi_xA7Rl0Vj1iqBdoerxCMG4y53uMz9Ziw/edit?usp=sharing
Test different neural networks models for forecasting of wind,solar and energ...Tonmoy Ibne Arif
In this project work, a multi-step deep neural network is used to forecast power generation and load demand for a short-term time frame. The data or feature vectors that have been used to predict the target, is a sequential time series sequence. In this project, a Recurrent Neural Network has been used in combination with a convolutional neural network to have a better forecasting model for the Windpark, Solar park and Loadpark datasets. Moreover, the forecasting performance of Feedforward neural network and Long Short Term Memory also has been compared. The whole project work has divided into two parts, in the first approach the raw dataset has been divided into a train, test split and no previous step data have been used. In the second step whole raw dataset has been divided into test, train and validation split. Additionally, current and seven previous time steps data has been fed into the model.
Explore how our student team leveraged data science to forecast power consumption, empowering smarter energy management and sustainability initiatives. visit for more: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Architectural decisions in designing data and computation intensive systems can have a major impact on the ability of these systems to perform statistical and other complex calculations efficiently. The storage, processing, tools, and associated databases coupled with the networking and compute infrastructure make some kinds of computations easier, and other harder. This talk will provide an introduction to software and data systems components that are important for understanding how these choices impact data analysis uncertainties and costs, and thus for developing system and software designs best suited to statistical analyses.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
A Deep Learning use case for water end use detection by Roberto Díaz and José...Big Data Spain
Deep Learning (DL) is a major breakthrough in artificial intelligence with a high potential for predictive applications.
https://www.bigdataspain.org/2017/talk/a-deep-learning-use-case-for-water-end-use-detection
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
"Quantum clustering - physics inspired clustering algorithm", Sigalit Bechler...Dataconomy Media
"Quantum clustering - physics inspired clustering algorithm", Sigalit Bechler, Researcher, Similar Web
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About the Author:
I am a data science researcher. I have a diverse academic background - a B.Sc. in electrical engineering, a B.Sc. in physics (cum laude) from Tel Aviv University's prestigious program for parallel B.Sc. in Physics and in Electrical Engineering, an M.Sc. in condensed matter (cum laude), and have started my Ph.D. in bioinformatics. Prior to my M.Sc. I have served as a captain in a technology unit of the IDF.
I am passionate about science and solving complex big data problems that require out of the box thinking, and like to dive deep into the details. I always take a positive, proactive approach, and put an emphasis on understanding the big picture as well.
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.
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
Epcon is One of the World's leading Manufacturing Companies.EpconLP
Epcon is One of the World's leading Manufacturing Companies. With over 4000 installations worldwide, EPCON has been pioneering new techniques since 1977 that have become industry standards now. Founded in 1977, Epcon has grown from a one-man operation to a global leader in developing and manufacturing innovative air pollution control technology and industrial heating equipment.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
Climate Change All over the World .pptxsairaanwer024
Climate change refers to significant and lasting changes in the average weather patterns over periods ranging from decades to millions of years. It encompasses both global warming driven by human emissions of greenhouse gases and the resulting large-scale shifts in weather patterns. While climate change is a natural phenomenon, human activities, particularly since the Industrial Revolution, have accelerated its pace and intensity
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
1. Development of Inferential Sensors for
Real-time Quality Control of Water-
Level Data for the Everglades Depth
Estimation Network
Ruby Daamen, Advanced Data Mining Int’l
Edwin Roehl, Advanced Data Mining Int’l
Paul Conrads, USGS – SC Water Science Center
Matthew Petkewich, USGS – SC Water Science Center
2. Presentation Outline
• What is an “Inferential Sensor” (IS)?
• Background - Industrial application
• Everglades Depth Estimation Network
(EDEN)
• Automated Data Assurance and
Management (ADAM) - Inferential Sensor
development for EDEN
3. Development of IS in Industry
• A tough environment to monitor
– Emissions regulations require measurements of
effluent gases
– Smoke stack burns up probes
– Need alternative to “hard” sensors
4. Hard Sensor vs. Inferential Sensor
• Virtual sensor replaces actual sensor
– Temporary gage smoke stack
– Operate plant to cover range of emissions
– Develop model of emissions based on
operations
– Model becomes the “Inferential Sensor”
5. Inferential Sensors for Real-Time Data
• Problem – Need to minimize missing and
erroneous data
• Use similar approach taken by industry to predict
real-time data – ie. “Inferential Sensors”
– QA/QC hard sensor
– Provide accurate estimates for hard sensor
– Provide redundant signal
6. EDEN Problem
• EDEN – integrated network of real-time water-
level (WL) stations, ground-elevation models, and
water-surface models
– Real-time data is used to generate EDEN WL surfaces
used by scientists, engineers and water-resource
managers (253+ stations)
– Data used to guide large-scale field operations,
integrate hydrologic and ecological responses, support
assessments that measure ecosystem responses to the
Comprehensive Everglades Restoration Plan (CERP)
– Correcting errors is often time consuming; gaging
stations may be in remote areas
– Need to identify errors and to provide estimates on a
daily basis
8. EDEN / ADAM Overview
Inferential Sensor
application
9. Some of the Challenges
• 253+ gaging stations
• Inferential sensor uses data from one or
more correlated gages. At any given
datetime do not know what stations will have
available data
• Stations added/removed over time
10. Implementation
• Create models “on the fly”
– Use “best” available stations
– Simplifies addition / removal of stations
• Needs to consider
– Assessment of data – prevent use of suspect
data in models
– Model inputs need be decorrelated
11. Methods
• Two algorithms sequenced to analyze data
– Univariate filtering
• Provides information about the quality and behavior
for each stations WL
• A Statistical Process Control (SPC) which consists of
14 univariate filters – uniquely set for each station
– Estimate parameter value
• Select a “pool” of candidate gaging stations using
matrix of Pearson coefficients
• Principal component analysis (PCA) – calculates
decorrelated inputs
• Multivariate linear regression
12. Univariate Filtering
UNIVARIATE
FILTER CHECK DESCRIPTION PRECEDENCE
WATER LEVEL
LIMIT (ft.)
LOST_SIGNAL no signal 1 NA
GT_RNG_UL x(t) > signal range Upper Range Limit 2 15.19
LT_RNG_LL x(t) < signal range Upper Range Limit 3 6.99
GT_UCL x(t) > signal Upper Control Limit 4 14.73
LT_LCL x(t) < signal Upper Control Limit 5 8.56
Sn_LT_L flatlined: x'(t) = x(t)=x(t-1); SUM[(|x'(t)|,…,|x'(t-n+1)|] < Limit 6 0.00
D1_GT_L_1 vfast vlarge increase: x(t)-x(t-1) > Limit 7 1.92
D1_LT_L_1 vfast vlarge decrease: x(t)-x(t-1) < Limit 8 -2.34
D1Sn_GT_L_1 fast vlarge increase: x'(t)=x(t)-x(t-1); Sum[x'(t),…x'(t-n+1)] > Limit 9 1.98
D1Sn_LT_L_1 fast vlarge decrease: x'(t)=x(t)-x(t-1); Sum[x'(t),…x'(t-n+1)] < Limit 10 -2.52
D1_GT_L_2 vfast large increase: x(t) - x(t-1) > Limit 11 1.69
D1_LT_L_2 vfast large decrease: x(t) - x(t-1)< Limit 12 -0.25
D1Sn_GT_L_2 fast large increase: x'(t)=x(t)-x(t-1); Sum[x'(t),…x'(t-n+1)] > Limit 13 1.98
D1Sn_LT_L_2 fast large decrease: x'(t)=x(t)-x(t-1); Sum[x'(t),…x'(t-n+1)] < Limit 14 -0.27
• Additional filters
– Dry Protocol – set using offset from ground elevation
• Any filter trips are flagged for review
• Data triggering a filter is not used in any
predictions
13. Synthesize WL Measurements
• WL at candidate stations are correlated – no
surprise
• First approach examined selected the most
highly correlated station as a “standard”
signal and then attempted decorrelating the
other stations by computing differences from
the standard.
14. Principal Component Analysis (PCA)
• PCA is a statistical technique used to
“reduce the dimensionality of a data set
consisting of a large number of interrelated
variables, while retaining as much as
possible of the variation present in the data
set. This is achieved by transforming to a
new set of variables, the principal
components (PCs), which are uncorrelated,
and which are ordered so that the first few
retain most of the variation present in all of
the original variables” (Joliffe)
15. PCA – Main Points
• PCA - the main points
– Principal components are uncorrelated
– Transforms a set of correlated variables into a
smaller number of uncorrelated variables
– The first principal component (PC) accounts for
most of the variability in the data
16. PCA - Analysis
• PCA – a brief description of the analysis:
– Calculate the eigenvectors and eigenvalues of
the covariance matrix
• Create data set of n inputs with no gaps
• Subtract the mean from each n input
• Calculate the covariance matrix (square nXn matrix)
• Calculate eigenvectors
– Sort by eigenvalues (highest to lowest)
• Largest eigenvector = 1st principal component
• Use eigenvalues to determine how many PCs to
include
17. PCA – A 2-Dimension Example
Original Data
(Mean Subtracted)
Eigenvectors Principal
Components
Eigenvectors
Plotted on
Data
-4
-3
-2
-1
0
1
2
3
4
-4 -3 -2 -1 0 1 2 3 4
Normalized Data
E1
E2
Original Data
18. ADAM - Functionality
• Setup
– File paths
– PCA setup – period, number of sites to include
– Add / Edit / Remove sites
– Univariate filters
• Inferential Sensor – Option to analyze daily
(hourly and 15 minute data), quarterly and
annual (hourly) daily files
• Review results
• Output daily median files as required for
EDEN water surface map
19. ADAM – Control Worksheet
Select Daily, Quarterly or
Annual Run Analysis
Resume, redo OR continue from
last analyzed
Fill Setup
Remove , add, or edit sites
included in ADAM
Set Pathnames for files used
by ADAM
20. ADAM – Control Worksheet
Loads data from
selected run for review
Creates output files
required to generate
EDEN water surface
map
Dumps a listing sites
tripping any filters
27. PCA – The methodology
• From our favorite source -Wikipedia – PCA is:
– A mathematical procedure that transforms a set of correlated variables into a smaller number of
uncorrelated variables. The first principal component accounts for as much of the variability in
the data as possible and each succeeding component accounts for as much of the remaining
variability as possible.
• How to do it: In the broadest sense it is an eigenvector based multivariate analysis. The
methodology used is:
– Assemble the data
• In EDENIS the data will be water level data from up to 5 gaging stations. For 90 days of hourly data this
equates to up to 2160 vectors (8640 for 15 minute data)
– Remove any vectors that contain any missing data (1)
• Resulting matrix X[n,m] where n = number of fully populated vectors; m = number of gages included
– Subtract the mean from each of the data dimensions (m) – lets call this the normalized matrix B
(2)
• B[n,m] stores mean-subtracted data
– Calculate the Covariance matrix (3)
• Covariance matrix is a square matrix with dimension mXm: C[m,m]
– Calculate the eigenvalues and eigenvectors of the covariance matrix
• This is an iterative process. If you want to look up some more on this I used the Jacobi eigenvalue
algorithm. Some important properties of eigenvectors:
– Can only be found for square matrices. If a square matrix (mXm) does have eigenvectors, there are m of them
– All the eigenvectors of a matrix are orthogonal to each other. This is important: when the data is expressed in terms of
these eigenvectors the resulting principal components are uncorrelated
• For a refresher course on eigenvalues / eigenvectors heres a link
http://en.wikipedia.org/wiki/Eigenvalue,_eigenvector_and_eigenspace
– Sort the results: Largest eigenvalue to smallest eigenvalue. (4)
– Decide how many components to keep and calculate the new data set to be used. This is a
simple matrix multiplication of B X E where E is the mXm eigenvector matrix. (5)
• For the 10-12 sites I looked at when presenting the PCA results I would expect we’ll rarely if ever use more
than 2 principal components out of a possible 5 to make the regression predictions.
28. PCA – A 2-Dimension Example
1. Original Data 2. Mean
Subtracted
3. Covariance
Matrix
4. Eigenvalues /
Eigenvectors
5. New Data Set
Note high
correlation of
orignal X1, X2 vs.
No correlation of
PC1, PC2
E1 and E2 are the 2
eigenvectors layed on
top of the data. Note E1
and E2 are
perpendicular. Also note
that E1 goes through the
middle of the data – like
a best fit. E2 provides
less information about
the variance in the data.
-4
-3
-2
-1
0
1
2
3
4
-4 -3 -2 -1 0 1 2 3 4
Normalized Data
E1
E2
29. Challenges
• Develop 253+ models using artificial neural
networks (ANNs) (1 model per station)
– Pros
• authors have prior success modeling complex
processes using ANNs
• ANNs use non-linear curve fitting to capture complex
behaviors
– Cons
• Do not know what stations will have “good” data at
any given datetime
• Stations are removed and added
WL data at real-time gages are related to ungaged ares andusing ground elevation data, water depths throughout the Everglades are computed
Data used by scientists, engineers, and water resource managers to support CERP – Comprehensive Everglades Restoration Plan
Used to :
1) Guide large scale field operations, 2) integrate hydrologic and ecological responses and 3) support biological and ecological assesssments that measure ecosystem responses to the CERP