Tampa Bay Water Chooses InfoWater for Water Infrastructure Modeling and Management

 
Large Florida Utility Switches to Innovyze Smart Water Technology
Tampa Bay Water Chooses InfoWater for Water Infrastructure Modeling and Management
Broomfield, Colorado USA, January 8, 2013 — Innovyze, a leading global innovator of business analytics software and technologies for wet infrastructure, today announced that Tampa Bay Water, a large regional water supply authority in Florida, has adopted the company’s industry-leading InfoWater Suite software as its standard water modeling, design, and management solution. The software will serve as the foundation for managing and optimizing Tampa Bay Water wholesale drinking water distribution system. 

The selection underscores the value of the company’s geocentric smart water network modeling and design solutions — tools that have made Innovyze a worldwide market leader. Among the reasons cited for the utility’s decision were the software’s many powerful tools, comprehensive functionality, speed, ease of use, flexibility, and seamless ArcGIS (Esri, Redlands, CA) integration.

Tampa Bay Water delivers water to more than 2.3 million people in the Tampa Bay area, including: Hillsborough County, Pasco County, Pinellas County, New Port Richey, St. Petersburg and Tampa.  The system includes over a dozen groundwater pumping and treatment facilities, two surface water sources, a 15.5 billion gallon storage reservoir, a seawater desalination plant, and over 200 miles of water mains. Several of the municipalities that receive wholesale water from Tampa Bay Water also use InfoWater software for hydraulic modeling.

“We chose InfoWater because it integrates seamlessly with our ArcGIS platform, providing the ability to model and analyze the season changes in supply sources and daily demand patterns,” said Suzannah Folsom, Design Project Manager for Tampa Bay Water. “These tools help Tampa Bay Water plan for the future, with increased efficiency and cost-effectiveness in our delivery system.”

Built atop ArcGIS, InfoWater seamlessly integrates sophisticated analytics, systems dynamics, and optimization functionality directly within the ArcGIS setting. The InfoWater product suite comes equipped with everything water utility owner–operators need to best design, operate, secure, and sustain their distribution systems — from fire flow and water quality simulations, valve criticality, and energy cost analysis to pressure zone management and advanced Genetic Algorithm and Particle Swarm optimization. In addition, the software serves as a base platform for advanced smart network modeling, operational, capital planning, and asset management extensions. These critical applications include IWLive (real-time operations and security),InfoWater UDF (unidirectional flushing), CapPlan (risk-based capital planning), InfoMaster and InfoMaster Mobile (asset integrity management and condition assessment), InfoWater MSX (multispecies, temperature, and particle transport/deposition modeling), InfoWater BTX (event/particle backtracking), InfoSurge(surge/transient analysis), and Sustainability (carbon footprint calculation).

“We continue to invest in best-of-breed GIS-centric modeling and design solutions that are easy to implement and use, because they give our customers a significant business edge,” said J. Erick Heath, P.E., Innovyze Vice President—Business Director, Americas. “Utilities that are leaders in their field, like Tampa Bay Water, continue to upgrade to the power of Innovyze products because they deliver consistent, high-fidelity engineering GIS modeling results, enhanced efficiency, and cost-effective project plans. These advantages lead to measurable improvements in productivity, system performance, return on investment, and customer satisfaction — and ultimately, greater success.”

How to Compile SWMM 5 in Visual Studio 2010 Express

How to Compile SWMM 5 in Visual Studio 2010 Express

Download the newest SWMM 5 code(Figure 1) from http://www.epa.gov/nrmrl/wswrd/wq/models/swmm/#Downloads and then make a new directory on your computer. We will call it c:\newSWMM5Code with a subdirectory C:\newSWMMCode\VC2005_DLL  in which the attached vcxproj file is placed.  The source code from the EPA should be placed on C:\newSWMMCode.  You can then open up the file swmm5_ms.vcxproj and make a new SWMM 5 DLL model with your code modifications (if needed). 

Figure 1.  The source code from the EPA for SWMM5.

Singapore - Catching Every Drop of Rain

Singapore - Catching Every Drop of Rain

  The source of the map of the rivers of Singapore is the Singapore PUB

As a small island that doesn't have natural aquifers and lakes and with little land to collect rainwater, Singapore needs to maximize whatever it can harvest.

Currently, Singapore uses two separate systems to collect rainwater and used water. Rainwater is collected through a comprehensive network of drains, canals, rivers and stormwater collection ponds before it is channelled to Singapore's 17 reservoirs for storage. This makes Singapore one of the few countries in the world to harvest urban stormwater on a large scale for its water supply.

The newest reservoirs are Punggol and Serangoon Reservoirs which are our 16th and 17th reservoirs. By 2011, the water catchment area has increased from half to two-thirds of Singapore’s land surface with the completion of the Marina, Punggol and Serangoon reservoirs.

With all the major estuaries already dammed to create reservoirs, PUB aims to harness water from the remaining streams and rivulets near the shoreline using technology that can treat water of varying salinity. This will boost Singapore’s water catchment area to 90% by 2060,

The goal is to capture every drop of rain (Figure 1)

Reservoirs

Pandan Reservoir

Kranji Reservoir

Jurong Lake Reservoir

MacRitchie Reservoir

Upper Peirce Reservoir

Lower Peirce Reservoir

Bedok Reservoir

Upper Seletar Reservoir

Lower Seletar Reservoir

Poyan Reservoir

Murai Reservoir

Tengeh Reservoir

Sarimbun Reservoir

Pulau Tekong Reservoir

Marina Reservoir

Serangoon Reservoir

Punggol Reservoir

Rivers

Singapore River

Sungei Kallang

Rochor River

Sungei Whampoa

Geylang River

Sungei Bedok

Sungei Ketapang

Sungei Changi

Sungei Selarang

Sungei Loyang

Sungei Tampines

Sungei Api Api

Sungei Blukar

Sungei Serangoon

Sungei Punggol

Sungei Tongkang

Sungei Pinang

Sungei Seletar

Sungei Khatib Bongsu

Sungei Seletar Simpang Kiri

Sungei Sembawang

Sungei Mandai

Sungei China

Sungei Mandai Kechil

Sungei Peng Siang

Sungei Tengah

Sungei Kangkar

Sungei Buloh Besar

Sungei Jurong

Sungei Lanchar

Sungei Pandan

Sungei Ulu Pandan

Figure 1. Overall Map of Singapore from http://caelanchewthegreat.blogspot.sg/2012/04/geography-aa-2012national-tap-1-water.html

 

 

 

 

Advances in artificial intelligence: deep learning

Advances in artificial intelligence: deep learning

November 25, 2012 – 12:34 am

If you want to keep up with advances in artificial intelligence, the New York Times has an essentialarticle on a recent step forward called deep learning.

There is a rule of thumb for following how AI is progressing: keep track of what Geoffrey Hinton is doing.

Much of the current science of artificial neural networks and machine learning stems from his work or work he has done with collaborators.

The New York Times piece riffs on the fact that Hinton and his team just won a competition to design software to help find molecules that are most likely to be good candidates for new drugs.

Hinton’s team entered late, their software didn’t include a big detailed database of prior knowledge, and they easily won by applying deep learning methods.

To understand the advance you need to know a little about how modern AI works.

Most uses abstract statistical representations. For example, a face recognition system will not use human-familiar concepts like ‘mouth’, ‘nose’ and ‘eyes’ but statistical properties derived from the image that may bear no relation to how we talk about faces.

The innovation of deep learning is that it not only arranges these properties into hierarchies – with properties and sub-properties – but it works out how many levels of hierarchy best fit the data.

If you’re a machine learning aficionado Hinton described how they won the competition in a recent interview but he also puts all his scientific papersonline if you want the bare metal of the science.

Either way, while the NYT piece doesn’t go into how the new approach works, it nicely captures it’s implications for how AI is being applied.

And as many net applications now rely on communication with the cloud – think Siri or Google Maps – advances in artificial intelligence very quickly have an impact on our day-to-day tools.
 

Link to NYT on deep learning AI (via @hpashler)

via Mind Hacks  


Maximum HGL Head Class in InfoSWMM AND H2OMAP SWMM

Maximum HGL Head Class in InfoSWMM AND H2OMAP SWMM

 

You can find the node flood or surcharge maximum occurrence during a simulation in the Junction Summary Report table in InfoSWMM and H2OMAP SWMM (Figure 1)

 

Empty                                   if the Node Head is below or equal to the Lowest Link Connecting  Elevation

Below Link Crown            if the Node Head is below or equal to the Highest Link Connecting Crown

Below Maximum Depth   if the Node Head is below or equal to the Node Invert + Full  Depth.  The column Max Surcharge Height above Crown will also tell you how deep the Surcharge in a Node.

Surchaged                           if none of the above is true.

 

 

Figure 1.  Junction Summary Report in InfoSWMM

 


Figure 2.  Maximum Surcharge Height above Crown Definition