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M+M Consulting Engineering a Better Solution Engineers |
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Engineering a Better Solution |
The left picture shown below is a satellite image of
the King’s Basin, which is located in California.
For this problem, the goal is to determine where the snow is located on
the ground, Snow Coverage Area (SCA). If
one knows where the snow is located, then it is fairly straightforward to
determine the probability of flooding due to spring runoff.
Unfortunately, any regional cloud cover prevents the satellite image from
being useful for locating SCA. To
overcome this problem, a neural network was used to predict where the snow is
located on the ground. The
synthetic image (right) was produced using a neural network, producing a better
image than previously thought possible.
Predicting
Electrical Demand Electrical demand prediction can save users and producers of
electricity millions of dollars per month if it were possible to predict when
its use would be required. A neural
network was used to predict the electrical load within an average of 1.6% at the
United States Military Academy, West Point, NY. The graphic below shows the electrical demand profile that
was created using simple, easily obtainable information. Shown on the graph is electrical demand over time.
Peaks are indicative of daytime electrical demand and valleys occur at
night. In this example, the first
two peaks were recorded on Thursday and Friday with lower peaks following on
Saturday and Sunday. Eight
additional days are shown with the last peak being a Monday.
Predicting
Ice Jams Breakup ice jams occur during periods of thaw when increased
discharge due to snowmelt and/or precipitation cause the forces on an ice cover
to exceed its strength, resulting in the breakup of the ice cover.
The broken ice is transported down the river until the river’s
transport capacity is exceeded. This forms an accumulation that obstructs flow, creates
backwater, and can cause flooding. Breakup
ice jams can create significantly more flooding than traditional river flooding
due to the reduction in channel width and rapid rise in water levels, similar to
flash floods. These rapid increases
in water level can make it difficult to plan or execute ice jam mitigation
measures such as evacuation or blasting. Depending on the jam characteristics, a
prediction method might significantly increase warning time.
Table 1 shows three methods that were used to predict ice jams (smaller
numbers are better). In all cases,
we see where neural networks provide better predictions than traditional
statistical methods for predicting ice jam occurrences. Table 1.
Comparisons of ice jam prediction at Oil City using various techniques.
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