The Boating Risk Analysis INnformation System (BRAINS) uses the statistical relationships between accident report variables within the U.S. Coast Guard's Boating Accident Report Database (BARD) System to determine the probability that a specific accident could occur given the conditions entered by a user. BRAINS enables those interested in boating accidents to determine the effect a specific variable, or group of variables, have on the probability that a specific accident could occur based on the relationships between variables within the BARD System.
 

 

If you have questions about BRAINS, you might also want to read the FAQ or read the using BRAINS section.


About BRAINS:

BRAINS is a tool that enables you to isolate the effect of a specific variable, or a group of variables, on the probability of having a specific type of boating accident (based on the relationships between variables in the Boating Accident Report Database (BARD), which is the universe of all reported accidents).

The Coast Guard does not know if variable relationships of non-reported accidents are similar to those reported in BARD. BRAINS also gives you the flexibility to make predictions using variable combinations that are not frequently occurring in BARD.


What BRAINS Does - by example

The BRAINS system enables you to isolate the effect that an individual variable has on the probability of a specific type of accident occurring (within the universe of all reported accidents). The benefit of this system is it enables you to measure the likelihood of a reported accident either increasing or decreasing given a change in a specified variable while holding the effect of all other variables constant. Thus, BRAINS allows you to determine what variables have the greatest impact in determining the likelihood of common accident occurrences. Further, BRAINS is flexible enough to allow you to determine the likelihood of uncommon or hypothetical accident scenarios.

Let's use a specific example to illustrate these points. Assume that you want to answer the question

What major factors increase or decrease one's risk of being in an accident that involves a capsizing, swamping/flooding, or sinking in New England?

(For the rest of this example, the model will be simply referred to as "CAPSIZING").

Further, let's assume that you do not have a copy of BRAINS, but instead you own a statistical software package and have a knowledgeable statistician to operate it. This person begins analyzing the Boating Accident Report Database (BARD) for you.

To answer this question, you first ask your statistician to compare the incidence of CAPSIZING with two particular types of boats, open motorboat and canoe/kayak. First, your statistician reminds you that the results from any analysis of the BARD cannot report what happened to all boaters - it can only report what happened to boaters involved in reported accidents.

Now your statistician compares CAPSIZING with boat types. You discover that in New England:

Comparison #1:

  • 18% of the open motorboats in reported accidents were involved in CAPSIZING accidents
  • 83% of the canoes/kayaks in reported accidents were involved in CAPSIZING accidents

Our statistician tells us that canoe/kayaks are 3.6 times more likely to be involved in a capsizing accident than an open motorboat, i.e., (83-18)/18 = 3.6.

Thinking that water conditions or operator behavior may affect our results, we inquire further into the accident data. Our statistician provides us with the following information:

4% of open motorboats involved in accidents reported strong currents at the time of the accident, whereas 16% of canoes/kayaks involved in accidents reported strong currents. 3% of the open motorboats involved in all accidents reported over or improper loading as a contributing factor, compared to 12% of all canoes/kayaks involved in accidents.

Thus, we find that compared with the open motorboats, canoes/kayaks:

Comparison #2

  • Are 3 times more likely to be involved in accidents with strong currents
  • Are 3 times more likely to be over or improperly loaded


But this raises a question in your mind regarding capsizings:

Are canoes and kayaks really more likely to be involved in capsizings, or are they just more likely to be operating in strong currents and/or improperly loaded?

That is, what is more important in affecting the likelihood of a capsizing: is it the boat type, the water conditions, or the way the boat was loaded? Using simple statistical techniques does not seem to provide a clear answer.

When we originally found that canoes/kayaks were 3.6 times more likely to be involved in capsizings, we did not consider that canoes/kayaks are more likely to be in strong currents and improperly loaded. This is the major point to consider. The original comparison (Comparison #1) inadvertently included the effects of additional variables in comparing the effect of boat type on the likelihood of a capsizing. In other words, Comparison #1 actually assessed the joint effect of changes in boat type, water conditions, and load on the likelihood of a capsizing instead of just the effect of changing the type of boat. This automatic inclusion of "hidden" variables can frequently happen when we use basic statistical comparisons.

To eliminate these confounding effects, you may have to think of all the complicated conditions and effects that would have to be explained to get a clear answer to our original question. There must be a simpler way...and there is - the BRAINS system.

BRAINS has done the statistical work for you!

Using BRAINS, you decide to pick the model New England Capsizings. You will see that the system estimates the probability of a capsizing given the conditions reported in the model (the default conditions are the average or most likely conditions associated with New England capsizings based on accident report data). You also note that you can change the model by modifying the value of a variable and, by doing so, can see the specific effect that a variable has upon the likelihood of a capsizing while holding the effects of all other variables constant.

You decide to first estimate the probability of capsizing using the following settings:


Test Scenerio 1:

 Vessel Length 16
 Vessel Type Open Motorboat
 Hull Type Fiberglass
 Year of Accident 1992
 Wind Speed 7
 Water Type Lake
 Vessel improperly loaded or overloaded No
 Stability Loss No
 Strong Current No

Probability Result: 13%


These are the BAR variables that are statistically related to the likelihood of a capsizing. All other variables were found to be statistically insignificant. Under these conditions, you see that the model predicts that the probability of a capsizing is 13%.

Please Note: The probabilities reported above should NOT be interpreted as the probability that a capsizing will occur.

Remember that the BAR data represent reported accidents only. Although nearly all fatal accidents are reported, only a small percentage of non-fatal accidents are reported as required by law. In addition there may be hours of boating activity fulfilling model conditions that never result in an accident. The models look at the statistical relationships inherent within the accident report data. As a result, one should not place emphasis on absolute predictions; one should instead rely on the relative predictions provided by the model. These limitations always hold when using the BAR data and BRAINS.

You now rerun the model changing only the vessel type variable from open motorboat to canoe/kayak:


Test Scenerio 2:

 Vessel Length 16
 Vessel Type Canoe/Kayak
 Hull Type Fiberglass
 Year of Accident 1992
 Wind Speed 7
 Water Type Lake
 Vessel improperly loaded or overloaded No
 Stability Loss No
 Strong Current No

Probability Result: 68%


With the result of 68%, what has BRAINS told you? Well, what BRAINS is reporting is that, holding the other conditions constant in the model, the specific effect of a change in vessel type from open motorboat to canoe/kayak increases the likelihood of a capsizing by 4.2 times ([68 - 13])/13 = 4.2)

You now compare this to your earlier estimate (the one done without BRAINS) on the effect of a change in vessel type which indicated that moving from an open motorboat to a canoe/kayak increased the likelihood of a capsizing by 3.6 times. BRAINS reported a 4.2 times increase. Evidently, our earlier analysis had suffered effects from some additional conditions we did not account for. BRAINS automatically reported only the effect of a change in vessel type while simultaneously holding the effect of all other variables constant.

Let's test BRAINS further. You now want to know what is more likely to increase one's risk of capsizing; is it boat type, current, or load? To answer this question, you rerun the model two more times:

After you set vessel type back to open motorboat, alternatively switch the other variables (current or load) from "no" to "yes":


Test Scenerio 3:

 Vessel Length 16
 Vessel Type Open Motorboat
 Hull Type Fiberglass
 Year of Accident 1992
 Wind Speed 7
 Water Type Lake
 Vessel improperly loaded or overloaded No
 Stability Loss No
 Strong Current Yes

Probability Result: 42%


Test Scenerio 4:

 Vessel Length 16
 Vessel Type Open Motorboat
 Hull Type Fiberglass
 Year of Accident 1992
 Wind Speed 7
 Water Type Lake
 Vessel improperly loaded or overloaded Yes
 Stability Loss No
 Strong Current No

Probability Result: 86%


BRAINS indicates that among open motorboats, the likelihood of a capsizing is 42% when there is a strong current and 86% when the vessel is improperly loaded.

Thus, a strong current increases the likelihood of a capsizing by 2.2 times and an improper load increases the likelihood of a capsizing by 5.6 times.

Now we know the answer to our question. Of the three factors we studied, the following have the greatest impact in increasing the likelihood of a capsizing:

  • Over and/or Improper loading of the boat: 5.6 times more likely to capsize
  • Change in operation from an open motorboat to a canoe/kayak: 4.2 times more likely to capsize
  • Presence of a strong current: 2.2 times more likely to capsize
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jcSystems, inc.
Produced under a grant from the
Aquatic Resources (Wallop-Breaux) Trust Fund