James (Andy) Efta and Woodam Chung, The University of Montana
Department of Forest Management
College of Forestry and Conservation
October 2009

1.
Introduction
1a. Assumptions and limitations
2.
Installing the software
3.
Model components/general model overview
4.
Preparing WEPP: Road/erosion estimate data
4a. Collecting/processing WEPP: Road field data
4b. Identifying hot spots on the road network
4c. Identifying appropriate BMPs
4d. Assessing effectiveness of BMP treatments
5.
Preparing BMP-SA input files
5a. Description/formatting of input text files
5b. BMP-SA file format examples
6.
Running the model
6a. Model outputs
6b. Assessing solution quality
6c. Troubleshooting
7.
References
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Tables
Table 1. Limitations in BMP-SA model
Figures
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Figure 1. Proper installation location for BMP-SA and associated file folders
Figure 2. Flowchart of steps involved in preparing and running BMP-SA
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Figure 3. Example initial solution formulated from a list of alternative BMPs on a road segment
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Figures 4 and 5. Examples of neighborhood solutions formulated from the initial (current) solution 6
Figures 6 and 7. Examples of number of BMPs installed in a given planning period at two budgets within BMP-SA 7
Figure 8. Example changes to WEPP: Road Batch inputs for assessing BMP effectiveness
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Figure 9. BMP-SA per-segment cost and maintenance summary text file
Figure 10. Per-period cost summary text file
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Figure 11. Example of solution value change with iteration number in a minimization problem using simulated annealing optimization 22
BMP-SA file format examples
16 existing_ road_network.txt file new_BMP.txt file lookup_table.txt file
User_input.txt file
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BMP-SA and its associated methodology allow the user to spatially optimize road-related Best
Management Practice (BMP) implementation on a forest road network. BMP treatments and treatment schedules are optimized by minimizing sediment leaving the buffer (hillslope lying between the fill slope and nearest stream) while simultaneously accounting for budget constraints, maintenance, and equipment scheduling concerns. An attractive function of BMP-
SA is that users can not only find the most ideal budget for minimizing sediment through BMP implementation but also the best possible options for treatment given a limited budget. By applying this tool, managers can identify where to focus limited resources to achieve the greatest economic and environmental benefit across multiple spatial and temporal scales.
BMP-SA requires estimates of sediment leaving the buffer as input data. Several methods
(empirical models, process-based models, or field measurement) can be used to determine this.
Empirical modeling and field measurements, while both viable means of predicting erosion risk from a given location, generally have a limited scale at which they are feasible and/or applicable.
Since the intention of BMP-SA is to address one- to multiple- watershed scale BMP planning problems, process-based modeling provides a tool from which detailed yet generalizable predictions can be generated with minimal input data. WEPP: Road provides a user-friendly web-based interface to a widely-used process-based model. WEPP: Road requires few input parameters, many of which can be acquired without field surveys. The interface allows users from a variety of technical backgrounds to effectively predict impacts associated with roadrelated management decisions. By providing a range of climates (and the option to alter climates to represent site-specific conditions), surface types, traffic levels, and road designs, WEPP: Road is applicable virtually anywhere in the U.S.
BMP-SA was designed to use WEPP: Road erosion estimates. Guidance has been provided accordingly, but is not meant to deter the user from using other erosion estimation methods. No matter what method is used for predicting sediment, the often-used slogan “garbage in, garbage out” is highly pertinent for managers wishing to achieve accurate predictions from this combination of models.
Having been initially constructed for the Lake Tahoe Basin, BMP-SA was meant to minimize loss of Lake Tahoe’s water clarity due to sediment inputs from upland forest roads. Once familiar with model inputs and operation, users can readily use the model to minimize sediment leaving the road instead of the buffer by changing text file inputs.
Intended users of BMP-SA and its methodology are roads engineers, hydrologists, and watershed planners. It is assumed that users have moderate levels of computer expertise, including GIS experience. This user manual will go beyond operation of BMP-SA and also discuss the overall methodology for minimizing road-related sediment inputs to streams. This methodology includes collection of road survey data, general discussion of WEPP: Road modeling, input file formatting, and final model outputs. For in-depth discussion of model application in the Lake
Tahoe Basin, see Efta (2009).
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1a. Assumptions and limitations:
There are multiple assumptions and limitations built into the model that the user should be aware of. In addition to those discussed below, see Table 1 for limitations directly related to model inputs.
The model assumes that BMPs are maintained in perpetuity. Note, however, that existing BMP maintenance and associated equipment scheduling are not accounted for within the model. The model does account for equipment scheduling costs when maintaining new BMPs, but assumes that each piece of equipment required for BMP installation and maintenance costs the same to move into and out of a site.
The best erosion mitigation scenario at any given budget level is heavily dependent on the quality of erosion prediction or measured sediment from a given treatment. For this reason, it is important to critically analyze erosion estimates prior to implementing them in the model.
WEPP: Road estimates have been shown to deviate from field measurements by +/- 50% (Elliot et al. 1999a). Accordingly, the importance of having accurate model inputs cannot be understated.
Finally, the results of BMP-SA are not meant to replace professional judgment in the field.
Rather, this tool is meant to assist in the decision-making process associated with forest road management and planning.
Table 1. Limitations in BMP-SA model.
Model component
Planning horizon
Maximum value
100
Unit years
Length of planning period
Number of segments
Treatments per segment
1
1000
4 year segments
BMPs/treatments
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Access the following website: http://www.cfc.umt.edu/PersonnelDetail.aspx?id=1109.
From the website, download BMP-SA. Install the BMP-SA folder on your C:\ drive. Within the folder, there should be a BMP-SA input files folder, a BMP-SA output files folder, a folder labeled Microsoft.VC80.CRT, and BMP-SA.exe.
Figure 1. Proper installation location for BMP-SA and associated file folders.
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From start to finish, implementation of BMP-SA requires multiple steps (Figure 1). These steps include data acquisition in the field as well as processing and manipulation of data in several computer programs in order to get the end result. While the process may seem daunting, when broken down into steps BMP-SA implementation is relatively simple.
The user must first establish erosion hot spots on the road network of interest. If using WEPP:
Road to establish erosion hot spots, field surveys must be conducted within the watershed of interest to acquire model input data. After hot spots have been established, appropriate treatments must be assigned to problematic road segments. Upon assigning a set of alternative
BMP options, these options can then be input into BMP-SA, along with installation and maintenance costs, maintenance frequencies, and the predicted sediment leaving the buffer associated with each treatment.
BMP-SA uses simulated annealing as its heuristic solver. Within the model, applicable BMP installation and maintenance scenarios are created from a list of BMP options. BMP-SA first randomly assembles an initial, or current, solution where one treatment option is selected for every segment (Figure 2). A neighborhood solution is then formulated where one element of the current solution is altered (Figures 3 and 4). Sediment generated from these alternative solutions is compared within the algorithm as temperature (a variable within the algorithm) is cooled. If the neighborhood solution is better than the current solution, the neighborhood solution is always accepted and used to formulate the next neighborhood solution for comparison. If a neighborhood solution is worse than the current solution, an acceptance probability is calculated that is linked to the temperature at the time of comparison:
݌ሺ݊݁ݓሻ ൌ ݁
௖௨௥௥௘௡௧ି௡௘௪
௧௘௠௣ where current is current solution sediment leaving buffer, new is new solution sediment leaving buffer, and temp is current temperature in model. Depending on the outcome of the acceptance probability computation, the worse solution may be accepted. In doing so, simulated annealing provides a means to find the best solution faster than if a purely random search technique was used.
Within BMP-SA, sediment leaving the buffer is discounted through the course of the planning horizon. In doing so, the model assumes that sediment saved now is more valuable than sediment saved in the future. The four percent discount rate used here is standard for natural resource economic analysis involving U.S. Forest Service investments (Row et al. 1981). In terms of the effect discounting has on solutions, the model will favor solutions where BMPs are installed early in the planning horizon (Figures 5 and 6).
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Gather input data for WEPP: Road
GIS
Field work
Existing BMP infrastructure input table
Process road erosion in WEPP: Road Batch
Identify erosion hot spots
Identify appropriate BMP alternatives for a given site
BMP-SA
Lookup table of distances between delivery points on segments
User-defined input parameter file
Process road erosion in
WEPP: Road Batch again
Alternative New BMP table
Figure 2. Flowchart of steps involved in preparing and running BMP-SA.
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BMP
Outslope
Segment
10
Period
2
Sediment leaving buffer
(tons)
.010
Drain dip 27 4 .002
Pavement 13 8 .035
Figure 3. Example initial solution formulated from a list of alternative BMPs on a road segment. Initial solutions are randomly formulated within BMP-SA.
BMP Segment Period
Sediment leaving buffer (tons)
BMP Segment Period
Sediment leaving buffer (tons)
Outslope 10 2 .010
Outslope 10 2 .010
Drain dip 27 12 .002
None 27 4 .032
Pavement 13 8 .035
Pavement 13 8 .035
Figures 4 and 5. Examples of neighborhood solutions formulated from the initial (current) solution. Neighborhood solutions are formulated by changing one element of the initial (current) solution, either period of installation (Figure 4) or BMP installed (Figure
5). In this case, the BMP that was installed or the type of BMP installed can be changed. With BMP-SA, current and neighborhood
BMP solutions are iteratively compared. The algorithm compares two solutions, and if a neighborhood solution is worse than the current solution, an acceptance probability is calculated such that, based on temperature, the neighborhood solution may be accepted as the current solution. In doing so, BMP-SA can arrive at a consistent near-optimum BMP implementation and management scenario more quickly than if purely random search techniques were employed.
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$6,000 $20,000
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6
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0
5
0
1 2 3 4 5 6 7 8
Planning period
1 2 3 4 5 6 7 8
Planning period
Figures 6 and 7. Examples of number of BMPs installed in a given planning period at two budgets within BMP-SA (from Efta 2009). Note that as budget goes up, more BMPs are installed in earlier periods.
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4a. Collecting/processing WEPP: Road field data:
This section provides some instructions for collecting and formatting data inputs for WEPP:
Road. In providing some general guidelines, this is not meant to be an exhaustive description of
WEPP: Road implementation. Disregard this section if using other methods for estimating erosion risk.
Establishing problematic segments on the road network is the first step in implementing BMP-
SA. If using WEPP: Road to establish these erosion hot spots, field surveys are necessary to acquire input variables for WEPP: Road. A sensitivity analysis of WEPP: Road suggests that soil type, segment length and gradient, and buffer length and gradient all have substantial impact on predicted erosion leaving a segment or the road buffer (Efta 2009). For that reason, averaging of any of these variables over large areas may produce highly inaccurate estimates of sediment leaving the road and buffer. WEPP: Road inputs should be collected on a per-segment basis.
Parameters acquired in the field:
Road segment width, gradient, surface type, road design, rock content, and fill slope length and gradient are best measured or observed in the field. A logger’s tape and clinometer are ideal for making measurements. Note that WEPP: Road expects all lengths to be horizontal lengths, not slope lengths. To establish road segment lengths, mark each segment’s from node and to node using a GPS unit.
Traffic level must also be determined prior to inputting road segments into WEPP: Road.
Generally, active logging operations indicate heavy traffic, and patchy recreational traffic is considered low traffic.
Rock content is percent rock content greater than 2 mm diameter by volume. This can be measured in the field using a sieve. Soil texture can either be found via soil survey or hand texturing in the field.
Parameters acquired from alternative sources:
GIS may be helpful for finding several WEPP: Road inputs, including buffer gradient, buffer length, and road segment length. In terms of calculating buffer length, the end goal is to have a flow path distance from a road segment’s delivery point to the nearest stream. From this flow path distance, buffer gradient can be calculated by taking elevation difference between the two points and dividing by flow path length. To calculate road segment length using GIS, use vector
GIS road data and the segment ends saved in the GPS. With this data, road segment lengths can be calculated either manually or via script/code-based tool. Because these are relatively advanced GIS tasks, you may wish to enlist the help of your agency or company’s GIS personnel.
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Climate is an important WEPP: Road input. While a number of climates are available within
WEPP: Road, the library does not include every climate station and/or SNOTEL site in the
United States. To partially alleviate this problem, the PRISM model has been incorporated into the FS WEPP interfaces. PRISM provides the capacity to alter climate data to a specific site using regression relationships based on elevation. PRISM climate regressions, however, are gridded at a resolution of 4 km (Daly et al. 2004), so PRISM-modified climates have limited spatial applicability from a given site. Also, PRISM does not alter wet days, an important climate input for WEPP: Road.
What does all this mean for the user? First, look for the closest available climate station when modeling erosion using WEPP: Road. If the closest station is not available in the climate library, try altering the climate using PRISM. If PRISM climate parameters don’t seem appropriate for a given site, a climate can be manually entered and saved if desired. See WEPP: Road user documentation for further instruction on this process (Elliot et al. 1999b).
Running WEPP: Road:
WEPP: Road Batch ( http://forest.moscowfsl.wsu.edu/cgi-bin/fswepp/wr/wepproadbat.pl
) provides a convenient approach for modeling up to 200 road segments at once. Download the
Microsoft Excel template and enter your collected segment data into the template. This data can then be copied and pasted into the input dialog box for WEPP: Road Batch. “Comment” cells are a handy place to include segment numbers.
Upon completion of the modeling process, users should have a spreadsheet of predicted sediment leaving the road and sediment leaving the buffer for each segment. These outputs can now be analyzed for identification of hot spots on the road network.
For more documentation on WEPP: Road, see Elliot et al. (1999a).
4b. Identifying hot spots on road network
What is a hot spot??
This question is left purely to the discretion of managers. In the initial demonstration of this methodology, erosion hot spots were identified by creating a histogram of the data and looking for natural breaks within the histogram to classify high, moderate, and low risk road segments. Depending on the size of the watershed and overall project complexity, the user may choose to call any segment predicted to have sediment leaving the buffer a hot spot. If the user has limited resources and numerous problematic road segments, only those segments posing a high risk to overall water quality may be the appropriate to model.
Once potential hot spots have been identified, field verification is recommended. Through these visits, the overriding factor causing the segment to be a hot spot can be identified and may provide some insight as to what treatments may be appropriate on a given site.
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4c. Identifying appropriate BMPs:
The next step in this process is to decide what appropriate treatment(s) can be applied to these problematic road segments. This can be done in two ways. The first is through field visits to every hot spot of interest and identification of any and all appropriate BMPs for that site. In doing so, site specific BMPs or combinations of BMPs can be identified for a given site.
An alternative method for identifying applicable BMPs is to apply generic BMPs to site conditions identified through WEPP: Road field surveys. For example, a drain dip may be an applicable treatment for a road segment if the road segment is longer than a designated length.
Efta (2009, Chapter 3) provides a set of guidelines used for BMP assignment in the Lake Tahoe
Basin.
There are some tradeoffs between these two BMP assignment methods. Often times, not all segments can be visited to identify appropriate BMPs. Visiting every site in the field may require large investments of time (and money), or may be impossible due to weather. However, without visiting every site there is no guarantee that a generic BMP would be suitable on a given site. If not automated, the process of assigning generic BMPs to problematic road segments requires tedious computer work. This process could be automated using scripts, but still requires computer expertise. These tradeoffs are at the discretion of the manager implementing this methodology.
4d. Assessing effectiveness of BMP treatments:
After applicable BMPs have been identified for erosion hot spots, some assessment must be made of how much sediment can be saved through BMP implementation. WEPP: Road provides the capability of assessing BMP effectiveness for numerous but not all BMPs. For example, if the best treatment for a segment is deemed surfacing using gravel, the WEPP: Road input template can be changed to “G” for the cell designating surface type. As another example, drain dip implementation can be modeled by splitting the segment in half, modeling erosion, and doubling the sediment prediction (Figure 7). These segments can be re-run in WEPP: Road.
If WEPP: Road does not have the capacity to model a given BMP type, or if not using WEPP:
Road for sediment estimation, some other estimate of BMP effectiveness must be made. This estimate can be made through expert judgment, empirical modeling, or physical measurement.
These predictions must then be incorporated into the new_BMP.txt file in the BMP-SA input files folder. This process will be explained in the following section.
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Figure 8. Example changes to WEPP: Road Batch inputs for assessing BMP effectiveness. Original WEPP: Road inputs (top) can be altered to represent BMP implementation (middle and bottom), then modeled again using WEPP: Road.
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5a. Description/formatting of input text files:
As mentioned before, there are four text files that provide inputs to BMP-SA. Each is discussed in further detail below. For graphical input file examples, see Section 4f.
First, some general notes about text file structuring. Spacing between values and characters is very important. Too many spaces or nonexistent spaces will produce erroneous model results.
The best way to avoid spacing errors is to enter and format data within a spreadsheet, then save as text. If, however, data is translated from spreadsheet to text and a cell was left blank, the file will be read incorrectly. Be sure to double check your text file inputs prior to inputting them into
BMP-SA. If BMP names have two words, either enter them as one word (ex. “draindip”) or use an underscore in the name (ex. “drain_dip”).
A.
Existing BMP infrastructure input table
File name: C:\BMP-SA\BMP-SA input files\existing_road_network.txt
This text file contains two columns: segment number and sediment leaving the buffer from that road segment.
Segment number [space] sediment leaving buffer (tons)
Sediment leaving the buffer should be converted to tons per year. Segment number should be an integer. It is important that segments are consecutively numbered , starting at one.
B.
New BMP input table
File name: C:\BMP-SA\BMP-SA input files\new_BMP.txt
The purpose of this text file is to supply the model with possible BMP options for a given segment. BMP names are limited to 20 characters. The model will accept up to four possible
BMP options per segment, including “none”, or no BMP applied to the road network at that point. This text file should be formatted as follows:
Segment number [space] BMP name [space] installation cost [space] maintenance cost
[space] maintenance frequency [space] number of pieces of installation machinery [space] number of pieces of maintenance machinery [space] sediment leaving buffer (tons)
[space]….
This pattern should be repeated four times, with no space at the end of the line. If there are greater than four possible treatment options, include only the best three options besides
“none.” If there are fewer than four possible treatment options, enter “none” and zeroes for each of the extra BMP slots. In the first BMP slot within the text file, “none” (and the
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associated zeroes) should be included with each segment in the new_BMP.txt file.
Otherwise, there may be no feasible solutions under modeling scenarios with limited budget.
Segment numbers need not be consecutive, but must correspond with segment numbers in the existing_road_network.txt file.
Note that if “none” is used as an applicable option, “0” should be used for the amount of sediment leaving the buffer. This may be slightly confusing because if modeling is warranted, the segment likely has greater than zero sediment leaving the buffer.
Forest Service Road Construction Cost Estimating Guides are helpful references for estimating installation and maintenance costs. Personal experience may be invaluable for establishing maintenance frequencies and costs for site-specific treatments. Perhaps a restatement of the obvious, but establishing and inputting the best possible cost estimates will yield more applicable model results.
C.
Distance between delivery points lookup table
File name: C:\BMP-SA\BMP-SA input files\lookup_table.txt
This table is used to quantify equipment scheduling costs. Within the model’s clustering subroutines, segments with delivery points less than the specified threshold distance between one another are incorporated into a cluster. The model assumes that a given segment can only lie in one cluster. After clusters are formed, the model than adds up machine move-in costs for all clusters in each planning period.
Table format is simple: ID number [space] from segment [space] to segment [space] distance
The lookup table should start at the number of the first segment being assessed for alternative
BMP scenarios, then should have a distance to every other segment being modeled with alternative BMP scenarios. Distances should be between delivery points for two segments or, if two segments share a delivery point, make the distance between the segments a small value greater than zero. Leave out distances from a given point to itself (e.g. segment 6 to segment
6 0.000000). Also, don’t include reciprocal segment distances (e.g. don’t include segment 7 to segment 6 after including segment 6 to segment 7).
Distance unit doesn’t matter, so long as the threshold cluster distance given by the user in the
User-defined parameter input file is the same as the unit used within the lookup table. ID number is not segment number, only an index for a given from-to segment distance.
Depending on available data, formulating the lookup table may be tricky. ArcGIS’s Network
Analyst toolset provides one means of formulating the table. Create a network dataset from the GIS roads layer that has been repartitioned into hydraulically contiguous segments. Use the Origin-Destination (OD) Cost Matrix function, then export the “lines” attribute table. Be sure that there are no breaks in your vector layer, otherwise OD Cost Matrix will not work
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properly. Open the exported attribute table in spreadsheet format, remove unnecessary columns, change location names, and save as text.
If data is unavailable or the user does not wish to account for equipment scheduling costs, there are two options. The lookup table can have one line with dummy segment and distance values (e.g. 1 1100 1101 0.0000), or threshold clustering distance can be set very low but greater than zero.
D.
User-defined input parameter file
File name: C:\BMP-SA input files\User_input.txt
Model users must provide the model with multiple critical bits of information in this file.
Data in this text file is formatted as a list of numbers.
Because the order of these input parameters is very specific, a template is included in the input file folder. Inputs for this file are formatted as follows:
Initial temperature
Final temperature
Cooling rate
Number of iterations at a given temperature
Initial budget per period
Length of planning horizon
Cost for one piece of equipment to be moved in to a work site
Cluster radius
Total number of segments on road network
Total number of road segments that can have BMPs applied to them
Initial temperature, final temperature, cooling rate, and number of iterations at a given temperature:
Initial temperature, final temperature, cooling rate, and number of iterations at a given temperature are all related input parameters. Forming an integral part of the annealing metaphor, these values are not actual temperature values in the algorithm. Rather, these variables let the user define number of iterations the model runs before stopping and reporting a best solution. In addition, these input variables define the degree of solution variability within which the model operates. If initial temperature is set close to the final temperature and/or cooling rate is relatively high, the solution may be nowhere near the global optimum. Conversely, providing the model with a large temperature interval and a slow (small) cooling rate could cause the model to run more iterations than necessary to solve the problem.
Not only should width of the temperature interval be considered, but so too should absolute value. Absolute value plays an important role in overall solution accuracy. For example, if
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the difference between sediment leaving the buffer is .01, a final temperature of 2 will not provide a near optimum solution. Rather, a more applicable final temperature would be closer to .01 or .001 (or perhaps even smaller).
Finding the ideal temperature interval and cooling rate will require some experimentation by the user. For approximately 170 segments, each producing on average less than .1 ton per year sediment leaving the buffer, Efta found an ideal initial and final temperature combination of 5 to .00005, with a cooling rate of .99 degrees every 15 iterations (2009 unpublished data).
Initial budget per period:
Initial budget per period is the maximum dollar amount that can be allotted to BMP installation, maintenance, and equipment scheduling per year. This budget may not be completely used by the end of the year, but cannot be transferred to another planning period.
If attempting to find an optimum budget where sediment can be minimized over the course of the planning horizon, users may wish to start with an unlimited (large) initial budget and scale backwards to establish the most ideal budget.
Length of planning horizon:
Enter length of desired planning horizon in years. Remember that planning horizon is limited to 100 years and that planning periods within BMP-SA are fixed at one year.
Cost for one piece of equipment to be moved in to a work site:
This cost may vary significantly between national forests or regions. BMP-SA assumes that equipment move-in costs are fixed and equal no matter what the treatment is. If not wishing to account for equipment costs, set this value to greater than zero but very low (ex. .00001).
Cluster radius:
Cluster radius should reflect how many BMPs can be installed or maintained by machine operators in one work session. Cluster radius unit should correspond to segment length unit and distance unit used in the lookup table. If no equipment scheduling is needed, set the cluster radius to 1.
Total number of segments on road network:
Total number of segments on the road network should include all segments modeled for erosion on the forest road network of interest. This value should match the total number of erosion estimates contained in the existing road network text file.
Total number of road segments that can have BMPs applied to them:
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In all likelihood, not all road segments will be considered problematic; some may be predicted to produce no sediment leaving the buffer at all. For those segments that are causing sedimentation problems, some problematic road segments may not benefit from any further BMP implementation. In either of these situations, the number of segments that can have BMPs applied to them will be less than the total number of road segments on the road network.
5b. BMP-SA file format examples
Existing_road_network.txt file:
A B
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A.
Segment number
B.
Sediment leaving buffer (tons/yr) new_BMP.txt input file:
Segment number Alternative BMP 1 Alternative BMP 2
Alternative BMP 4 Alternative BMP 3
A B C D E F
A.
BMP name
B.
Installation cost ($)
C.
Maintenance cost ($)
D.
Maintenance frequency (years)
E.
Number of pieces of equipment necessary for installation
F.
Number of pieces of equipment necessary for maintenance
G.
Sediment leaving buffer (tons/yr)
H.
Next BMP (repeat same pattern)
G H
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lookup_table.txt input file:
A B C D
A: Index
B: From segment number
C: To segment number
D: Distance between delivery points
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User_input.txt input file:
A
B
C
D
E
F
G
H
I
J
A.
Initial temperature
B.
Final temperature
C.
Cooling rate
D.
Number of iterations spent at one temperature level
E.
Initial budget per period ($)
F.
Length of planning horizon (years)
G.
Equipment move-in cost ($/machine)
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H.
Number of segments on road network
I.
Number of segments with alternative BMPs
Once all data is formatted into the four input text files, the user can simply click on the BMP-
SA.exe icon and the program will run. While the program is running, numbers will scroll down in the DOS dialog box indicating the number of model iterations already executed. Upon completion, the dialog box will automatically close.
6a. Model outputs
A.
Per-segment cost and maintenance summary
File name: C:\BMP-SA output files\BMP-SA_results.txt
This text file contains multiple outputs of interest to the user. Total sediment leaving the buffer through the course of the planning horizon can be found in this text file. The cost of the best solution can be found below total sediment leaving the buffer. This is the total cost of installation, maintenance, and equipment move-in for the entire planning horizon. This will not be the same as the initial budget per period added together for all periods, since initial budget per period is not necessarily used up in each period. The initial budget value used by the model is displayed below the previous two outputs (Figure 9).
Following these values reported in the header, the text file contains columns including segment number, BMP installed, period of installation, maintenance frequency, maintenance cost, and installation cost. Though there is an offset between column headings and associated values, this allows the user to readily open and manipulate the text file in a spreadsheet.
Figure 9. BMP-SA per-segment cost and maintenance summary text file.
B.
Per-period cost summary
File name: C:\BMP-SA output files\BMP-SA_results2.txt
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Per-period maintenance and installation costs are summarized in this text file, along with total cost per period. This total cost per period includes installation and maintenance equipment move-in costs. Above the per-period summary, the standard deviation between the last two solutions compared within BMP-SA is reported. Again, the file is formatted so that it can be opened in a spreadsheet (Figure 10).
Figure 10. Per-period cost summary text file.
6b. Assessing solution quality
An obvious question after running BMP-SA is, “How good is the solution??” With numerous possible solution outputs for a given set of model inputs, it is important for the user to critically assess solution quality.
The standard deviation included in the per-period cost summary text file provides an important metric for assessing the validity of the model output. By evaluating the difference between the last two solutions, the user can assess whether a near-global optimum solution was achieved. We suggest running the model several times and assessing the difference between standard deviations and solutions with the same user-defined variables. A standard deviation of zero means that the last two solutions compared by BMP-SA were predicted to produce the exact same amount of sediment leaving the buffer. If a model run yields a standard deviation of zero, run BMP-SA again.
Standard deviation will vary between solutions and datasets, which makes it difficult to set specific criteria defining what values are appropriate. When using small datasets where average sediment leaving the buffer per segment is less than .1, standard deviations of less than .1 have been associated with high-quality solutions. The user will have to do some experimenting to figure out what combination of values produces the best solution.
No matter what standard deviation the user finds initially, we recommend trying several different temperature-cooling rate-iteration number combinations. If so desired, users can open the range_plot.txt file in a spreadsheet and create a scatter plot of solution values. BMP-SA will be producing near-optimum solution results if the plot looks like that seen in Figure 14.
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45
40
35
30
25
20
0 2000 4000 6000 8000 10000 12000 14000 16000 18000
Number of iterations
Figure 11. Example of solution value change with iteration number in a minimization problem using simulated annealing optimization.
6c. Troubleshooting
If when running BMP-SA the dialog box opens, the cursor blinks and no numbers scroll down the screen, wait for a few seconds. When the initial budget is small compared to the number and cost of BMPs available, it may take BMP-SA some time before a feasible initial solution can be formulated. In some instances, the model may not find a feasible initial solution within a reasonable time frame. In these cases, click the “X” in the upper right hand corner of the dialog box to stop the program. Choose a higher budget, and click on the executable button again.
Should the model stop after reporting multiple iterations and not continue on, close the program
(again using the “X” in the upper right hand corner of the dialog box) and check your input text files. In these instances, there is likely a text file input error that is causing an inappropriate data value to be saved in a given memory slot. Correct and save the text files, then try running BMP-
SA again.
If either of these scenarios persists after troubleshooting, contact model developers.
Contact person:
Woodam Chung
Department of Forest Management
College of Forestry and Conservation
The University of Montana
32 Campus Drive
Missoula, MT 59812
(406) 243-6606 woodam.chung@umontana.edu
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