Fuzzy Traffic Light Methods by W. Silvert, IPIMAR, Portugal and P. Fanning, R. Halliday, and R. Mohn DFO, Canada Why are we doing this? The first question to be asked about any approach is why it is needed. SGPA Term of Reference D is to revise the description of PA concepts to make them more intelligible for non-fishery users. The Traffic Light Approach is one of the types of descriptions currently under investigation to simplify the process of management decision-making. The Traffic Light Method The Precautionary Approach (and Risk Management in general, not just for fisheries) requires that masses of complex data be presented clearly to managers, fishermen and other stakeholders. The Traffic Light Method is an easily understood way of presenting information about stock conditions. Indicators & Characteristics We speak of Indicators, which are basic properties of the system, and Characteristics, which are integrated variables representing several Indicators. Abundance is a typical Characteristic, since it represents the result of combining several Indicators, such as: Research Trawl data VPA analysis Catch per Unit Effort Standard Traffic Lights Each Indicator or Characteristic is represented by a single traffic light, red, yellow or green in the standard traffic light representation. There is no smooth transition, just two sharp lines separating red-yellow and yellow-green. The meaning of the lights can be very sensitive to the location of these cuts. Example: 4VsW Cod A “crisp” traffic light indicator sharp transitions between colours make positioning the boundaries very critical A lot of information is lost Criteria for Improvement The objective is to develop a more general approach with the following characteristics: Resolution Uncertainty Weighting Resolution The most serious problem with the standard traffic light method is the way that the lights change discontinuously when the Indicators change smoothly. There is general agreement that there must be a more gradual representation of the significance of changing indicators. Uncertainty A less obvious point, but one which is clearly relevant to fisheries management, is the need to represent the degree of uncertainty in the interpretation of Indicators, and to provide a mechanism for expressing conflicting evidence or interpretation. Weighting It is also clear that not all Indicators are equally significant. They can be: Of varying accuracy Of different relevance Of dubious value New and untested Alternative Approaches Most alternatives to the standard traffic light method use some sort of averaging to show that an Indicator is on the border between red and yellow or between yellow and green. One example is using intermediate colours, such as orange between red and yellow. Fuzzy Traffic Lights Fuzzy Sets offer one way to improve the standard traffic light method. With fuzzy traffic lights an Indicator can correspond to more than one light. For example, instead of using orange to show that an Indicator is on the red- yellow boundary, we can simply show both red and yellow lights. Advantages of Fuzzy Fuzzy traffic lights are continuous, we can switch between colours gradually to achieve higher resolution. Fuzzy traffic lights show uncertainty if we illuminate several lights at once. Fuzzy traffic lights can be weighted to show relative importance of indicators. Memberships The key idea behind Fuzzy Set Theory is that something can belong to more than one set at a time. When we say that a light is red, that means that it belongs to the set “red”. With fuzzy sets we can have a light that is 50% in set red and 50% in yellow. Membership Example Let the amount of each light displayed vary with the level of the indicator Traffic Light Fuzzy Set Strict Default Rules 0.6*Mean Mean 1.25 1 0.75 Degree 0.5 0.25 0 0 5000 10000 15000 20000 25000 30000 35000 Indicator Fuzzy Indicators Use a combination of colours gradual transitions show uncertainty and contain more information than solid colour bars Note that some bars have multiple colours Application to Haddock 4TVW Haddock Summer RV #/tow(26-41cm) 75 Note how much data is included on this 60 figure, and how easy it is to see a pattern 45 30 15 0 Abundance Production FishingM Management Char Weight Summer RV #/tow(26-41cm) Abun 1.0 Summer RV #/tow(42cm+) Abun 1.0 Sentinel (kg/set) Abun 1.0 Area occupied(30cm+) Abun 1.0 Density(30cm+) Abun 0.5 VPA SSB Abun 1.0 Area occupied(1-29cm) Prod 1.0 Density(1-29cm) Prod 0.5 VPA Rec Prod 1.0 Summer RV Condition Prod 0.5 Summer RV growth age7(len) Prod 1.0 Spring RV 50% mat Prod 1.0 Spring RV condition Prod 0.5 Misaine Temperature Prod 0.5 Exploitation (%)(ages 5-10) Fish 1.0 Fraction over 42 Mana 1.0 Cod SSB Mana 1.0 1970 1975 1980 1985 1990 1995 2000 C:/1paulf/PA/hadd6c_fuzzy_traffic.txt Application to White Hake We have no VPA results, but we still can present an assessment Current Developments 4X/5 white hake July Survey numbers (>45) 24000 18000 12000 6000 0 3 year trend Abundance Production Fishing Mortality Environment Char Weight July Survey numbers (>45) Abun 1.0 ITQ Survey numbers/set Abun 1.0 Halibut Survey numbers/set Abun 1.0 July Area Occupied (>45cm) Abun 0.5 July Survey mean weight Abun 0.5 July Survey Z Prod 1.0 July Survey numbers (<45) Prod 1.0 July Area Occupied (<45cm) Prod 0.5 Condition Factor Prod 0.5 Relative F Fish 1.0 Temperature (Area >6C) Envi 1.0 1970 1975 1980 1985 1990 1995 2000 C:/1paulf/PA/TLwhhakesimple4x_2.txt Uncertain Reference Levels Wide yellow zone reflects uncertainty 4X/5 white hake ITQ Survey numbers/set 9.5 7.6 5.7 3.8 1.9 3 year trend Abundance Production Fishing Mortality Environment Char Weight July Survey numbers (>45) Abun 1.0 ITQ Survey numbers/set Abun 1.0 Halibut Survey numbers/set Abun 1.0 July Area Occupied (>45cm) Abun 0.5 July Survey mean weight Abun 0.5 July Survey Z Prod 1.0 July Survey numbers (<45) Prod 1.0 July Area Occupied (<45cm) Prod 0.5 Condition Factor Prod 0.5 Relative F Fish 1.0 Temperature (Area >6C) Envi 1.0 1970 1975 1980 1985 1990 1995 2000 C:/1paulf/PA/TLwhhakesimple4x_2.txt Fuzzy Rules The use of Fuzzy Traffic Lights to represent stock status means that we also use fuzzy rules to make management decisions. Some typical (and familiar) fuzzy rules: IF it feels cold THEN light a fire IF you are hungry THEN eat something Fuzzy rules are like crisp rules: IF the temperature falls below 14.7º C THEN switch on the heater Fuzzy Control of Fisheries Fuzzy rules are of the form: IF (condition) THEN (act) IF management= green AND production= green AND abundance= green THEN tac_increment is large positive IF production= green AND abundance= green THEN tac_increment is small positive IF production= green AND abundance=yellow THEN tac_increment is no change IF production=green AND abundance=red THEN tac_increment is small negative IF production=yellow AND abundance=green THEN tac_increment is no change IF production=yellow AND abundance=yellow THEN tac_increment is small negative IF production=yellow AND abundance=red THEN tac_increment is large negative IF production=red AND abundance=green THEN tac_increment is small negative IF production=red AND abundance<>green THEN tac_increment is large negative Displaying Fuzzy Lights There are several ways to show a fuzzy traffic light: Bubble charts, which look a lot like real traffic lights Pie charts, which display information more quantitatively Stacked bar graphs, which are less familiar but very effective Bubble Charts A Bubble Chart looks like a regular traffic light, but the sizes of the ”lights” are proportional to the membership in each of the three sets, red yellow & green. Pie Charts A pie chart looks less like a traffic light, but it gives a more quantitative picture of how much of each light is lit, The area of each slice represents the fuzzy membership. Stacked Bar Graphs A stacked bar graph is somewhat like a traffic light with rectangular bulbs. The area of each part of the bar represents the membership in the corresponding set. Choosing the Display The bubble chart resembles traffic lights most, but it does not give a good sense of the quantitative information about memberships. The pie chart and the stacked bar graph both represent the relative memberships clearly. Displaying Weighting The bubble graph does not give a good idea of the relative weights of the different Indicators. By varying the diameter of the pie charts or the width of the bar graphs we can show the relative importance of different indicators. At present weighting has not been well implemented in trial applications and it is difficult to achieve agreement on it. Comparison of Pie Charts Comparison of Bar Graphs Conclusions Traffic Lights offer a clear way to present complex fisheries data. Fuzzy Traffic Lights provide more information with little loss of clarity.
Pages to are hidden for
"Fuzzy Traffic Light Methods"Please download to view full document