background problems with existing models (bi) a separable point process model testing separability
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Applications of point process modeling, separability testing, & estimation to wildfire hazard assessment. Background Problems with existing models (BI) A separable point process model Testing separability Alarm rates & other basic assessment techniques. Earthquakes: next lecture. - PowerPoint PPT PresentationTRANSCRIPT
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Applications of point process modeling, separability testing, & estimation to wildfire hazard assessment
1. Background2. Problems with existing models (BI)3. A separable point process model4. Testing separability5. Alarm rates & other basic assessment techniques
Earthquakes: next lecture.
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Los Angeles County wildfires, 1960-2000
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Background Brief History.
• 1907: LA County Fire Dept.• 1953: Serious wildfire suppression.• 1972/1978: National Fire Danger Rating System.
(Deeming et al. 1972, Rothermel 1972, Bradshaw et al. 1983)• 1976: Remote Access Weather Stations (RAWS).
Damages.• 2003: 738,000 acres; 3600 homes; 26 lives.(Oct 24 - Nov 2: 700,000 acres; 3300 homes; 20 lives)• Bel Air 1961: 6,000 acres; $30 million.• Clampitt 1970: 107,000 acres; $7.4 million.
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NFDRS’s Burning Index (BI): Uses daily weather variables, drought index, and
vegetation info. Human interactions excluded.
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Some BI equations: (From Pyne et al., 1996:)
Rate of spread: R = IR (1 + w+ s) / (b Qig). Oven-dry bulk density: b = w0/.
Reaction Intensity: IR = ’ wn h Ms. Effective heating number: = exp(-138/).
Optimum reaction velocity: ’ = ’max ( / op)A exp[A(1- / op)].
Maximum reaction velocity: ’max = 1.5 (495 + 0.0594 1.5) -1.
Optimum packing ratios: op = 3.348 -0.8189. A = 133 -0.7913.
Moisture damping coef.: M = 1 - 259 Mf /Mx + 5.11 (Mf /Mx)2 - 3.52 (Mf /Mx)3.
Mineral damping coef.: s = 0.174 Se-0.19
(max = 1.0).
Propagating flux ratio: = (192 + 0.2595 )-1 exp[(0.792 + 0.681 0.5)( + 0.1)].
Wind factors: w = CUB (/op)-E. C = 7.47 exp(-0.133 0.55). B = 0.02526 0.54. E = 0.715 exp(-3.59 x 10-4 ).
Net fuel loading: wn = w0 (1 - ST). Heat of preignition: Qig = 250 + 1116 Mf.
Slope factor: s = 5.275 -0.3 (tan 2. Packing ratio: = b / p.
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On the Predictive Value of Fire Danger Indices:
From Day 1 (05/24/05) of Toronto workshop:• Robert McAlpine: “[DFOSS] works very well.”• David Martell: “To me, they work like a charm.”• Mike Wotton: “The Indices are well-correlated with fuel moisture and fire
activity over a wide variety of fuel types.”• Larry Bradshaw: “[BI is a] good characterization of fire season.”
Evidence?
• FPI: Haines et al. 1983 Simard 1987 Preisler 2005Mandallaz and Ye 1997 (Eur/Can), Viegas et al. 1999 (Eur/Can), Garcia Diez et al. 1999 (DFR), Cruz et al. 2003 (Can).
• Spread: Rothermel (1991), Turner and Romme (1994), and others.
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Some obvious problems with BI:• Too additive: too low when all variables are med/high
risk.
• Low correlation with wildfire. Corr(BI, area burned) = 0.09 Corr(BI, # of fires) = 0.13 Corr(BI, area per fire) = 0.076! Corr(date, area burned) = 0.06! Corr(windspeed, area burned) = 0.159
• Too high in Winter (esp Dec and Jan) Too low in Fall (esp Sept and Oct)
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Some obvious problems with BI:• Too additive: too high for low wind/medium RH,
Misses high RH/medium wind. (same for temp/wind).
• Low correlation with wildfire. Corr(BI, area burned) = 0.09 Corr(BI, # of fires) = 0.13 Corr(BI, area per fire) = 0.076! Corr(date, area burned) = 0.06! Corr(windspeed, area burned) = 0.159
• Too high in Winter (esp Dec and Jan) Too low in Fall (esp Sept and Oct)
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More problems with BI:
• Low correlation with wildfire. Corr(BI, area burned) = 0.09 Corr(BI, # of fires) = 0.13 Corr(BI, area per fire) = 0.076! Corr(date, area burned) = 0.06! Corr(windspeed, area burned) = 0.159
• Too high in Winter (esp Dec and Jan) Too low in Fall (esp Sept and Oct)
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r = 0.16(s
q m
)
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More problems with BI:
• Low correlation with wildfire. Corr(BI, area burned) = 0.09 Corr(BI, # of fires) = 0.13 Corr(BI, area per fire) = 0.076! Corr(date, area burned) = 0.06! Corr(windspeed, area burned) = 0.159
• Too high in Winter (esp Dec and Jan) Too low in Fall (esp Sept and Oct)
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Model Construction
• Relative Humidity, Windspeed, Precipitation, Aggregated rainfall over previous 60 days, Temperature, Date. • Tapered Pareto size distribution f, smooth spatial background .
(t,x,a) = 1exp{2R(t) + 3W(t) + 4P(t)+ 5A(t;60) + 6T(t) + 7[8 - D(t)]2} (x) g(a).
… More on the fit of this model later. First, how can we test whether a separable model like this is
appropriate for this dataset?
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Testing separability in marked point processes:
Construct non-separable and separable kernel estimates of by smoothing over all coordinates simultaneously or separately. Then compare these two estimates: (Schoenberg 2004)
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Testing separability in marked point processes:
May also consider:
S5 = mean absolute difference at the observed points.
S6 = maximum absolute difference at observed points.
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S3 seems to be most powerful for large-scale non-separability:
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However, S3 may not be ideal for Hawkes processes, and all these statistics are terrible for inhibition processes:
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For Hawkes & inhibition processes, rescaling according to the separable estimate and then looking at the L-function seems much more powerful:
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Testing Separability for Los Angeles County Wildfires:
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Statistics like S3 indicate separability, but the L-function after rescaling shows some clustering of size and date:
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r = 0.16(s
q m
)
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32(F)
(sq
m)
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Model Construction
• Wildfire incidence seems roughly separable.(only area/date significant in separability test)
• Tapered Pareto size distribution f, smooth spatial background .(t,x,a) = 1exp{2R(t) + 3W(t) + 4P(t)+ 5A(t;60)
+ 6T(t) + 7[8 - D(t)]2} (x) g(a).Compare with:
(t,x,a) = 1exp{2B(t)} (x) g(a), where B = RH or BI.
Relative AICs (Poisson - Model, so higher is better):
Poisson RH BI Model
0 262.9 302.7 601.1
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Comparison of Predictive Efficacy
False alarms
per year
% of fires correctly alarmed
BI 150: 32 22.3
Model : 32 34.1
BI 200: 13 8.2
Model : 13 15.1
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One possible problem: human interactions.…. but BI has been justified for decades based on its correlation
with observed large wildfires (Mees & Chase, 1993; Andrews and Bradshaw, 1997).
Towards improved modeling
• Time-since-fire (fuel age)
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41(years)
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Towards improved modeling
• Time-since-fire (fuel age)• Wind direction
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Towards improved modeling
• Time-since-fire (fuel age)• Wind direction• Land use, greenness, vegetation
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Greenness (UCLA IoE)
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(IoE)
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Towards improved modeling
• Time-since-fire (fuel age)• Wind direction• Land use, greenness, vegetation• Precip over previous 40+ days, lagged variables
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(cm
)
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Conclusions:(For Los Angeles County data, Jan 1976- Dec 2000:)
• BI is positively associated with fire incidence and burn area, though its predictive value seems limited.
• Windspeed has a higher correlation with burn area, and a simple model using RH, windspeed, precipitation, aggregated rainfall
over previous 60 days, temperature, & date outperforms BI.• For multiplicative models (and sometimes for additive models),
can estimate parameters separately.• Separability testing: S3 seems quite powerful.
Next lecture: earthquakes: Ogata’s residual analysis, prototypes, and non-simple point process models.