HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

More documents

Recommendations

Info

2.0 [D,N,F] 25 1.5 1.0 0.5 0.0 1.5 1.0 0.5 0.0 ● ●● ● ● ●●●● ● ●●● ● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● 39 Good Fit Models 2.0 [P,Z,N,F] 28 2 Good Fit Models ● ● ●● ● ●●● ● ● ● ●● ● ●● [P,Z,D,N] 0 Good Fit Models [P,D,N,F] 5 Good Fit Models ● ● ●● ● ● ● ●● ●● ●● ● ● ●●●● 0 200 400 600 800 1000 26 29 ●● ●●● ● ●●●● ● ● [P,Z,D,F] 0 Good Fit Models [Z,D,N,F] 0 Good Fit Models ●● ● ●● ● ●●● ●● ●● ● ● ● ● 0 200 400 600 800 1000 ● ●●●● 27 30 ● 2.0 [P,Z,D,N,F] 31 1.5 ●● 1.0 0.5 0.0 0 Good Fit Models 0 200 400 600 800 1200 Figure 6: reMSE value are ranked from lowest to highest. The reMSE = 0.5 signifies the good fit model cutoff, any models under that value are considered good fit models. The experimental setup for each run as well as the ID number is indicated in the top right corner. 23
3.1 Increase in number of time-series input One of the first observations that was made when looking at the data set, is that the general trend was the more time-series were used in HIPM the smaller the number of good fit models, as represented in Figure 7. Number of Good Fit Models (reMSE
Page 1 and 2: HIERARCHAL INDUCTIVE PROCESS MODELI
Page 3 and 4: APPENDIX . . . . . . . . . . . . .
Page 5 and 6: DEDICATION This Thesis is dedicated
Page 7 and 8: LIST OF TABLES 1 Example of entity
Page 9 and 10: LIST OF SYMBOLS P = Amount of Phyto
Page 11 and 12: models of natural systems are non-u
Page 13 and 14: Figure 1: This schematic represent
Page 15 and 16: specifying the space of possible eq
Page 17 and 18: phytoplankton productivity and comm
Page 19 and 20: 2 METHOD The method employed in thi
Page 21 and 22: set of values that respect the cons
Page 23 and 24: fitted such as for “conc”, with
Page 25 and 26: Table 2: Defining a process - Growt
Page 27 and 28: upon whether certain time-series ar
Page 29 and 30: 3 COMPUTATIONAL RESULTS The main to
Page 31: ● ● ● 2.0 ● [D,N] ●●●
Page 35 and 36: Table 4: This table represents each
Page 37 and 38: different restriction powers based
Page 39 and 40: experiment 22, yielding no good fit
Page 41 and 42: not taken into consideration which
Page 43 and 44: Table 5: This table summarizes all
Page 45 and 46: Table 6: This table summarizes all
Page 47 and 48: 4.2 Preliminaries Both Models A and
Page 49 and 50: In addition to these 2 Lemmas, let
Page 51 and 52: When t → ∞ we get the following
Page 53 and 54: For the Zooplankton not to go to ze
Page 55 and 56: ( ∴ P 0 + a ) 13 α − δ Z 0 e
Page 57 and 58: 4.4 Model B Shifting our focus to M
Page 59 and 60: Using the exogenous variables time-
Page 61 and 62: Then finding an lower bound, dD l d
Page 63 and 64: Thus, using Proposition 1 we get, d
Page 65 and 66: ∴ a 23 = 4.5.10 −4 ≤ F (t)
Page 67 and 68: Model C Where, dP dt dZ dt dD dt dN
Page 69 and 70: Next we turn our attention to P (t)
Page 71 and 72: We can rewrite D u (t) as, D u (t)
Page 73 and 74: his research on the Ross Sea Phytop
Page 75 and 76: zooplankton to be properly fitted m
Page 77 and 78: eally well (Atanasova et al. 2007).
Page 79 and 80: [9] Dzeroski, S. and Todorovski, L.
Page 81 and 82: APPENDIX A. Sample CIAO data - 1997
Page 83 and 84:
"death_rate":0.02, "respiration_rat
Page 85 and 86:
"death_rate": (0.02,0.04), "Ek_max"
Page 87 and 88:
# --- GROWTH --- lib.add_generic_pr
Page 89 and 90:
"arrigoetal1998", "light_lim", [("P
Page 91 and 92:
lib.add_generic_process( "death_exp
Page 93 and 94:
lib.add_generic_process( "holling_t
Page 95 and 96:
[], {"max_mixing_rate":(0.000001,1)
Page 97 and 98:
Model E [ dP [ ] dt = (1 − E ice
Page 99 and 100:
Model G [ dP [ ] ( dt = (1 − E ic
Page 101:
BIOGRAPHICAL SKETCH I was born and
show all

HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

Create successful ePaper yourself

Delete template?

Save as template?