HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

More documents

Recommendations

Info

Having the background knowledge necessary for HIPM to conduct successful runs we designed thirty one experiments; each experiment represents a possible combination of time-series constraints that could potentially be entered into the software. For example, if we had time-series for Iron and Nitrate and fed the information into HIPM they would act as additional constraints in the model selection process. To be selected, models have to exhibit behavior close to the given time-series. All the experiments are summarized in Table 4 . 19
3 COMPUTATIONAL RESULTS The main topic in this paper, is to determine how to optimize the usage we make of HIPM to assist scientists in there decision making process when it comes to selecting a model that most accurately represent an ecosystem. The first need is to narrow down the number of possible good fit models capable of describing the system. We did this feeding additional time series about one of the state variable into HIPM, thus providing more constraints; so did this assumption hold true Secondly, if adding more constraints to HIPM does reduce that number, are observations for a specific state variable holding more reducing power than the other state variables The data collected helped us answer these questions as well as discuss the efficiency of HIPM in its current state. There were thirty-one different experiments performed, each returning a measure of fit value (reMSE) every one of the 1120 models tested in every experiment. This makes for a large amount of data to analyze. To get a better idea of what this data looks like, the measures of fit values of models that had an reMSE between 0 and 2 were graphed, ranking and graphing them from lowest to highest (see Figure 4, 5 and 6) value. We did not look at reMSE higher than 2.0 since, as stated previously, models with reMSE higher than 1.0 are typically classified as poorly performing models as it indicates a very large difference between observed and expected values. We estimated that the (0,2) range would be sufficient for our purpose, as it would encompass most models. Based on these initial results we decided to pick an reMSE of 0.5 as our good fit model cutoff; any model under that cutoff is considered of good fit. This choice of cutoff was made because the multiple graphs seemed to exhibit a turning point or slight step pattern around this reMSE value, such as portrayed in the graph for experiments 1, 5 or 20. 20
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: upon whether certain time-series ar
Page 31 and 32: ● ● ● 2.0 ● [D,N] ●●●
Page 33 and 34: 3.1 Increase in number of time-seri
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 ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?