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ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-05-15 Roundup Benoit Parmentier

ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-05-15 Roundup Benoit Parmentier. What I have been working on: 1 ) GAM prediction for 365 dates and first round up of results Assessing results across the year : results per month. 2) GAM prediction: model diagnostics and residuals

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ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-05-15 Roundup Benoit Parmentier

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  1. ENVIRONMENTAL LAYERS MEETING IPLANT TUCSON 2012-05-15 Roundup Benoit Parmentier

  2. What I have been working on: • 1) GAM prediction for 365 dates and first round up of results • Assessing results across the year: results per month. • 2) GAM prediction: model diagnostics and residuals • Contribution of variables, land cover and variables • Outliers: searching for patterns. • Improving screening of unreliable observations. • 3) Examining the effect of sampling on the results • Examining the RMSE for different training and testing samples • Examining the RMSE for the different hold out proportions • 4) Incorporating spatial information: Kriging and spatial filtering • GAM + Kriging: results • Spatial eigenvectors: first steps… • 5) Comparing results to the literature • - Review • - RMSE, MAE, BIAS

  3. GAM PREDICTION FOR 365 DAYS GAM models were run for every day in year 2010 and RMSE were summarized by month to look for patterns.

  4. GAM MODELS USED FOR THE ANALYSIS Using monthly LST mean… mod1<- tmax~ s(lat) + s (lon) + s (ELEV_SRTM) mod2<- tmax~ s(lat,lon) +s(ELEV_SRTM) mod3<- tmax~ s(lat) + s (lon) + s (ELEV_SRTM) + s (Northness)+ s (Eastness) + s(DISTOC) mod4<- tmax~ s(lat) + s (lon) + s(ELEV_SRTM) + s(Northness) + s (Eastness) + s(DISTOC) + s(LST) mod5<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST) mod6<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC1) mod7<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC3) mod8<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST) + s(LC1)

  5. SUMMARIZING RMSE RESULTS PER MONTH: MOD1

  6. SUMMARIZING RMSE RESULTS PER MONTH: MOD2

  7. SUMMARIZING RMSE RESULTS PER MONTH: MOD3

  8. SUMMARIZING RMSE RESULTS PER MONTH: MOD4

  9. SUMMARIZING RMSE RESULTS PER MONTH: MOD5

  10. SUMMARIZING RMSE RESULTS PER MONTH: MOD6

  11. SUMMARIZING RMSE RESULTS PER MONTH: MOD7

  12. MEAN AND MEDIAN OVER 365 days… > median_r mod1 mod2 mod3 mod4 mod5 mod6 mod7 mod8 24.30157 23.75949 24.11623 24.12670 23.84741 23.82039 24.20816 23.83253 >

  13. 3) EXAMINING THE EFFECT OF SAMPLING ON THE RESULTS Sampling was randomly done 30 times for different 10 dates. Seven GAM models are performed and RMSE are computed for each.

  14. TESTING INFLUENCE OF SAMPLING ON MODEL RESULTS > median_r mod1 mod2 mod3 mod4 mod5 mod6 mod7 22.49635 23.77285 22.52123 22.88943 22.1857622.50351 22.33177 mod5<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST)

  15. TESTING INFLUENCE OF SAMPLING ON MODEL RESULTS Median and Averages were calculated for 300 runs (300x10dates). > median_r mod1 mod2 mod3 mod4 mod5 mod6 mod7 23.57426 24.31624 23.39801 23.44873 22.27103 22.56197 22.61572 mod5<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST)

  16. TESTING INFLUENCE OF SAMPLING ON MODEL RESULTS Median and Averages were calculated for 260 runs (26x10dates). The first results indicate that models with the inclusion of LST have lowest median RMSE. mod5<- tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST)

  17. 4) TWO STAGE REGRESSION: GAM + KRIGING

  18. GAM+Kriging: MEAN AND MEDIAN FOR GAM (r1) and GAMKR (r2) This figure shows the mean and median for the eight GAM models for 10 dates. The “r2” models areGAM+KR models. Results: There is an improvement from mod1 to mod3 (simple models) both in the mean and median RMSE  There is an slight improvement in mean RMSE for models using LST but the median RMSE does not show an overall improvement.

  19. What I be working on: • 1) GAM prediction for 365 dates and first round up of results • Assessing results across the year: results per month. • 2) Examining the effect of sampling on the results • Examining the RMSE for different training and testing samples • Examining the RMSE for the different hold out proportions • 2) GAM prediction: model diagnostics and residuals • Contribution of variables, land cover and variables: gam.vcomp • Outliers: searching for patterns. • Screening of unreliable observations: based on temporal statistics and climatology. • 4) Incorporating spatial information: Kriging and spatial filtering • GAM + Kriging: results • Spatial eigenvectors: first steps… • 5) Comparing results to the literature • - Review literature. • - RMSE, MAE, BIAS

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