back to first page [..][[BR]] back to ["River width computation"] [[BR]] __Other model__ :[[BR]] - Elvira model : model 4 with river >2nd order[[BR]] Log10(Wet width + 1) = 0.22734+ 0.20045 (log10 catchment area) + 0.25939 (log 10 Shreve index)[[BR]] - Thornton model : W = 4.98 CA^0.47 [[BR]] 4. __Models tested__:[[BR]] '''Linear regression''' * Model 1 : width ~ a*catchment_area + b * Model 2 : log(width) ~ a*log(up_area)+ b * Model 3 : log(width) ~ a*log(up_area) + uga + b * Model 4 : log(width) ~ a*log(up_area) + b*log(shreve index) + c * Model 5 : log(width) ~ a*log(up_area) + b*log(shreve index) + uga + c '''Non linear regression''' with library(nlme) * Model 1: width~alpha*up_area^beta * Model 2 : width~gamma*shreve^lambda (à tester : width~gamma*strahler^lambda * Data extraction : * year > 1987 (RHP) * Period : april-october * complete sampling method * largeur lame eau >0 * CV largeur lame eau <= 5% * shreve >1 ? (pris en compte) NB : f=a(x^b )[[BR]] On pose A=log(a), F=log(f), X=log(x)[[BR]] F=log(f)=log(a)+b log(x)=A+bX [[BR]] La représentation graphique donne une droite, ce qui permet de déterminer A et b, puis de calculer a grâce à la relation A=log(a). La méthode dite "de régression linéaire par les moindres carrés" est utilisée.[[BR]] __Model Validation for linear regression__ * Residuals versus fitted values to verify homogeneity * QQ-plot or histogram of the residuals for normality * Residuals versus each explanatory variable to check independence ["River width graphics"] __Model results__ 1618 stations ||df||AIC|| ||lm1|| 3|| 9706.075|| ||lm2|| 3|| 2595.026|| ||lm3|| 12|| 2472.912|| ||lm4|| 4|| 2375.609|| ||lm5|| 13|| 2354.614|| --> model lm2 width=1.88*area^0.24 Estimate Std. Error t value Pr(>|t|) [[BR]] (Intercept) 0.63593 0.03924 16.20 <2e-16 ***[[BR]] log(ers0_station2$st_up_area) 0.24624 0.00892 27.61 <2e-16 ***[[BR]] --> model lm4 retenu avec 41% de la variation expliquée[[BR]] Estimate Std. Error t value Pr(>|t|) [[BR]] (Intercept) 0.90824 0.04071 22.312 < 2e-16 ***[[BR]] log(ers0_station2$st_up_area) 0.09331 0.01297 7.195 9.53e-13 ***[[BR]] log(ers0_station2$shreve) 0.20489 0.01331 15.390 < 2e-16 ***[[BR]] * '''nls1<-nls(st_cs_largeurlameeau~gamma*st_up_area^lambda, data=ers0_station2,start=list(gamma=5,lambda=0.5))''' summary(nls1) Estimate Std. Error t value Pr(>|t|) [[BR]] gamma 2.25662 0.12533 18.00 <2e-16 ***[[BR]] lambda 0.23981 0.01031 23.26 <2e-16 ***[[BR]] [[Image(source:data/Docs/trac/RiverWidth/nls1_width_up_area.jpeg)]] [[BR]] * '''nls2<-nls(st_cs_largeurlameeau~gamma*shreve^lambda, data=ers0_station2,start=list(gamma=3.33,lambda=0.28)) summary(nls2)'''[[BR]] Estimate Std. Error t value Pr(>|t|) [[BR]] gamma 4.108061 0.121048 33.94 <2e-16 ***[[BR]] lambda 0.227287 0.008639 26.31 <2e-16 ***[[BR]] [[Image(source:data/Docs/trac/RiverWidth/nls2_width_shreve.jpeg)]] [[BR]] * '''nls3<-nls(st_cs_largeurlameeau~alpha*st_up_area^beta +gamma*shreve^lambda, data=subset(ers0_station2,ers0_station2$st_up_area<=8000),ers0_station2,start=list(alpha=1.37,beta=0.32,gamma=3.33,lambda=0.28))'''[[BR]] summary(nls3)[[BR]] Parameters:[[BR]] Estimate Std. Error t value Pr(>|t|) [[BR]] alpha 0.44572 0.32403 1.376 0.169 [[BR]] beta 0.36923 0.09191 4.017 6.16e-05 ***[[BR]] gamma 2.60166 0.62737 4.147 3.55e-05 ***[[BR] lambda 0.23017 0.03480 6.614 5.07e-11 ***[[BR]] --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 [[BR]] Residual standard error: 4.077 on 1611 degrees of freedom[[BR]] Number of iterations to convergence: 22 [[BR]] Achieved convergence tolerance: 8.651e-06 [[BR]] --> st_cs_largeurlameeau ~ 0.44 * st_up_area^0.36+ 2.6 * shreve^0.23 [[BR]] * '''loess''' loess.smooth(ers0_station2$st_up_area[ers0_station2$st_up_area<800],ers0_station2$st_cs_largeurlameeau[ers0_station2$st_up_area<800])[[BR]] [[Image(source:data/Docs/trac/RiverWidth/loess_width_up_area.jpeg)]] [[BR]] ["Some comments"]