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Informe_Belesar.R
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library(here)
source(here::here('libraries.R'))
DHI<- readRDS(here::here('Data/Parques/Belesar/Historico/Historico_DHI_Belesar.RDS'))
head(DHI)
plot(DHI$`APORTACION (m3/s)`,
xlab = "Date",
ylab= "Aportacion [m³/s]")
outlierKD <- function(dt, var) {
var_name <- eval(substitute(var),eval(dt))
tot <- sum(!is.na(var_name))
na1 <- sum(is.na(var_name))
m1 <- mean(var_name, na.rm = T)
par(mar = rep(2, 4))
par(mfrow=c(2, 2), oma=c(0,0,3,0))
boxplot(var_name, main="With outliers")
hist(var_name, main="With outliers", xlab=NA, ylab=NA)
outlier <- boxplot.stats(var_name)$out
mo <- mean(outlier)
var_name <- ifelse(var_name %in% outlier, NA, var_name)
boxplot(var_name, main="Without outliers")
hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
title("Outlier Check", outer=TRUE)
na2 <- sum(is.na(var_name))
message("Outliers identified: ", na2 - na1, " from ", tot, " observations")
message("Proportion (%) of outliers: ", (na2 - na1) / tot*100)
message("Mean of the outliers: ", mo)
m2 <- mean(var_name, na.rm = T)
message("Mean without removing outliers: ", m1)
message("Mean if we remove outliers: ", m2)
dt[as.character(substitute(var))] <- invisible(var_name)
assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
message("Outliers successfully removed", "\n")
return(invisible(dt))
}
DHI$`APORTACION (m3/s)`[which(DHI$`APORTACION (m3/s)`<0)]<- NA
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
outlierKD(DHI,`APORTACION (m3/s)`)
plot(DHI$`APORTACION (m3/s)`,
xlab = "Date",
ylab= "Aportacion [m³/s]",
type = "l")
sum(is.na(DHI$`APORTACION (m3/s)`))
library(imputeTS)
#Rellenamos Huecos
DHI$`APORTACION (m3/s)`<- na.interpolation(DHI$`APORTACION (m3/s)`)
#Comprobamos
sum(is.na(DHI$`APORTACION (m3/s)`))
library(TTR)
for(n in c(1,seq(48,192,length.out = 4))){
Mavg<- SMA(DHI$`APORTACION (m3/s)`, n)
plot(DHI$DATE, Mavg,
xlab = "Date",
ylab= "Aportacion [m³/s]",
type = "l",
main = paste0("Aportación en Belesar. MA= ", n))
}
outlierKD(DHI,`NIVEL EMBALSE (msnm)`)
DHI$`NIVEL EMBALSE (msnm)`<- na.interpolation(DHI$`NIVEL EMBALSE (msnm)`)
x<- 96
aportacion_SMA<- SMA(DHI$`APORTACION (m3/s)`,x)
nivel_SMA<- SMA(c(0,diff(DHI$`NIVEL EMBALSE (msnm)`)*8000+150), x)
aportacion_SMA<- aportacion_SMA[!is.na(aportacion_SMA)]
nivel_SMA<- nivel_SMA[!is.na(nivel_SMA)]
plot(aportacion_SMA,type = "l",
xlab =" " ,ylab = "",xaxt= 'n',yaxt='n',
main=paste0("Variacion del nivel y aportacion\n Correlación de: ",
round(cor(aportacion_SMA, nivel_SMA), 3)))
lines(nivel_SMA, col = "red")
ccf_belesar<- ccf(aportacion_SMA, nivel_SMA, lag.max = 5000)
#Máxima correlación
max(ccf_belesar$acf)
#Con cuanto desfase se produce la máxima correlación.
ccf_belesar$lag[which.max(ccf_belesar$acf)]
#Retrasamos hacia detrás todos los datos de lluvia porque la acumulada de toda la hora la suma en la hora siguiente....
DHI$`LLUVIA ACUMULADA DÍA (l/m2)`<- lead(DHI$`LLUVIA ACUMULADA DÍA (l/m2)`)
Desacumular_lluvia_2018<- DHI[which(year(DHI$DATE)==2018),] %>% group_by(yday(DATE)) %>% mutate(desacumulada= c(diff(`LLUVIA ACUMULADA DÍA (l/m2)`),0),
lluvia=ifelse(desacumulada>=0, desacumulada, 0))
Desacumular_lluvia_2019<- DHI[which(year(DHI$DATE)==2019),] %>% group_by(yday(DATE)) %>% mutate(desacumulada= c(diff(`LLUVIA ACUMULADA DÍA (l/m2)`),0),
lluvia=ifelse(desacumulada>=0, desacumulada, 0))
Desacumular_lluvia<- rbind(Desacumular_lluvia_2018, Desacumular_lluvia_2019)
Medias_horarias_2018<- as.data.frame(Desacumular_lluvia[year(Desacumular_lluvia$DATE)==2018,]) %>% group_by(yday(DATE), hour(DATE)) %>%
summarize(., Acum_horaria=sum(lluvia, na.rm = T),
aport_mean=mean(`APORTACION (m3/s)`, na.rm = T),
nivel_mean=mean(`NIVEL EMBALSE (msnm)`, na.rm = T))
Medias_horarias_2019<- as.data.frame(Desacumular_lluvia[year(Desacumular_lluvia$DATE)==2019,]) %>% group_by(yday(DATE),hour(DATE)) %>%
summarize(., Acum_horaria=sum(lluvia, na.rm = T),
aport_mean=mean(`APORTACION (m3/s)`, na.rm = T),
nivel_mean=mean(`NIVEL EMBALSE (msnm)`, na.rm = T))
Medias_horarias<- rbind(Medias_horarias_2018, Medias_horarias_2019)
vector_Date<- seq(range(DHI$DATE)[1],range(DHI$DATE)[2],
by="hour")
Lluvia_acum_horaria<- as.data.frame(cbind(as.character(vector_Date[2:length(vector_Date)]),
Medias_horarias[,3:5]))
Lluvia_acum_horaria$`as.character(vector_Date[2:length(vector_Date)])`<- ymd_hms(Lluvia_acum_horaria$`as.character(vector_Date[2:length(vector_Date)])`)
colnames(Lluvia_acum_horaria)<- c("Date", "Lluvia_mm", "aport_mean", "nivel_mean")
for (n in seq(6,24*3, by=6)) {
y<- n
aportacion_mean_SMA<- SMA(Lluvia_acum_horaria$aport_mean,y)
nivel_mean_SMA<- SMA(Lluvia_acum_horaria$nivel_mean,y)
Tabla_DHI<- as.data.frame(cbind(Lluvia_acum_horaria,
aportacion_mean_SMA,
nivel_mean_SMA))
colnames(Tabla_DHI)<- c(names(Lluvia_acum_horaria), "aport_mean_SMA", "nivel_mean_SMA")
Tabla_DHI<- Tabla_DHI[complete.cases(Tabla_DHI),]
ccf_aport_mean<- ccf(Tabla_DHI$aport_mean_SMA,
Tabla_DHI$nivel_mean_SMA,
lag.max = 1000,
plot = T)
print(paste0("Máxima correlacion de ",
round(max(ccf_aport_mean$acf), digits = 3) ,
" .Para un desfase de: ",
round(ccf_aport_mean$lag[which.max(ccf_aport_mean$acf)]/24, digits = 2),
" días. Con un SMA de :",
n,
" horas"))
}
#se selecciona 96 porque es la cantidad de datos que corresponden a 1 día.
x<- 96
aportacion_SMA<- SMA(DHI$`APORTACION (m3/s)`,x)
nivel_SMA<- SMA(DHI$`NIVEL EMBALSE (msnm)`, x)
aportacion_horaria<- aportacion_SMA[DHI$DATE%in%vector_Date]
nivel_horario<- nivel_SMA[DHI$DATE%in%vector_Date]
#Se ponen 142 horas porque se obtienen las mejoras correlaciones de esta manera
y<- 24
aportacion_mean_SMA<- SMA(Lluvia_acum_horaria$aport_mean,y)
nivel_mean_SMA<- SMA(Lluvia_acum_horaria$nivel_mean,y)
Tabla_DHI<- as.data.frame(cbind(Lluvia_acum_horaria,
aportacion_horaria[2:length(aportacion_horaria)],
nivel_horario[2:length(nivel_horario)],
aportacion_mean_SMA,
nivel_mean_SMA))
colnames(Tabla_DHI)<- c(names(Lluvia_acum_horaria), "aport_SMA", "nivel_SMA",
"aport_mean_SMA", "nivel_mean_SMA")
Tabla_DHI<- Tabla_DHI[complete.cases(Tabla_DHI),]
k<- max(Tabla_DHI$nivel_mean_SMA)/max(Tabla_DHI$aport_mean_SMA)
ggplot(data=Tabla_DHI, aes(x=Date))+
geom_line(aes(y=aport_mean_SMA))+
xlab("Date")+ylab("Aportación [m³/s]")+
theme(panel.background = element_blank(),
panel.grid = element_blank())+
geom_line(aes(y=nivel_mean_SMA/k),group=1, color="red") +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / (1/k), name = "Nivel [msnm]",
breaks = seq(min(Tabla_DHI$nivel_mean_SMA),
max(Tabla_DHI$nivel_mean_SMA),
by=10)),
breaks = seq(min(Tabla_DHI$aport_mean_SMA),
max(Tabla_DHI$aport_mean_SMA),
by=20)) +
ggtitle("Nivel y aportación. SMA (144 horas) de la media horaria")
ccf_DHI<- ccf(Tabla_DHI$aport_mean_SMA,
Tabla_DHI$nivel_mean_SMA,
lag.max = 1000)
print(paste0("Máxima correlacion de ",
round(max(ccf_DHI$acf), digits = 3) ,
" .Para un desfase de: ",
round(ccf_DHI$lag[which.max(ccf_DHI$acf)]/24, digits = 2),
" días"))
k<- max(Tabla_DHI$nivel_SMA)/max(Tabla_DHI$aport_SMA)
ggplot(data=Tabla_DHI, aes(x=Date))+
geom_line(aes(y=aport_SMA))+
xlab("Date")+ylab("Aportación [m³/s]")+
theme(panel.background = element_blank(),
panel.grid = element_blank())+
geom_line(aes(y=nivel_SMA/k),group=1, color="red") +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / (1/k), name = "Nivel [msnm]",
breaks = seq(min(Tabla_DHI$nivel_SMA),
max(Tabla_DHI$nivel_SMA),
by=10)),
breaks = seq(min(Tabla_DHI$aport_SMA),
max(Tabla_DHI$aport_SMA),
by=20)) +
ggtitle("Nivel y aportación. SMA (96 datos = 1 día) de los datos 15-minutales")
ccf_DHI<- ccf(Tabla_DHI$aport_SMA,
Tabla_DHI$nivel_SMA,
lag.max = 4000)
print(paste0("Máxima correlacion de ",
round(max(ccf_DHI$acf), digits = 3) ,
" .Para un desfase de: ",
round(ccf_DHI$lag[which.max(ccf_DHI$acf)]/24, digits = 2),
" días"))
k<- max(Tabla_DHI$nivel_mean)/max(Tabla_DHI$aport_mean)
ggplot(data=Tabla_DHI, aes(x=Date))+
geom_line(aes(y=aport_mean))+
xlab("Date")+ylab("Aportación [m³/s]")+
theme(panel.background = element_blank(),
panel.grid = element_blank())+
geom_line(aes(y=nivel_mean/k),group=1, color="red") +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / (1/k), name = "Nivel [msnm]",
breaks = seq(min(Tabla_DHI$nivel_mean),
max(Tabla_DHI$nivel_mean),
by=10)),
breaks = seq(min(Tabla_DHI$aport_mean),
max(Tabla_DHI$aport_mean),
by=20)) +
ggtitle("Nivel y aportación. Medias horarias")
ccf_DHI<- ccf(Tabla_DHI$aport_mean,
Tabla_DHI$nivel_mean,
lag.max = 1000)
print(paste0("Máxima correlacion de ",
round(max(ccf_DHI$acf), digits = 3) ,
" .Para un desfase de: ",
round(ccf_DHI$lag[which.max(ccf_DHI$acf)]/24, digits = 2),
" días"))
k<- max(Tabla_DHI$Lluvia_mm)/max(Tabla_DHI$aport_mean_SMA)
ggplot(data=Tabla_DHI, aes(x=Date))+
geom_line(aes(y=aport_mean_SMA))+
xlab("Date")+ylab("Aportación [m³/s]")+
theme(panel.background = element_blank(),
panel.grid = element_blank())+
geom_line(aes(y=Lluvia_mm/k),group=1, color="red") +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / (1/k), name = "Nivel [msnm]",
breaks = seq(min(Tabla_DHI$Lluvia_mm),
max(Tabla_DHI$Lluvia_mm),
by=10)),
breaks = seq(min(Tabla_DHI$aport_mean_SMA),
max(Tabla_DHI$aport_mean_SMA),
by=20)) +
ggtitle("Lluvia y aportacion")
ccf_DHI<- ccf(Tabla_DHI$aport_mean_SMA,
Tabla_DHI$Lluvia_mm,
lag.max = 1000)
print(paste0("Máxima correlacion de ",
round(max(ccf_DHI$acf), digits = 3) ,
" .Para un desfase de: ",
round(ccf_DHI$lag[which.max(ccf_DHI$acf)]/24, digits = 2),
" días"))
Tabla_DHI_cut<- Tabla_DHI[which(month(Tabla_DHI$Date)==12), ]
k<- max(Tabla_DHI_cut$Lluvia_mm)/max(Tabla_DHI_cut$aport_mean_SMA)
ggplot(data=Tabla_DHI_cut, aes(x=Date))+
geom_line(aes(y=aport_mean_SMA))+
xlab("Date")+ylab("Aportación [m³/s]")+
theme(panel.background = element_blank(),
panel.grid = element_blank())+
geom_line(aes(y=Lluvia_mm/k),group=1, color="red") +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / (1/k), name = "Nivel [msnm]",
breaks = seq(min(Tabla_DHI_cut$Lluvia_mm),
max(Tabla_DHI_cut$Lluvia_mm),
by=10)),
breaks = seq(min(Tabla_DHI_cut$aport_mean_SMA),
max(Tabla_DHI_cut$aport_mean_SMA),
by=20)) +
ggtitle("Lluvia y aportacion. Diciembre")
ccf_DHI<- ccf(Tabla_DHI_cut$aport_mean_SMA,
Tabla_DHI_cut$Lluvia_mm,
lag.max = 1000)
print(paste0("Máxima correlacion de ",
round(max(ccf_DHI$acf), digits = 3) ,
" .Para un desfase de: ",
round(ccf_DHI$lag[which.max(ccf_DHI$acf)]/24, digits = 2),
" días"))
path_dataDHI <- here::here('Data/Parques/Belesar/Historico/DHI_historico_afinado.RDS')
#sRDS(Tabla_DHI,path_dataDHI)
#Listamos archivos dentro de la carpeta de Belesar
All_files_Belesar<- list.files(here::here('Data/Parques/Belesar/'),
recursive = F, full.names = T)
#Detectamos cuales son RDS
RDS_Belesar<- All_files_Belesar[str_detect(All_files_Belesar, ".RDS")]
#Eliminamos los RDS que no son de WRF
RDS_Belesar1<- RDS_Belesar[!str_detect(RDS_Belesar, "E001")]
#Construimos Lista para cada instante de tiempo
Lista_localizacion<- list()
for (i in 1:length(RDS_Belesar1)) {
Belesar_data<- readRDS(RDS_Belesar1[i])
Belesar_lolat<- lon_lat_df_ls(Belesar_data)
Belesar_lolat1<- lapply(Belesar_lolat, uv_transformation)
Belesar_rain<- lapply(Belesar_lolat1, extract_rain_data2)
Lista_localizacion[[i]]<- Belesar_rain
}
#Nombramos la lista
names_fechas<- sapply(RDS_Belesar1, function(x){
r<- str_split(x, "/")
return(str_remove(str_remove(r[[1]][length(r[[1]])], ".RDS"), "Belesar_"))
})
names(Lista_localizacion)<- names_fechas
#Creamos una lista por localización
Lista_localizacion2<- list()
for (i in 1:length(Lista_localizacion[[1]])) {
Lista_localizacion2[[i]]<- lapply(Lista_localizacion,
function(x) return(x[[i]]))
}
#Nombramos la lista
names(Lista_localizacion2)<- names(Lista_localizacion[[1]])
#Guardamos la lista
path_lista_total<- here::here('Data/Parques/Belesar/Historico/')
nombre_lista<- paste0(path_lista_total, 'Historico_WRF_Belesar_Variables.RDS')
#sRDS(Lista_localizacion2, nombre_lista)
#Cargamos lista
path_lista_total<- here::here('Data/Parques/Belesar/Historico/')
nombre_lista<- paste0(path_lista_total, 'Historico_WRF_Belesar_Variables.RDS')
Lista_total1<- readRDS(nombre_lista)
#Juntamos todos los Dataframes
Lista_total_MF<- lapply(Lista_total1, function(x) bind_rows(x))
#creamos dos data.frames... uno para D1 y otro para D2
Lista_d1_d2_loc<- list()
for (i in 1:length(Lista_total_MF)) {
p<- Lista_total_MF[[i]]
d1<- p[duplicated(p$fechas),]
d1<-d1[!duplicated(d1$fechas),]
d2<- p[!duplicated(p$fechas),]
d2_qneed1<-d2[!(d2$fechas%in%d1$fechas),]
d1_2<-bind_rows(d1,d2_qneed1)
d1_2<-d1_2[order(d1_2$fechas),]
d2<-d2[order(d2$fechas),]
d1_2$pre_acum<- NULL
d2$pre_acum<- NULL
colnames(d1_2)<- c("Date", "LON", "LAT", "RAINC", "RAINNC","RAINSH", "T02_MEAN","PSFC","WS_MAX", "prep_hourly")
colnames(d2)<- c("Date", "LON", "LAT", "RAINC", "RAINNC","RAINSH", "T02_MEAN","PSFC","WS_MAX", "prep_hourly")
lista_loc_d12<- list(d1_2,d2)
names(lista_loc_d12)<- c("D1", "D2")
Lista_d1_d2_loc[[i]]<- lista_loc_d12
}
#nombramos la lsta
names(Lista_d1_d2_loc)<- names(Lista_total_MF)
Tabla_periodo1<- Return_periodo_Belesar()
colnames(Tabla_periodo1)<- c("Date", "LON", "LAT", "RAINC", "RAINNC","RAINSH", "T02_MEAN","PSFC","WS_MAX", "prep_hourly")
Lista_d1_d2_loc2<- list()
for (j in 1:length(Lista_d1_d2_loc)) {
prueba_list<- Lista_d1_d2_loc[[j]]
lista_retorno<- list()
for(i in 1:2){
prueba<- prueba_list[[i]]
Tabla_periodo<- Tabla_periodo1
Tabla_periodo$LON[match(prueba$Date,Tabla_periodo$Date)] <- prueba$LON
Tabla_periodo$LAT[match(prueba$Date,Tabla_periodo$Date)] <- prueba$LAT
Tabla_periodo$RAINC[match(prueba$Date,Tabla_periodo$Date)] <- prueba$RAINC
Tabla_periodo$RAINNC[match(prueba$Date,Tabla_periodo$Date)] <- prueba$RAINNC
Tabla_periodo$RAINSH[match(prueba$Date,Tabla_periodo$Date)] <- prueba$RAINSH
Tabla_periodo$T02_MEAN[match(prueba$Date,Tabla_periodo$Date)] <- prueba$T02_MEAN
Tabla_periodo$PSFC[match(prueba$Date,Tabla_periodo$Date)] <- prueba$PSFC
Tabla_periodo$WS_MAX[match(prueba$Date,Tabla_periodo$Date)] <- prueba$WS_MAX
Tabla_periodo$prep_hourly[match(prueba$Date,Tabla_periodo$Date)] <- prueba$prep_hourly
lista_retorno[[i]]<- Tabla_periodo
}
names(lista_retorno)<- c("D1", "D2")
Lista_d1_d2_loc2[[j]]<- lista_retorno
}
names(Lista_d1_d2_loc2)<- names(Lista_d1_d2_loc)
#Creamos lista con las variables afinadas
Lista_d1_d2_loc3<- lapply(Lista_d1_d2_loc2, function(x){
x[[1]]$RAINC<- NULL
x[[1]]$RAINNC<- NULL
x[[1]]$RAINSH<- NULL
x[[2]]$RAINC<- NULL
x[[2]]$RAINNC<- NULL
x[[2]]$RAINSH<- NULL
return(x)})
names(Lista_d1_d2_loc3)<- names(Lista_d1_d2_loc2)
#Guardamos
path_hist_WRF<- here::here('Data/Parques/Belesar/Historico/Historico_WRF_Belesar_Variables_D1D2.RDS')
#saveRDS(Lista_d1_d2_loc3,path_hist_WRF)
#Usamos dplyr para juntar ambos datasets
Belesar_WRF<- readRDS(here::here('Data/Parques/Belesar/Historico/Historico_WRF_Belesar_Variables_D1D2.RDS'))
Belesar_DHI<- readRDS(here::here('Data/Parques/Belesar/Historico/DHI_historico_afinado.RDS'))
df2<- Belesar_DHI
Belesar_Merge<- list()
for (j in 1:length(Belesar_WRF)) {
lista_retorno<- list()
for(i in 1:2){
df1<- Belesar_WRF[[j]][[i]]
Merge_table<- left_join(df1, df2, by=c("Date"))
lista_retorno[[i]]<- Merge_table
}
names(lista_retorno)<- c("D1", "D2")
Belesar_Merge[[j]]<- lista_retorno
}
names(Belesar_Merge)<- names(Belesar_WRF)
#Belesar merge completecases
Belesar_Merge_cc<- list()
for (j in 1:length(Belesar_Merge)) {
lista_retorno<- list()
for(i in 1:2){
df1<- Belesar_Merge[[j]][[i]]
df1$Acumulated<- NULL
Table_fine<- df1[complete.cases(df1),]
lista_retorno[[i]]<- Table_fine
}
names(lista_retorno)<- c("D1", "D2")
Belesar_Merge_cc[[j]]<- lista_retorno
}
names(Belesar_Merge_cc)<- names(Belesar_Merge)
#Guardamos
path_hist_WRF<- here::here('Data/Parques/Belesar/Historico/Hist_D1D2_DHI_MERGED.RDS')
#saveRDS(Belesar_Merge_cc,path_hist_WRF)
## Probamos diferentes regresiones lineales
#importar
path_hist_WRF<- here::here('Data/Parques/Belesar/Historico/Hist_D1D2_DHI_MERGED.RDS')
clean_data<- readRDS(path_hist_WRF)
#cortar en entrenamiento y predicción
cut_train<- lapply(clean_data, function(x){
y<- lapply(x, function(r){
fecha_ini<- ymd("2018/10/01")
fecha_end<- ymd("2019/02/01")
Jan_data<- r[which(r$Date<fecha_end & r$Date>fecha_ini),]
return(Jan_data)
})
return(y)
})
cut_predict<- lapply(clean_data, function(x){
y<- lapply(x, function(r){
fecha_ini<- ymd("2019/02/01")
fecha_end<- ymd("2019/02/20")
Jan_data<- r[which(r$Date<fecha_end & r$Date>fecha_ini),]
return(Jan_data)
})
return(y)
})
lista_TL<- list()
for (i in 1:length(cut_train)) {
lista_d1d2<- list()
for (j in 1:2) {
data_predict<- cut_train[[i]][[j]]
data_predict2<- cut_predict[[i]][[j]]
fit_1 <- lm(Lluvia_mm ~ prep_hourly, data = data_predict)
fit_2 <- lm(Lluvia_mm ~ prep_hourly + T02_MEAN, data = data_predict)
fit_3 <- lm(Lluvia_mm ~ prep_hourly + PSFC, data = data_predict)
fit_4 <- lm(Lluvia_mm ~ prep_hourly + WS_MAX, data = data_predict)
fit_5 <- lm(Lluvia_mm ~ prep_hourly * T02_MEAN, data = data_predict)
fit_6 <- lm(Lluvia_mm ~ prep_hourly * PSFC, data = data_predict)
fit_7 <- lm(Lluvia_mm ~ prep_hourly * WS_MAX, data = data_predict)
uncorrected<-data_predict2$prep_hourly
prediction_rain<- predict(fit_1, data.frame(prep_hourly =data_predict2$prep_hourly))
prediction_rain2<- predict(fit_2, data.frame(prep_hourly =data_predict2$prep_hourly,
T02_MEAN =data_predict2$T02_MEAN))
prediction_rain3<- predict(fit_3, data.frame(prep_hourly =data_predict2$prep_hourly,
PSFC =data_predict2$PSFC))
prediction_rain4<- predict(fit_4, data.frame(prep_hourly =data_predict2$prep_hourly,
WS_MAX =data_predict2$WS_MAX))
prediction_rain5<- predict(fit_5, data.frame(prep_hourly =data_predict2$prep_hourly,
T02_MEAN =data_predict2$T02_MEAN))
prediction_rain6<- predict(fit_6, data.frame(prep_hourly =data_predict2$prep_hourly,
PSFC =data_predict2$PSFC))
prediction_rain7<- predict(fit_7, data.frame(prep_hourly =data_predict2$prep_hourly,
WS_MAX =data_predict2$WS_MAX))
observed_rain<-data_predict2$Lluvia_mm
tabla_cor<-cbind(cor(uncorrected, observed_rain),
cor(prediction_rain, observed_rain),
cor(prediction_rain2, observed_rain),
cor(prediction_rain3, observed_rain),
cor(prediction_rain4, observed_rain),
cor(prediction_rain5, observed_rain),
cor(prediction_rain6, observed_rain),
cor(prediction_rain7, observed_rain))
fit_1 <- svm(Lluvia_mm ~ prep_hourly, data = data_predict)
fit_2 <- svm(Lluvia_mm ~ prep_hourly + T02_MEAN, data = data_predict)
fit_3 <- svm(Lluvia_mm ~ prep_hourly + PSFC, data = data_predict)
fit_4 <- svm(Lluvia_mm ~ prep_hourly + WS_MAX, data = data_predict)
fit_5 <- svm(Lluvia_mm ~ prep_hourly * T02_MEAN, data = data_predict)
fit_6 <- svm(Lluvia_mm ~ prep_hourly * PSFC, data = data_predict)
fit_7 <- svm(Lluvia_mm ~ prep_hourly * WS_MAX, data = data_predict)
uncorrected<-data_predict2$prep_hourly
prediction_rain<- predict(fit_1, data.frame(prep_hourly =data_predict2$prep_hourly))
prediction_rain2<- predict(fit_2, data.frame(prep_hourly =data_predict2$prep_hourly,
T02_MEAN =data_predict2$T02_MEAN))
prediction_rain3<- predict(fit_3, data.frame(prep_hourly =data_predict2$prep_hourly,
PSFC =data_predict2$PSFC))
prediction_rain4<- predict(fit_4, data.frame(prep_hourly =data_predict2$prep_hourly,
WS_MAX =data_predict2$WS_MAX))
prediction_rain5<- predict(fit_5, data.frame(prep_hourly =data_predict2$prep_hourly,
T02_MEAN =data_predict2$T02_MEAN))
prediction_rain6<- predict(fit_6, data.frame(prep_hourly =data_predict2$prep_hourly,
PSFC =data_predict2$PSFC))
prediction_rain7<- predict(fit_7, data.frame(prep_hourly =data_predict2$prep_hourly,
WS_MAX =data_predict2$WS_MAX))
observed_rain<-data_predict2$Lluvia_mm
tabla_cor<-cbind(tabla_cor,
cor(prediction_rain, observed_rain),
cor(prediction_rain2, observed_rain),
cor(prediction_rain3, observed_rain),
cor(prediction_rain4, observed_rain),
cor(prediction_rain5, observed_rain),
cor(prediction_rain6, observed_rain),
cor(prediction_rain7, observed_rain))
colnames(tabla_cor)<- c("uncorrected", "1","2", "3","4","5","6","7",
"svm1","svm2", "svm3","svm4","svm5","svm6","svm7")
lista_d1d2[[j]]<- tabla_cor
}
names(lista_d1d2)<- c("D1", "D2")
lista_TL[[i]]<- lista_d1d2
}
colnames(tabla_cor)<- c("uncorrected", "1","2", "3","4","5","6","7",
"svm1","svm2", "svm3","svm4","svm5","svm6","svm7")
names(lista_d1d2)<- c("D1", "D2")
Lista_TL_names<-lapply(lista_TL, function(x){
y<- lapply(x, function(r) {
r<- as.data.frame(r)
names(r)<- c("uncorrected", "1","2", "3","4","5","6","7",
"svm1","svm2", "svm3","svm4","svm5","svm6","svm7")
return(r)
})
names(y)<- c("D1", "D2")
return(y)
})
names(Lista_TL_names)<- names(cut_predict)
x<- lapply(Lista_TL_names, function(x){bind_rows(as.data.frame(x))})
y<- bind_rows(x, .id="id")
cor_table<- as.data.frame(matrix(ncol = 3, nrow = 43))
colnames(cor_table)<- c("id","corr", "name")
for (i in 2:length(y[1,])) {
f<- as.data.frame(cbind(y[which.max(y[,i]), c(1,i)], names( y[which.max(y[,i]), c(1,i)])[2]))
colnames(f)<- c("id","corr", "name")
cor_table<- rbind(cor_table,f )
}
cor_table<- cor_table[complete.cases(cor_table),]
table(cor_table$id)
punto_Belesar<- c(42.628577,-7.713948)
extract_lat_lon<- lapply(str_split(cor_table$id,"__"), function(x){
return(as.data.frame(cbind(x[[1]],x[[2]])))
})
extract_lat_lon<- as.data.frame(bind_rows(extract_lat_lon))
cor_table<- as.data.frame(cbind(extract_lat_lon, cor_table[,2:3]))
colnames(cor_table)<- c("LON","LAT", "Corr", "Method")
library(geosphere)
vec_dist<- vector()
for (i in 1:length(cor_table[,1])) {
vec_dist[i]<- distm(c(-7.713948, 42.628577), c(as.numeric(cor_table[i,1]),
as.numeric(cor_table[i,2])),
fun = distHaversine)
}
vec_dist<- round(vec_dist/1000, digits = 1)
cor_table<- as.data.frame(cbind(cor_table, vec_dist))
cor_table$LON<-round(as.numeric(cor_table$LON), digits = 2)
cor_table$LAT<-round(as.numeric(cor_table$LAT), digits = 2)
cor_table$Corr<-round(as.numeric(cor_table$Corr), digits = 3)
cor_table
clean_data_oct<-lapply(clean_data, function(x){
y<- lapply(x, function(r) r[which(r$Date > ymd("2018/10/01")),])
})
#Plot_rain2 necesita columna Date y columna rain
plot_rain3<- function(data,data2, titulo){
ggplot(data=data, aes(x=Date))+
geom_line(aes(y=rain), stat="identity")+
xlab("Date")+ylab("Lluvia por hora [mm/h]")+theme(panel.background = element_blank(),
panel.grid = element_blank()) +
geom_line(data= data2, aes(x=Date, y=rain), color="red", alpha=0.5)+
geom_text(aes(as.POSIXct(ymd("2019/02/01")),10, label=paste0("Cor: ",round(cor(data$rain,data2$rain),
digits = 2)))) +
ggtitle(titulo)
}
for (i in 1:length(clean_data_oct)) {
data1<- as.data.frame(cbind(as.character(clean_data_oct[[i]][[1]]$Date),
clean_data_oct[[i]][[1]]$prep_hourly))
names(data1)<- c("Date", "rain")
data1$Date<- ymd_hms(data1$Date)
data1$rain<- as.numeric(as.character(data1$rain))
data2<- as.data.frame(cbind(as.character(clean_data_oct[[i]][[1]]$Date),
clean_data_oct[[i]][[1]]$Lluvia_mm))
names(data2)<- c("Date", "rain")
data2$Date<- ymd_hms(data2$Date)
data2$rain<- as.numeric(as.character(data2$rain))
plot_rain3(data = data1 ,
data2 = data2,
titulo = paste(str_split(names(clean_data_oct)[i], "_")[[1]], collapse = " "))
}
Cor_rain_place<- data.frame(matrix(ncol = 4))
colnames(Cor_rain_place)<- c("LON", "LAT", "Corr", "Dist")
for (i in 1:length(clean_data_oct)) {
Corr<- cor(clean_data_oct[[i]][[1]]$prep_hourly,
clean_data_oct[[i]][[1]]$Lluvia_mm)
LON<- as.numeric(str_split(names(clean_data_oct)[i], "_")[[1]][1])
LAT<- as.numeric(str_split(names(clean_data_oct)[i], "_")[[1]][3])
Dist<- distm(c(-7.713948, 42.628577),
c(as.numeric(LON),
as.numeric(LAT)),
fun = distHaversine)
Cor_rain_place[i,]<- as.data.frame(cbind(LON, LAT, Corr, Dist))
}
Cor_order<- Cor_rain_place[order(Cor_rain_place$Corr, decreasing = T),]
clean_data_jan<-lapply(clean_data_oct, function(x){
y<- lapply(x, function(r) r[which(r$Date > ymd("2019/01/01")),])
})
for (i in 1:length(clean_data_jan)) {
data1<- as.data.frame(cbind(as.character(clean_data_jan[[i]][[1]]$Date),
clean_data_jan[[i]][[1]]$prep_hourly))
names(data1)<- c("Date", "rain")
data1$Date<- ymd_hms(data1$Date)
data1$rain<- as.numeric(as.character(data1$rain))
data2<- as.data.frame(cbind(as.character(clean_data_jan[[i]][[1]]$Date),
clean_data_jan[[i]][[1]]$Lluvia_mm))
names(data2)<- c("Date", "rain")
data2$Date<- ymd_hms(data2$Date)
data2$rain<- as.numeric(as.character(data2$rain))
x<- plot_rain3(data = data1 ,
data2 = data2,
titulo = paste(str_split(names(clean_data_jan)[i], "_")[[1]], collapse = " "))
print(x)
}
clean_data_jan<-lapply(clean_data_oct, function(x){
y<- lapply(x, function(r) r[which(r$Date > ymd("2019/01/01")),])
})
Cor_rain_place<- data.frame(matrix(ncol = 4))
colnames(Cor_rain_place)<- c("LON", "LAT", "Corr", "Dist")
for (i in 1:length(clean_data_jan)) {
Corr<- cor(clean_data_jan[[i]][[1]]$prep_hourly,
clean_data_jan[[i]][[1]]$Lluvia_mm)
LON<- as.numeric(str_split(names(clean_data_jan)[i], "_")[[1]][1])
LAT<- as.numeric(str_split(names(clean_data_jan)[i], "_")[[1]][3])
Dist<- distm(c(-7.713948, 42.628577),
c(as.numeric(LON),
as.numeric(LAT)),
fun = distHaversine)
Cor_rain_place[i,]<- as.data.frame(cbind(LON, LAT, Corr, Dist))
}
Cor_order<- Cor_rain_place[order(Cor_rain_place$Corr, decreasing = T),]
### Correlación data desde diciembre
clean_data_dec<-lapply(clean_data, function(x){
y<- lapply(x, function(r) r[which(r$Date > ymd("2018/12/01")),])
})
Daiyli_avgs<- lapply(clean_data_dec, function(x){
x[[1]] %>% group_by(year(Date), yday(Date)) %>%
summarize(rWRF_acum= sum(prep_hourly),
rDHI_acum= sum(Lluvia_mm),
rWRF_mean= sum(prep_hourly),
rDHI_mean= sum(Lluvia_mm),
aport_SMA_diario=mean(aport_SMA),
aport_mean_diario=mean(aport_mean),
aport_mean_SMA_diario=mean(aport_mean_SMA),
nivel_SMA_diario=mean(nivel_SMA),
nivel_mean_diario=mean(nivel_mean),
nivel_mean_SMA_diario=mean(nivel_mean_SMA))
})
Daiyli_avgs2<- lapply(Daiyli_avgs, function(x){
x$Date<- ymd(ifelse(x$`year(Date)`==2018,
as.character(as.Date(x$`yday(Date)`, origin= "2018-01-01")),
as.character(as.Date(x$`yday(Date)`, origin= "2019-01-01"))))
x$`year(Date)`<- NULL
x$`yday(Date)`<- NULL
return(x)
})
Cor_rain_place<- data.frame(matrix(ncol = 11))
for (i in 1:length(Daiyli_avgs2)) {
Corr1<- cor(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$aport_SMA_diario)
Corr2<- cor(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$aport_mean_diario)
Corr3<- cor(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$aport_mean_SMA_diario)
Corr4<- cor(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$rDHI_acum)
Corr5<- cor(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$aport_SMA_diario)
Corr6<- cor(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$aport_mean_diario)
Corr7<- cor(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$aport_mean_SMA_diario)
Corr8<- cor(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$rDHI_mean)
LON<- as.numeric(str_split(names(Daiyli_avgs2)[i], "_")[[1]][1])
LAT<- as.numeric(str_split(names(Daiyli_avgs2)[i], "_")[[1]][3])
Dist<- distm(c(-7.713948, 42.628577),
c(as.numeric(LON),
as.numeric(LAT)),
fun = distHaversine)
Cor_rain_place[i,]<- as.data.frame(cbind(LON, LAT, Corr1,
Corr2,Corr3,Corr4,Corr5,
Corr6,Corr7,Corr8, Dist))
}
colnames(Cor_rain_place)<- c("LON","LAT","AWRF_aport1","AWRF_aport2", "AWRF_aport3","AWRF_ADHI",
"MWRF_aport1","MWRF_aport2", "MWRF_aport3","MWRF_MDHI","Dist")
Cor_rain_place_ccf<- data.frame(matrix(ncol = 11))
for (i in 1:length(Daiyli_avgs2)) {
ccfr1<- ccf(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$aport_SMA_diario)
ccfr2<- ccf(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$aport_mean_diario)
ccfr3<- ccf(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$aport_mean_SMA_diario)
ccfr4<- ccf(Daiyli_avgs2[[i]]$rWRF_acum,
Daiyli_avgs2[[i]]$rDHI_acum)
ccfr5<- ccf(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$aport_SMA_diario)
ccfr6<- ccf(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$aport_mean_diario)
ccfr7<- ccf(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$aport_mean_SMA_diario)
ccfr8<- ccf(Daiyli_avgs2[[i]]$rWRF_mean,
Daiyli_avgs2[[i]]$rDHI_mean)
LON<- as.numeric(str_split(names(Daiyli_avgs2)[i], "_")[[1]][1])
LAT<- as.numeric(str_split(names(Daiyli_avgs2)[i], "_")[[1]][3])
Dist<- distm(c(-7.713948, 42.628577),
c(as.numeric(LON),
as.numeric(LAT)),
fun = distHaversine)
Cor_rain_place_ccf[i,]<- as.data.frame(cbind(LON, LAT, max(ccfr1$acf),
max(ccfr2$acf),max(ccfr3$acf),max(ccfr4$acf),max(ccfr5$acf),
max(ccfr6$acf),max(ccfr7$acf),max(ccfr8$acf), Dist))
print(paste("Mejor corr para un retardo de: ",
ccfr1$lag[which.max(ccfr1$acf)],
ccfr2$lag[which.max(ccfr2$acf)],
ccfr3$lag[which.max(ccfr3$acf)],
ccfr4$lag[which.max(ccfr4$acf)],
ccfr5$lag[which.max(ccfr5$acf)],
ccfr6$lag[which.max(ccfr6$acf)],
ccfr7$lag[which.max(ccfr7$acf)],
ccfr8$lag[which.max(ccfr8$acf)], sep = " "))
}
colnames(Cor_rain_place_ccf)<- c("LON","LAT","AWRF_aport1","AWRF_aport2", "AWRF_aport3","AWRF_ADHI",
"MWRF_aport1","MWRF_aport2", "MWRF_aport3","MWRF_MDHI","Dist")