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MARopt.R
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MARopt.R
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library(shiny)
library(shinydashboard)
library(mco)
library(nsga2R)
library(swmmr)
library(lubridate)
library(zoo)
library(factorial2x2)
library(ggplot2)
library(shinybusy)
library(nloptr)
library(ggpubr)
library(rlang)
library(igraph)
library(DT)
library(readxl)
library(plotly)
library(viridis)
library(rio)
library(dplyr)
library(ggnewscale)
library(shinyFiles)
library(rlist)
library(hydroGOF)
library(DiagrammeR)
library(chron)
library(openxlsx)
library(gtools)
library(stringr)
#library(xlsx)
library(treemap)
library(data.tree)
library(DEoptim)
run_model=function(param,nodeID,inputrain,
inputNoder,inputNetwork,
inputinflow,sort1){
#####
Rainfall = as.matrix(inputrain[,-1])
T1 = nrow(Rainfall)
N1 = nrow(inputNoder)
rain_mat = matrix(0,ncol =N1,nrow = T1)
if(length(param)>0){
inputNoder$Retention = param
}
for(n in 1:(N1)){
ind_rain = which(colnames(Rainfall)==inputNoder$Rainfall[n])
rain1 = as.numeric(Rainfall[,ind_rain])*(inputNoder$MAP[n]/100000)*
(inputNoder$`Area (km2)`[n])
rain_mat[,n] = as.array(rain1*(1-inputNoder$Retention[n]))
}
######
Out_mat = rain_mat
inflow_mat = as.matrix(inputinflow[,-1])
N2 = nrow(inputNetwork)
for(n in sort1){
attr1 = which(inputNetwork$`To node`==n)
k = 1
if(length(attr1)>0){
share2 = inputNetwork$Share[attr1]
node2 = inputNetwork$`From node`[attr1]
# rain1 = as.matrix(rain_mat2[,attr1])
for(j in 1:length(attr1)){
share1 = share2[j]
if(j ==1){
sum1 = share1*Out_mat[,node2[j]]
}else{
sum1 = sum1+(share1*Out_mat[,node2[j]])
}
}
Out_mat[,n] = Out_mat[,n] + ((sum1 + inflow_mat[,n])*(1-inputNoder$Retention[n]))
}
}
return(Out_mat[,as.numeric(nodeID)])
}
obj_fun_calib = function(param,nodeID,Qobs,inputrain,
inputNoder,inputNetwork,
inputinflow,sort1){
#####
Rainfall = as.matrix(inputrain[,-1])
T1 = nrow(Rainfall)
N1 = nrow(inputNoder)
rain_mat = matrix(0,ncol =N1,nrow = T1)
inputNoder$Retention = param
for(n in 1:(N1)){
ind_rain = which(colnames(Rainfall)==inputNoder$Rainfall[n])
rain1 = as.numeric(Rainfall[,ind_rain])*(inputNoder$MAP[n]/100000)*
(inputNoder$`Area (km2)`[n])
rain_mat[,n] = as.array(rain1*(1-inputNoder$Retention[n]))
}
Out_mat = rain_mat
inflow_mat = as.matrix(inputinflow[,-1])
N2 = nrow(inputNetwork)
for(n in sort1){
attr1 = which(inputNetwork$`To node`==n)
k = 1
if(length(attr1)>0){
share2 = inputNetwork$Share[attr1]
node2 = inputNetwork$`From node`[attr1]
# rain1 = as.matrix(rain_mat2[,attr1])
for(j in 1:length(attr1)){
share1 = share2[j]
if(j ==1){
sum1 = share1*Out_mat[,node2[j]]
}else{
sum1 = sum1+(share1*Out_mat[,node2[j]])
}
}
Out_mat[,n] = Out_mat[,n] + ((sum1 + inflow_mat[,n])*(1-inputNoder$Retention[n]))
}
}
sim_q <- Out_mat[,as.numeric(nodeID)]
kge1 = hydroGOF::KGE(as.matrix(sim_q), Qobs)
return(kge1*-1)
}
##########UI: ############################
ui <- dashboardPage(
dashboardHeader(title = "MARopt"),
dashboardSidebar(
sidebarMenu(
menuItem("Calibration", tabName = "calib", icon = icon("dashboard")),
menuItem("Optimization", tabName = "Optimization", icon = icon("dashboard")),
menuItem("Optimization results", tabName = "optim_results", icon = icon("dashboard"))
)
),
dashboardBody(
add_busy_spinner(spin = "cube-grid", margins = c(0, 10), color = "#FFF"),
tabItems(
tabItem(tabName = "calib",
fluidRow(
h1("Calibration of the Node model"),
column(width = 12,
box(fileInput("file_calib", "Choose uncalibrated model file (.xlsx)",
accept = ".xlsx"),
numericInput("node1","Plot Node number",value = 5),
plotlyOutput("plot_current"),
status = "primary",
solidHeader = TRUE,
title = "Current model",
width = 2.4
),
box(
numericInput("num_calib","Number of iterations",value = 5),
actionButton("act_calib","Run calibration"),
textOutput("Calib"),
downloadButton("downloadcalib", "Export calibrated parameters (.csv)"),
plotlyOutput("plot_calib"),
status = "primary",
solidHeader = TRUE,
title = "Calibration",
width = 2.4
))
)),
tabItem(tabName = "Optimization",
fluidPage(
h1("Multi objective optimization of MAR"),
fileInput("file1", "Choose calibrated model file (.xlsx)", accept = ".xlsx"),
box(
numericInput("num1","Number of iterations",value = 200),
actionButton("act1","Run multi objective optimization"),
textOutput("MultiOpt"),
downloadButton("downloadData", "Export results")
)
)),
# First tab content
tabItem(tabName = "optim_results",
fluidPage(
h1("Optimization results"),
tabsetPanel(type = "tabs",
#tabPanel("Catchment", tableOutput("Cathment")),
tabPanel("Load optimization results (Multi objectives)",
fileInput("file2", "Choose .json result File", accept = ".json"),
numericInput("num6","Pareto solution",value = 1),
box(plotlyOutput("pareto_front"),title = "Optimization results"),
box(plotlyOutput("Rainfall_Runoff_pareto"),title = "Optimization results")),
tabPanel("Compare pareto solutions",
box(dataTableOutput("compare_pareto_input"),title = "Compare pareto solutions (input)"),
box(plotlyOutput("pareto_plot1"),title = "Solution 1"),
box(plotlyOutput("pareto_plot2"),title = "Solution 2"),
box(plotlyOutput("pareto_plot3"),title = "Solution 3"),
box(plotlyOutput("pareto_plot4"),title = "Solution 4"),
box(plotlyOutput("pareto_plot5"),title = "Solution 5"))
)
)
)
)
)
)
server <- function(input, output){
currresplot = reactive({
inFile <- input$file_calib
if(length(inFile)>0){
ExcelInOrig <- inFile$datapath
inputNoder= read_excel(ExcelInOrig, sheet='Nodes',skip=1)
inputNetwork= read_excel(ExcelInOrig, sheet='Network',skip=1)
inputrain= read_excel(ExcelInOrig, sheet="Rainfall")
inputcatch= read_excel(ExcelInOrig, sheet='catchments nodes',skip=1)
inputmeasures= read_excel(ExcelInOrig, sheet='Measures',skip=1)
inputinflow= read_excel(ExcelInOrig, sheet='Inflow')
inputmeasured= read_excel(ExcelInOrig, sheet='Measured flow')
###########################################################
nc = nrow(inputcatch) # number of catchments
ns = nrow(inputmeasures) # number of solutions
snames = inputmeasures$Measure # names of solutions
cnames = inputcatch$`catchment names` # names of catchments
node_catch =inputcatch$`catchment outlet node`
cost1 = inputmeasures$Cost # cost per m2 of NBS, DAM, pump
rate1 = inputmeasures$`Mm3/m2/month` # rate of recharge per month per m2
outlet1 = as.numeric(inputcatch$node[1]) # outlet of the whole system
kt = as.data.frame(t(inputcatch[,c(5:(5+ns-1))])) # transportation factor
ke = as.data.frame(t(inputcatch[,c((5+ns):(5+ns+ns-1))])) # energy factor
kl = as.data.frame(t(inputcatch[,c((5+ns+ns):(5+ns+ns+ns-1))])) # local condition factor
limit1 = as.data.frame(t(inputcatch[,c((5+ns+ns+ns):(5+ns+ns+ns+ns-1))])) # limit of NBS for each catchment
inf_cap = as.numeric(inputcatch$`Mm3/m2/month`) # infiltration capacity of each basin
###########################################################
tree_data = as.data.frame(inputNetwork)[c(1,2,3)]
colnames(tree_data) <- c('from', 'to', 'w')
tree1 <- graph_from_data_frame(tree_data)
sort1 = topo_sort(tree1,mode = "out")
nodeID = input$node1
Qobs = as.data.frame(inputmeasured[,(nodeID+1)])
######################
qsim = run_model(param=NULL,
nodeID=nodeID,
inputrain=inputrain,
inputNoder=inputNoder,
inputNetwork=inputNetwork,
inputinflow=inputinflow,
sort1=sort1)
time_step = c(1:nrow(inputrain))
dataplot <- data.frame(time_step,Qobs,qsim)
kge = hydroGOF::KGE(as.matrix(qsim), Qobs)
title1 = paste0("Node no",nodeID,", KGE = ", round(kge, 2))
colnames(dataplot) <- c("time_step","Qobs","qsim")
fig <- plot_ly(dataplot, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~time_step, y = ~Qobs, name = 'Observed flow')%>%
add_trace(x = ~time_step, y = ~qsim, name = 'Simulated flow')%>%
layout(title = title1,xaxis = list(title = 'Time step (month)'),
yaxis = list(title = 'Outflow (Mm3/month)'))
fig
}
})
calibplot = reactive({
inFile <- input$file_calib
if(length(inFile)>0){
res = calib_res()
if(length(res)>0){
ExcelInOrig <- inFile$datapath
inputNoder= read_excel(ExcelInOrig, sheet='Nodes',skip=1)
inputNetwork= read_excel(ExcelInOrig, sheet='Network',skip=1)
inputrain= read_excel(ExcelInOrig, sheet="Rainfall")
inputcatch= read_excel(ExcelInOrig, sheet='catchments nodes',skip=1)
inputmeasures= read_excel(ExcelInOrig, sheet='Measures',skip=1)
inputinflow= read_excel(ExcelInOrig, sheet='Inflow')
inputmeasured= read_excel(ExcelInOrig, sheet='Measured flow')
###########################################################
nc = nrow(inputcatch) # number of catchments
ns = nrow(inputmeasures) # number of solutions
snames = inputmeasures$Measure # names of solutions
cnames = inputcatch$`catchment names` # names of catchments
node_catch =inputcatch$`catchment outlet node`
cost1 = inputmeasures$Cost # cost per m2 of NBS, DAM, pump
rate1 = inputmeasures$`Mm3/m2/month` # rate of recharge per month per m2
outlet1 = as.numeric(inputcatch$node[1]) # outlet of the whole system
kt = as.data.frame(t(inputcatch[,c(5:(5+ns-1))])) # transportation factor
ke = as.data.frame(t(inputcatch[,c((5+ns):(5+ns+ns-1))])) # energy factor
kl = as.data.frame(t(inputcatch[,c((5+ns+ns):(5+ns+ns+ns-1))])) # local condition factor
limit1 = as.data.frame(t(inputcatch[,c((5+ns+ns+ns):(5+ns+ns+ns+ns-1))])) # limit of NBS for each catchment
inf_cap = as.numeric(inputcatch$`Mm3/m2/month`) # infiltration capacity of each basin
###########################################################
tree_data = as.data.frame(inputNetwork)[c(1,2,3)]
colnames(tree_data) <- c('from', 'to', 'w')
tree1 <- graph_from_data_frame(tree_data)
sort1 = topo_sort(tree1,mode = "out")
nodeID = input$node1
Qobs = as.data.frame(inputmeasured[,(nodeID+1)])
######################
qsim = run_model(param=res,
nodeID=nodeID,
inputrain=inputrain,
inputNoder=inputNoder,
inputNetwork=inputNetwork,
inputinflow=inputinflow,
sort1=sort1)
time_step = c(1:nrow(inputrain))
dataplot <- data.frame(time_step,Qobs,qsim)
kge = hydroGOF::KGE(as.matrix(qsim), Qobs)
title1 = paste0("Node no",nodeID,", KGE = ", round(kge, 2))
colnames(dataplot) <- c("time_step","Qobs","qsim")
fig <- plot_ly(dataplot, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~time_step, y = ~Qobs, name = 'Observed flow')%>%
add_trace(x = ~time_step, y = ~qsim, name = 'Simulated flow')%>%
layout(title = title1,xaxis = list(title = 'Time step (month)'),
yaxis = list(title = 'Outflow (Mm3/month)'))
fig
}
}
})
calib_res <- eventReactive(input$act_calib, {
inFile <- input$file_calib
if(length(inFile)>0){
ExcelInOrig <- inFile$datapath
inputNoder= read_excel(ExcelInOrig, sheet='Nodes',skip=1)
inputNetwork= read_excel(ExcelInOrig, sheet='Network',skip=1)
inputrain= read_excel(ExcelInOrig, sheet="Rainfall")
inputcatch= read_excel(ExcelInOrig, sheet='catchments nodes',skip=1)
inputmeasures= read_excel(ExcelInOrig, sheet='Measures',skip=1)
inputinflow= read_excel(ExcelInOrig, sheet='Inflow')
inputmeasured= read_excel(ExcelInOrig, sheet='Measured flow')
###########################################################
nc = nrow(inputcatch) # number of catchments
ns = nrow(inputmeasures) # number of solutions
snames = inputmeasures$Measure # names of solutions
cnames = inputcatch$`catchment names` # names of catchments
node_catch =inputcatch$`catchment outlet node`
cost1 = inputmeasures$Cost # cost per m2 of NBS, DAM, pump
rate1 = inputmeasures$`Mm3/m2/month` # rate of recharge per month per m2
outlet1 = as.numeric(inputcatch$node[1]) # outlet of the whole system
kt = as.data.frame(t(inputcatch[,c(5:(5+ns-1))])) # transportation factor
ke = as.data.frame(t(inputcatch[,c((5+ns):(5+ns+ns-1))])) # energy factor
kl = as.data.frame(t(inputcatch[,c((5+ns+ns):(5+ns+ns+ns-1))])) # local condition factor
limit1 = as.data.frame(t(inputcatch[,c((5+ns+ns+ns):(5+ns+ns+ns+ns-1))])) # limit of NBS for each catchment
inf_cap = as.numeric(inputcatch$`Mm3/m2/month`) # infiltration capacity of each basin
###########################################################
tree_data = as.data.frame(inputNetwork)[c(1,2,3)]
colnames(tree_data) <- c('from', 'to', 'w')
tree1 <- graph_from_data_frame(tree_data)
sort1 = topo_sort(tree1,mode = "out")
nodeID = input$node1
Qobs = as.data.frame(inputmeasured[,(nodeID+1)])
######################
calibration_res2 <- DEoptim(
fn = obj_fun_calib,
lower = rep(0,nrow(inputNoder)),
upper = rep(1,nrow(inputNoder)),
DEoptim.control(trace=TRUE,parallelType=0,
packages=c(),
parVar=c(),
itermax = input$num_calib),
Qobs = Qobs,
nodeID=nodeID,
inputrain=inputrain,
inputNoder=inputNoder,
inputNetwork=inputNetwork,
inputinflow=inputinflow,
sort1 = sort1
)
calibration_res2$optim$bestmem
}
})
output$plot_current <- renderPlotly({
inFile <- input$file_calib
if(length(inFile)>0){
currresplot()
}
})
output$downloadcalib <- downloadHandler(
filename = function() {
paste("calibParam.csv", sep = "")
},
content = function(file) {
res = calib_res()
write.csv(res,file,sep = ",",col.names = F,row.names = F)
}
)
output$plot_calib <- renderPlotly({
inFile <- input$file_calib
if(length(inFile)>0){
calibplot()
}
})
##############
initial_volume = reactive({
inFile <- input$file1
if(length(inFile)>0){
ExcelInOrig = inFile$datapath
###########################################################
inputNoder= read_excel(ExcelInOrig, sheet='Nodes',skip=1)
inputNetwork= read_excel(ExcelInOrig, sheet='Network',skip=1)
inputrain= read_excel(ExcelInOrig, sheet="Rainfall")
inputcatch= read_excel(ExcelInOrig, sheet='catchments nodes',skip=1)
inputmeasures= read_excel(ExcelInOrig, sheet='Measures',skip=1)
inputinflow= read_excel(ExcelInOrig, sheet='Inflow')
###########################################################
nc = nrow(inputcatch) # number of catchments
ns = nrow(inputmeasures) # number of solutions
snames = inputmeasures$Measure # names of solutions
cnames = inputcatch$`catchment names` # names of catchments
node_catch =inputcatch$`catchment outlet node`
cost1 = inputmeasures$Cost # cost per m2 of NBS, DAM, pump
rate1 = inputmeasures$`Mm3/m2/month` # rate of recharge per month per m2
outlet1 = as.numeric(inputcatch$node[1]) # outlet of the whole system
kt = as.data.frame(t(inputcatch[,c(5:(5+ns-1))])) # transportation factor
ke = as.data.frame(t(inputcatch[,c((5+ns):(5+ns+ns-1))])) # energy factor
kl = as.data.frame(t(inputcatch[,c((5+ns+ns):(5+ns+ns+ns-1))])) # local condition factor
limit1 = as.data.frame(t(inputcatch[,c((5+ns+ns+ns):(5+ns+ns+ns+ns-1))])) # limit of NBS for eavch catchment
inf_cap = as.numeric(inputcatch$`Mm3/m2/month`) # infiltration capacity of each basin
###########################################################
tree_data = as.data.frame(inputNetwork)[c(1,2,3)]
colnames(tree_data) <- c('from', 'to', 'w')
tree1 <- graph_from_data_frame(tree_data)
###########################################################
sort1 = topo_sort(tree1,mode = "out")
np = ns*nc
lb <- rep(0,np)
optim_fun = function(param){
############
# nc = 4 # number of catchments
# ns = 3 # number of solutions
# node_catch = c(2,9,12,15)
# cost1 = c(10,15,30) # cost per m2 of NBS, DAM, pump
# rate1 = c(0.01,0.014,0.04) # rate of recharge per month per m2
# kt = c(2.12,2,1.41,1)
# ke = c(1,1.5,3.5)
# kl = matrix(1,nrow = ns,ncol = nc)
k = 1
Ctotal = 0
Marc = array()
for(c in 1:nc){
Mar1 = 0
for(s in 1:ns){
ma1 = param[k] * min(rate1[s],inf_cap[c])
Ctotal = Ctotal + (param[k] * cost1[s]*kt[s,c]*ke[s,c]*kl[s,c])
Mar1 = Mar1 + ma1
k = k+1
}
Marc[c] = Mar1
}
dat_marc = data.frame(node = node_catch,
MAR = Marc)
############
Rainfall = as.matrix(inputrain[,-1])
T1 = nrow(Rainfall)
N1 = nrow(inputNoder)
rain_mat = matrix(0,ncol =N1,nrow = T1)
for(n in 1:(N1)){
ind_rain = which(colnames(Rainfall)==inputNoder$Rainfall[n])
rain1 = as.numeric(Rainfall[,ind_rain])*(inputNoder$MAP[n]/100000)*
(inputNoder$`Area (km2)`[n])
rain_mat[,n] = as.array(rain1*(1-inputNoder$Retention[n]))
}
######
#Out_mat = matrix(0,ncol = (N1),nrow = T1)
Out_mat = rain_mat
inflow_mat = as.matrix(inputinflow[,-1])
N2 = nrow(inputNetwork)
for(n in sort1){
if(n %in% node_catch){
MR = dat_marc[which(dat_marc$node==n),2]
}else{
MR = 0
}
attr1 = which(inputNetwork$`To node`==n)
k = 1
if(length(attr1)>0){
share2 = inputNetwork$Share[attr1]
node2 = inputNetwork$`From node`[attr1]
# rain1 = as.matrix(rain_mat2[,attr1])
for(j in 1:length(attr1)){
share1 = share2[j]
if(j ==1){
sum1 = share1*Out_mat[,node2[j]]
}else{
sum1 = sum1+(share1*Out_mat[,node2[j]])
}
}
Out_mat[,n] = Out_mat[,n] + ((sum1 + inflow_mat[,n])*(1-inputNoder$Retention[n]))- MR
}
}
################
y = array()
y[1] = sum(Out_mat[,outlet1]) # Total volume at the outlet
y[2] = Ctotal
return(y)
}
optim_fun(lb)[1]
}
})
res = eventReactive(input$act1, {
#############
inFile <- input$file1
if(length(inFile)>0){
ExcelInOrig <- inFile$datapath
###########################################################
inputNoder= read_excel(ExcelInOrig, sheet='Nodes',skip=1)
inputNetwork= read_excel(ExcelInOrig, sheet='Network',skip=1)
inputrain= read_excel(ExcelInOrig, sheet="Rainfall")
inputcatch= read_excel(ExcelInOrig, sheet='catchments nodes',skip=1)
inputmeasures= read_excel(ExcelInOrig, sheet='Measures',skip=1)
inputinflow= read_excel(ExcelInOrig, sheet='Inflow')
###########################################################
nc = nrow(inputcatch) # number of catchments
ns = nrow(inputmeasures) # number of solutions
snames = inputmeasures$Measure # names of solutions
cnames = inputcatch$`catchment names` # names of catchments
node_catch =inputcatch$`catchment outlet node`
cost1 = inputmeasures$Cost # cost per m2 of NBS, DAM, pump
rate1 = inputmeasures$`Mm3/m2/month` # rate of recharge per month per m2
outlet1 = as.numeric(inputcatch$node[1]) # outlet of the whole system
kt = as.data.frame(t(inputcatch[,c(5:(5+ns-1))])) # transportation factor
ke = as.data.frame(t(inputcatch[,c((5+ns):(5+ns+ns-1))])) # energy factor
kl = as.data.frame(t(inputcatch[,c((5+ns+ns):(5+ns+ns+ns-1))])) # local condition factor
limit1 = as.data.frame(t(inputcatch[,c((5+ns+ns+ns):(5+ns+ns+ns+ns-1))])) # limit of NBS for eavch catchment
inf_cap = as.numeric(inputcatch$`Mm3/m2/month`) # infiltration capacity of each basin
###########################################################
tree_data = as.data.frame(inputNetwork)[c(1,2,3)]
colnames(tree_data) <- c('from', 'to', 'w')
tree1 <- graph_from_data_frame(tree_data)
###########################################################
sort1 = topo_sort(tree1,mode = "out")
optim_fun = function(param){
############
# nc = 4 # number of catchments
# ns = 3 # number of solutions
# node_catch = c(2,9,12,15)
# cost1 = c(10,15,30) # cost per m2 of NBS, DAM, pump
# rate1 = c(0.01,0.014,0.04) # rate of recharge per month per m2
# kt = c(2.12,2,1.41,1)
# ke = c(1,1.5,3.5)
# kl = matrix(1,nrow = ns,ncol = nc)
k = 1
Ctotal = 0
Marc = array()
for(c in 1:nc){
Mar1 = 0
for(s in 1:ns){
ma1 = param[k] * min(rate1[s],inf_cap[c])
Ctotal = Ctotal + (param[k] * cost1[s]*kt[s,c]*ke[s,c]*kl[s,c])
Mar1 = Mar1 + ma1
k = k+1
}
Marc[c] = Mar1
}
dat_marc = data.frame(node = node_catch,
MAR = Marc)
############
Rainfall = as.matrix(inputrain[,-1])
T1 = nrow(Rainfall)
N1 = nrow(inputNoder)
rain_mat = matrix(0,ncol =N1,nrow = T1)
for(n in 1:(N1)){
ind_rain = which(colnames(Rainfall)==inputNoder$Rainfall[n])
rain1 = as.numeric(Rainfall[,ind_rain])*(inputNoder$MAP[n]/100000)*
(inputNoder$`Area (km2)`[n])
rain_mat[,n] = as.array(rain1*(1-inputNoder$Retention[n]))
}
######
#Out_mat = matrix(0,ncol = (N1),nrow = T1)
Out_mat = rain_mat
inflow_mat = as.matrix(inputinflow[,-1])
N2 = nrow(inputNetwork)
for(n in sort1){
if(n %in% node_catch){
MR = dat_marc[which(dat_marc$node==n),2]
}else{
MR = 0
}
attr1 = which(inputNetwork$`To node`==n)
k = 1
if(length(attr1)>0){
share2 = inputNetwork$Share[attr1]
node2 = inputNetwork$`From node`[attr1]
# rain1 = as.matrix(rain_mat2[,attr1])
for(j in 1:length(attr1)){
share1 = share2[j]
if(j ==1){
sum1 = share1*Out_mat[,node2[j]]
}else{
sum1 = sum1+(share1*Out_mat[,node2[j]])
}
}
Out_mat[,n] = Out_mat[,n] + ((sum1 + inflow_mat[,n])*(1-inputNoder$Retention[n]))- MR
}
}
################
y = array()
y[1] = sum(Out_mat[,outlet1]) # Total volume at the outlet
y[2] = Ctotal
return(y)
}
constraint1 <- function(param) {
############
# nc = 4 # number of catchments
# ns = 3 # number of solutions
# node_catch = c(2,9,12,15)
# cost1 = c(10,15,30) # cost per m2 of NBS, DAM, pump
# rate1 = c(0.01,0.014,0.04) # rate of recharge per month per m2
# kt = c(2.12,2,1.41,1)
# ke = c(1,1.5,3.5)
# kl = matrix(1,nrow = ns,ncol = nc)
k = 1
Ctotal = 0
Marc = array()
for(c in 1:nc){
Mar1 = 0
for(s in 1:ns){
ma1 = param[k] * min(rate1[s],inf_cap[c])
Ctotal = Ctotal + (param[k] * cost1[s]*kt[c]*ke[s]*kl[s,c])
Mar1 = Mar1 + ma1
k = k+1
}
Marc[c] = Mar1
}
dat_marc = data.frame(node = node_catch,
MAR = Marc)
############
Rainfall = as.matrix(inputrain[,-1])
T1 = nrow(Rainfall)
N1 = nrow(inputNoder)
rain_mat = matrix(0,ncol =(N1),nrow = T1)
for(n in 1:(N1)){
ind_rain = which(colnames(Rainfall)==inputNoder$Rainfall[n])
rain1 = as.numeric(Rainfall[,ind_rain])*(inputNoder$MAP[n]/100000)*
(inputNoder$`Area (km2)`[n])
rain_mat[,n] = as.array(rain1*(1-inputNoder$Retention[n]))
}
######
#Out_mat = matrix(0,ncol = (N1),nrow = T1)
Out_mat = rain_mat
inflow_mat = as.matrix(inputinflow[,-1])
for(n in sort1){
if(n %in% node_catch){
MR = dat_marc[which(dat_marc$node==n),2]
}else{
MR = 0
}
attr1 = which(inputNetwork$`To node`==n)
k = 1
if(length(attr1)>0){
share2 = inputNetwork$Share[attr1]
node2 = inputNetwork$`From node`[attr1]
# rain1 = as.matrix(rain_mat2[,attr1])
for(j in 1:length(attr1)){
share1 = share2[j]
if(j ==1){
sum1 = share1*Out_mat[,node2[j]]
}else{
sum1 = sum1+(share1*Out_mat[,node2[j]])
}
}
Out_mat[,n] = Out_mat[,n] + ((sum1 + inflow_mat[,n])*(1-inputNoder$Retention[n]))- MR
}
}
g_array = apply(Out_mat[,node_catch],2,sum)
return(as.array(g_array))
}
np = ns*nc
lb <- rep(0,np)
ub <-as.numeric(as.vector(as.matrix((limit1))))
ncon <- length(node_catch)
nsga2(optim_fun, np, 2,
generations=input$num1,
lower.bounds=lb,
upper.bounds=ub,
constraints=constraint1,
cdim=ncon)
}
})
output$MultiOpt <- renderText({
inFile <- input$file1
if(length(inFile)>0){
res = res()
if(length(res)>0){
print("Optimization is finished. Please export the results")
}
}
})
output$downloadData <- downloadHandler(
filename = function() {
paste("MultiOpt.json", sep = "")
},
content = function(file) {
res = res()
inFile <- input$file1
ExcelInOrig <- inFile$datapath
###########################################################
inputsetup= read_excel(ExcelInOrig, sheet='Setup',skip=2)
inputNoder= read_excel(ExcelInOrig, sheet='Nodes',skip=1)
inputNetwork= read_excel(ExcelInOrig, sheet='Network',skip=1)
inputrain= read_excel(ExcelInOrig, sheet="Rainfall")
inputcatch= read_excel(ExcelInOrig, sheet='catchments nodes',skip=1)
inputmeasures= read_excel(ExcelInOrig, sheet='Measures',skip=1)
inputinflow= read_excel(ExcelInOrig, sheet='Inflow')
###########################################################
curr = as.character(inputsetup[which(inputsetup[,1]=="Define currency"),2])
Rainfall = as.matrix(inputrain[,-1])
ny = nrow(Rainfall)/12
res[[2]][,1] = res[[2]][,1]/ny
###########################################################
nc = nrow(inputcatch) # number of catchments
ns = nrow(inputmeasures) # number of solutions
snames = inputmeasures$Measure # names of solutions
cnames = inputcatch$`catchment names` # names of catchments
node_catch =inputcatch$`catchment outlet node`
cost1 = inputmeasures$Cost # cost per m2 of NBS, DAM, pump
rate1 = inputmeasures$`Mm3/m2/month` # rate of recharge per month per m2
outlet1 = as.numeric(inputcatch$node[1]) # outlet of the whole system
kt = as.data.frame(t(inputcatch[,c(5:(5+ns-1))])) # transportation factor
ke = as.data.frame(t(inputcatch[,c((5+ns):(5+ns+ns-1))])) # energy factor
kl = as.data.frame(t(inputcatch[,c((5+ns+ns):(5+ns+ns+ns-1))])) # local condition factor
limit1 = as.data.frame(t(inputcatch[,c((5+ns+ns+ns):(5+ns+ns+ns+ns-1))])) # limit of NBS for eavch catchment
inf_cap = as.numeric(inputcatch$`Mm3/m2/month`) # infiltration capacity of each basin
###########################################################
tree_data = as.data.frame(inputNetwork)[c(1,2,3)]
colnames(tree_data) <- c('from', 'to', 'w')
tree1 <- graph_from_data_frame(tree_data)
###########################################################
np = ns*nc
lb <- rep(0,np)
initial_volume = initial_volume()/ny
MAR_volume = initial_volume-res[[2]][,1]
list1 = list(snames = snames,
cnames = cnames,
currency = curr,
parameters = res[[1]],
objectives = res[[2]],
MAR = MAR_volume,
pareto = res[[3]])
list.save(list1, file)
}
)
#############
output$pareto_front <- renderPlotly({
inFile <- input$file2
res3 <- list.load(inFile$datapath)
if(length(res3)>0){
#ind1 = which(res3$pareto=="FALSE")[1] -1
x = c(1:length(res3$pareto))
ind_order = order(res3$objectives[,1])
res3$objectives = res3$objectives[ind_order,]
pareto1 = cbind.data.frame(x,res3$objectives,res3$MAR)
colnames(pareto1) = c("Index","Total outflow (Mm3/year)","Cost","MAR volume (Mm3/year)")
plot_ly(data = pareto1, x = ~`Cost`, y = ~`Total outflow (Mm3/year)`, text = ~Index,
marker = list(size = 10,color = 'rgba(255, 182, 193, .9)',
line = list(color = 'rgba(152, 0, 0, .8)',
width = 2)))
}
})
output$Rainfall_Runoff_pareto <- renderPlotly({
inFile <- input$file2
res3 <- list.load(inFile$datapath)
if(length(res3)>0){
#ind1 = which(res3$pareto=="FALSE")[1] -1
cnames = res3$cnames
snames = res3$snames
nc = length(cnames)
ns = length(snames)
dat = data.frame()
ind_order = order(res3$objectives[,1])
res3$objectives = res3$objectives[ind_order,]
res3$parameters = res3$parameters[ind_order,]
ind1 = input$num6
areas = res3$parameters[ind1,]
dat = data.frame()
k = 1
for(c in 1:nc){
for(s in 1:ns){
dat[k,1] = cnames[c]
dat[k,2] = snames[s]
dat[k,3] =areas[k]
k = k+1
}
}
# Grouped
# Grouped
np = ns*nc
lb <- rep(0,np)
initial_volume = initial_volume()
mar_vol = round(initial_volume - res3$objectives[ind1,1],2)
mar_cost = round(res3$objectives[ind1,2],2)
title1 = paste0("Total MAR volume = ",mar_vol, " Mm3. Total cost = ",mar_cost," USD")
colnames(dat) = c("Catchment","MAR method","Area")
p = ggplot(dat, aes(fill=`MAR method`, y=Area, x=Catchment)) +
geom_bar(position="dodge", stat="identity")+
scale_fill_viridis_d() +
ggtitle(title1)+
xlab("Catchment")+
ylab("Area (m2)")+
theme_bw()
ggplotly(p)
}
})
############
shinyInput <- function(FUN, len, id, ...) {
inputs <- numeric(len)
for (i in seq_len(len)) {
inputs[i] <- as.character(FUN(paste0(id, i), label = NULL, ...))
}
inputs
}
# 1.4.2 obtain the values of inputs
shinyValue <- function(id, len) {
unlist(lapply(seq_len(len), function(i) {
value <- input[[paste0(id, i)]]
if (is.null(value)) NA else value
}))
}
compare_df <- reactive({
inFile <- input$file2
res3 <- list.load(inFile$datapath)
if(length(res3)>0){
data.frame(
Solutions =paste0("Solution ",c(1:5)),
Number = shinyInput(selectInput,5,
'select_pareto', choices = c(1:100), width = "100px"),
stringsAsFactors = FALSE
)
}
})
# 1.4.4 final output tags table
output$compare_pareto_input <- DT::renderDataTable(
compare_df(), rownames = FALSE, escape = FALSE, options = list(
autoWidth = TRUE, scrollX = TRUE, #scrollY = '400px',
dom = 't', ordering = FALSE,
preDrawCallback = JS('function() { Shiny.unbindAll(this.api().table().node()); }'),
drawCallback = JS('function() { Shiny.bindAll(this.api().table().node()); } '))
)
output$pareto_plot1 <- renderPlotly({
inFile <- input$file2
res3 <- list.load(inFile$datapath)
if(length(res3)>0){
dat2 <- data.frame(
Solutions = paste0("Solution",c(1:5)),
Number = shinyValue('select_pareto', 5)
)
j = as.numeric(dat2$Number[1])
cnames = res3$cnames
snames = res3$snames
nc = length(cnames)
ns = length(snames)
dat = data.frame()
ind_order = order(res3$objectives[,1])
res3$objectives = res3$objectives[ind_order,]
res3$parameters = res3$parameters[ind_order,]
ind1 = j
areas = res3$parameters[ind1,]
dat = data.frame()
k = 1
for(c in 1:nc){
for(s in 1:ns){
dat[k,1] = cnames[c]
dat[k,2] = snames[s]
dat[k,3] =areas[k]
k = k+1
}
}
# Grouped
# Grouped
np = ns*nc
lb <- rep(0,np)
initial_volume = initial_volume()
mar_vol = round(initial_volume - res3$objectives[ind1,1],2)
mar_cost = round(res3$objectives[ind1,2],2)
title1 = paste0("Total MAR volume = ",mar_vol, " Mm3. Total cost = ",mar_cost," USD")
colnames(dat) = c("Catchment","MAR method","Area")
p = ggplot(dat, aes(fill=`MAR method`, y=Area, x=Catchment)) +
geom_bar(position="dodge", stat="identity")+
scale_fill_viridis_d() +
ggtitle(title1)+
xlab("Catchment")+