R/paramModels.R
In ParisaBagheriTookanlou/FieldReadiness: A Markov decision process (MDP) used for scheduling tillage operations
Defines functions
setParam
Documented in
setParam
#' Set the parameters used when build the MDP model
#'
#' @param opNum Number of operations for scheduling
#' @param tMax Maximum length of growing cycle
#' @param StartDay The first day of growing cycle
#' @param LastDay The last day of growing cycle
#' @param opSeq A vector showing operations indexes and sequence
#' @param opE A vector showing earliest start time of operations
#' @param opL A vector showing latest start time of operationss
#' @param opD A vector showing the time needed to complete operation (obtained by machine capacity and the field area)
#' @param opDelay A vector including the delay times after finishing the operations (except the last operation)
#' @param opFixCost A vector including the fixed costs of tillage operations
#' @param watTh A vector including threshold values used as the optimal level of soil-water content for performing tillage operations
#' @param coefLoss Coeficiant showing yield reduction when soil-water content is not appropriate
#' @param priceYield Price per kg yield (DDK)
#' @param machCap A vector showing the machine capacity for tillage operations
#' @param yieldHa Estimated yield per hetar in field (kg)
#' @param fieldArea Area of field (ha)
#' @param coefTimeliness Timeliness Coeficiant showing yield reduction resulting from postponing a tillage operation.
#' @param costSkip Cost of skipping tillage operations for the current cropping period
#' @param watUpper Upper limits of water content for workability criterion calculated based on the method in \url{http://www.sciencedirect.com/science/article/pii/S0167198700001549}
#' @param watLower Lower limits of water content for workability criterion calculated based on the method in \url{http://www.sciencedirect.com/science/article/pii/S0167198700001549}
#' @param weightCompletion Weight of the completion criterion used in the reward function of the MDP.
#' @param weightWorkable Weight of the workability criterion used in the reward function of the MDP.
#' @param weightTraffic Weight of the trafficability criterion used in the reward function of the MDP.
#' @param minOpt The lower tail of the interval related to the best time period for finishing tillage operations.
#' @param maxOpt The upper tail of the interval related to the best time period for finishing tillage operations.
#' @param centerPointsAvgWat Center points for discritization of estimated mean of soil-water content using historical weather data.
#' @param centerPointsAvgMat Center points for discritization of estimated mean of matric water potential using historical weather data.
#' @param centerPointsSdWat Center points for discritization of estimated standard deviation of soil-water content
#' @param centerPointsSdPos Center points for discritization of estimated standard deviation of posterior distribution in SSM
#' @param centerPointsMeanPos Center points for discritization of estimated mean of posterior distribution in SSM
#' @param centerPointsTem Center points for discritization of weather forecast data regarding air temperature
#' @param centerPointsPre Center points for discritization of weather forecast data regarding precipitation
#' @param centerPointsGlobRd Center points for discritization of weather forecast data regarding global radiation
#' @param centerPointsWind Center points for discritization of weather forecast data regarding wind speed
#' @param temMeanDry Mean of air temprature in dry days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param temMeanWet Mean of air temprature in wet days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param temVarDry Variance of air temprature in dry days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param temVarWet Variance of air temprature in wet days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param GRdMeanDry Mean of global radiation in dry days
#' @param GRdMeanWet Mean of global radiation in wet days
#' @param GRdVarDry Variance of global radiation in dry days
#' @param GRdVarWet Variance of global radiation in wet days
#' @param WindMeanDry Mean of evatranspiration in dry days
#' @param WindMeanWet Mean of evatranspiration in wet days
#' @param WindVarDry Variance of evatranspiration in dry days
#' @param WindVarWet Variance of evatranspiration in wet days
#' @param observedSoilMois observed soil moisture
#' @param dryDayTh Threshold of precipitation amount for being a dry day (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param precShape Shape parameter of gamma distribution for precipitation amount (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param precScale Scale parameter of gamma distribution for precipitation amount (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param WindShape_Wet Shape parameter of Weibull distribution of Wind speed amount for wet day
#' @param WindScale_Wet Scale parameter of Weibull distribution of Wind speed amount for wet day
#' @param WindShape_Dry Shape parameter of Weibull distribution of Wind speed amount for dry day
#' @param WindScale_Dry Scale parameter of Weibull distribution of Wind speed amount for dry day
#' @param prDryWet Probability of a wet day when the previous day is dry (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param prWetWet Probability of a wet day when the previous day is wet (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param hydroWatR Residual soil moisture value (\url{http://onlinelibrary.wiley.com/doi/10.1002/hyp.6629/abstract}).
#' @param hydroWatS Soil moisture value at saturation (\url{http://onlinelibrary.wiley.com/doi/10.1002/hyp.6629/abstract}).
#' @param hydroVanAlpha Parameter used in van Genuchten equation to calculate the matric water potensial (\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param hydroVanM Parameter used in van Genuchten equation to calculate the matric water potensial (\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param hydroVanN Parameter used in van Genuchten equation to calculate the matric water potensial (\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param hydroBulkDensity Parameter used to change the unit of soil-water content from kg/kg to cm^3/cm^3(\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param gSSMW System variance of Gaussian SSM.
#' @param gSSMV Observation variance of Gaussian SSM.
#' @param gSSMm0 Initial posterior mean of Gaussian SSM.
#' @param gSSMc0 Initial posterior variance of Gaussian SSM.
#' @param nGSSMm0 Initial posterior mean of non-Gaussian SSM.
#' @param nGSSMc0 Initial posterior mean of non-Gaussian SSM.
#' @param nGSSMK Number of observations in non-Gaussian SSM.
#' @param rewRisk A boolean variable specifing to calculate the reward based on cost parameters or the satisfaction level for trafficability, workability and completion criteria.
#' @param check Check model e.g. do trans pr sum to one.
#'
#' @export
#' @return A list containing all the parameters used in three-level MDP
setParam <- function(
opNum=2,
tMax=14,
StartDay=13,
LastDay=30,
opSeq=c(1,2),
opD=c(6,4),
opE=c(1,1+opD[1]),
opL=c(tMax-opD[2],tMax),
opDelay=c(0,0),
opFixCost=c(1000,1000),
watTh=c(50,50),
coefLoss=0.3,
priceYield=10,
machCap=50,
yieldHa=100,
fieldArea=100,
coefTimeliness=0.01,
costSkip=80000,
watUpper=c(0.3390464,0.3390464)*100, # %
watLower=c(0.2084356,0.2084356)*100, # %
weightCompletion=1,
weightWorkable=1,
weightTraffic=1,
minOpt=18,
maxOpt=23,
# 2016
centerPointsAvgWat=c(22.26053, 24.52712, 26.79373, 29.06032, 31.32693, 33.59352, 35.86013),
centerPointsAvgMat=c(29.89776, 45.31459, 60.73142, 76.14826, 91.56509, 106.98192, 122.39876),
centerPointsSdWat=seq(1,1, by=1),
centerPointsSdPos=c(0.005,0.1),
centerPointsMeanPos=seq(0.8,1.2, by=0.12),
centerPointsTem=c(8.717964, 10.676148, 12.634332, 14.592516),
centerPointsPre= c(0.0000, 0.2500, 3.0375, 9.1125, 15.1875, 21.2625),
centerPointsGlobRd=c(39.73461, 75.43570, 111.13678, 146.83787),
centerPointsWind=c(0.9471526, 2.0897995, 3.2324465, 4.3750934),
temMeanDry=11.96235, #It is estimated based on histrocial data.
temMeanWet=11.42029, #It is estimated based on histrocial data.
temVarDry= 7.780112, #It is estimated based on histrocial data.
temVarWet=3.441829, #It is estimated based on histrocial data.
GRdMeanDry=119.9671, #It is estimated based on histrocial data.
GRdMeanWet=90.46169, #It is estimated based on histrocial data.
GRdVarDry=1640.874, #It is estimated based on histrocial data.
GRdVarWet=1468.409, #It is estimated based on histrocial data.
WindMeanDry=1.672297, #It is estimated based on histrocial data.
WindMeanWet=2.643447, #It is estimated based on histrocial data.
WindVarDry=0.9678253, #It is estimated based on histrocial data.
WindVarWet=0.9314723, #It is estimated based on histrocial data.
dryDayTh=0.25, #It is estimated based on histrocial data.
precShape=0.4183805, #It is estimated based on histrocial data.
precScale=7.366368, #It is estimated based on histrocial data.
prDryWet=0.4494048, #It is estimated based on histrocial data.
prWetWet=0.7750947, #It is estimated based on histrocial data.
WindShape_Wet=3.059134, #It is estimated based on histrocial data.
WindScale_Wet=2.994871, #It is estimated based on histrocial data.
WindShape_Dry=2.084098, #It is estimated based on histrocial data.
WindScale_Dry=1.989019, #It is estimated based on histrocial data.
# 2020
# centerPointsAvgWat=c(15.51984, 18.82241, 22.12498, 25.42755, 28.73012, 32.03269, 35.33526),
# centerPointsAvgMat=c(78.73775, 191.70698, 304.67621, 417.64544, 530.61467, 643.58390, 756.55313),
# centerPointsSdWat=seq(1,1, by=1),
# centerPointsSdPos=c(0.005,0.1),
# centerPointsMeanPos=seq(0.8,1.2, by=0.12),
# centerPointsTem=c(9.34062, 12.54412, 15.74761, 18.95111),
# centerPointsPre=c(0.0000, 0.2500, 3.0375, 9.1125, 15.1875, 21.2625),
# centerPointsGlobRd=c(38.25177, 79.57014, 120.88850, 162.20686),
# centerPointsWind=c(1.063851, 2.439893, 3.815936, 5.191979),
# temMeanDry=12.37325, #It is estimated based on histrocial data.
# temMeanWet=10.71889, #It is estimated based on histrocial data.
# temVarDry=6.873857, #It is estimated based on histrocial data.
# temVarWet=3.381453, #It is estimated based on histrocial data.
# GRdMeanDry=119.6411, #It is estimated based on histrocial data.
# GRdMeanWet=79.07292, #It is estimated based on histrocial data.
# GRdVarDry=1600.626, #It is estimated based on histrocial data.
# GRdVarWet=1059.53, #It is estimated based on histrocial data.
# WindMeanDry=2.117543, #It is estimated based on histrocial data.
# WindMeanWet=2.53611, #It is estimated based on histrocial data.
# WindVarDry=1.020088, #It is estimated based on histrocial data.
# WindVarWet=0.7630564, #It is estimated based on histrocial data.
# dryDayTh=0.25,
# precShape=0.2982603, #It is estimated based on histrocial data.
# precScale=6.9151, #It is estimated based on histrocial data.
# prDryWet=0.2949405, #It is estimated based on histrocial data.
# prWetWet=0.5920928, #It is estimated based on histrocial data.
# WindShape_Wet=3.055488, #It is estimated based on histrocial data.
# WindScale_Wet=3.158224, #It is estimated based on histrocial data.
# WindShape_Dry=1.983153, #It is estimated based on histrocial data.
# WindScale_Dry=2.715127, #It is estimated based on histrocial data.
# Sandy loam
hydroWatR=0.041, # cm3/cm3
hydroWatS= 0.412, # cm3/cm3
hydroVanAlpha=0.068,
hydroVanM=0.2436,
hydroVanN=1.322,
hydroBulkDensity=1.53, # (g/cm3)- The value of the bulk density that is related to A horizon
# 2016
gSSMW=0.1528,
# 2020
# gSSMW=0.1704,
# 2016
gSSMV=12.81,
# 2020
# gSSMV=41.66,
gSSMm0=1,
gSSMc0=0.001,
nGSSMm0=4,
nGSSMc0=2,
nGSSMK=20,
rewRisk=TRUE,
check=FALSE
){
model <- list(opNum=opNum)
model$opSeq <- opSeq
model$tMax <- tMax
model$StartDay <- StartDay
model$LastDay <- LastDay
model$opE <- opE
model$opL <- opL
model$opD <- opD
model$opDelay <- opDelay
model$opFixCost <- opFixCost
model$watTh <- watTh
model$coefLoss <- coefLoss
model$priceYield <- priceYield
model$machCap <- machCap
model$yieldHa <- yieldHa
model$fieldArea <- fieldArea
model$coefTimeliness <- coefTimeliness
model$costSkip <- costSkip
model$watUpper <- watUpper
model$watLower <- watLower
model$weightCompletion <- weightCompletion
model$weightWorkable <- weightWorkable
model$weightTraffic <- weightTraffic
model$minOpt <- minOpt
model$maxOpt <- maxOpt
model$temMeanDry <- temMeanDry
model$temMeanWet <- temMeanWet
model$temVarDry <- temVarDry
model$temVarWet <- temVarWet
model$GRdMeanDry <- GRdMeanDry
model$GRdMeanWet <- GRdMeanWet
model$WindMeanDry <- WindMeanDry
model$WindMeanWet <- WindMeanWet
model$GRdVarDry <- GRdVarDry
model$GRdVarWet <- GRdVarWet
model$WindVarDry <- WindVarDry
model$WindVarWet <- WindVarWet
model$dryDayTh <- dryDayTh
model$precShape <- precShape
model$precScale <- precScale
model$prDryWet <- prDryWet
model$prWetWet <- prWetWet
model$WindShape_Wet <- WindShape_Wet
model$WindScale_Wet <- WindScale_Wet
model$WindShape_Dry <- WindShape_Dry
model$WindScale_Dry <- WindScale_Dry
model$hydroWatR <- hydroWatR
model$hydroWatS <- hydroWatS
model$hydroVanAlpha <- hydroVanAlpha
model$hydroVanM <- hydroVanM
model$hydroVanN <- hydroVanN
model$hydroBulkDensity <- hydroBulkDensity
model$gSSMW <- gSSMW
model$gSSMV <- gSSMV
model$gSSMm0 <- gSSMm0
model$gSSMc0 <- gSSMc0
model$nGSSMm0 <- nGSSMm0
model$nGSSMc0 <- nGSSMc0
model$nGSSMK <- nGSSMK
model$rewRisk <- rewRisk
model$check <- check
model$centerPointsAvgWat <- centerPointsAvgWat
model$centerPointsAvgMat <- centerPointsAvgMat
model$centerPointsSdWat <- centerPointsSdWat
model$centerPointsSdPos <- centerPointsSdPos
model$centerPointsMeanPos <- centerPointsMeanPos
model$centerPointsTem <- centerPointsTem
model$centerPointsPre <- centerPointsPre
model$centerPointsGlobRd <- centerPointsGlobRd
model$centerPointsWind <- centerPointsWind
#Discritization of continious states:
obj <- discretizeGaussian::Discretize()
disAvgWat <- matrix()
disAvgMat <- matrix()
disSdWat <- matrix()
disSdPos <- matrix()
disMeanPos <- matrix()
disTem <- matrix()
disPre <- matrix()
disGR <- matrix()
disWind <- matrix()
disAvgWat <- as.matrix(obj$discretize1DVec(centerPointsAvgWat, mInf=-1, inf=100, asDF=F), ncol=3)
disAvgMat <- as.matrix(obj$discretize1DVec(centerPointsAvgMat, mInf=-1, inf=100, asDF=F), ncol=3)
disSdWat <- as.matrix(obj$discretize1DVec(centerPointsSdWat, inf=100, mInf=0.01, asDF=F), ncol=3)
disSdPos <- as.matrix(obj$discretize1DVec(centerPointsSdPos, inf=100, mInf=0, asDF=F), ncol=3)
disMeanPos <- as.matrix(obj$discretize1DVec(centerPointsMeanPos, inf=100, asDF=F), ncol=3)
disTem <- as.matrix(obj$discretize1DVec(centerPointsTem, inf=100, asDF=F), ncol=3)
disPre <- as.matrix(obj$discretize1DVec(centerPointsPre, inf=50, mInf=0, asDF=F), ncol=3)
disGR <- as.matrix(obj$discretize1DVec(centerPointsGlobRd, inf=250, mInf=0, asDF=F), ncol=3)
disWind <- as.matrix(obj$discretize1DVec(centerPointsWind, inf=10, mInf=0, asDF=F), ncol=3)
model$disAvgWat <- disAvgWat
model$disAvgMat <- disAvgMat
model$disSdWat <- disSdWat
model$disSdPos <- disSdPos
model$disMeanPos <- disMeanPos
model$disTem <- disTem
model$disPre <- disPre
model$disGR <- disGR
model$disWind <- disWind
#Soil water contents based on weather info. from centerpoints:
Combn_centrpoints <- as.matrix(expand.grid(disTem[,1], disPre[,1], disGR[,1], disWind[,1]))
combinations <- rbind(rbind(rbind(0,Combn_centrpoints),0),0)
days <- format(as.Date(seq(from=as.Date("2015-09-06"), to=as.Date("2016-09-26"), by='days')), "%m/%d/%Y") # The number of days is based on the number of combinations of centerpoints
days <- gsub('(?<=\\b|-)0', '', days, perl=TRUE)
wed_data <- as.data.frame(matrix(nrow = length(days) , ncol = 5))
colnames(wed_data) <- c("datetime", "Temperature", "Precipitation", "GlobalRadiation", "WindSpeed")
for (i in 1:length(days)){
date <- as.Date(days[i],'%m/%d/%Y')
wed_data$datetime[i] <- as.numeric(date)
if (i==1){
wed_data$Temperature[i] <- 0
wed_data$Precipitation[i] <- 0
wed_data$GlobalRadiation[i] <- 0
wed_data$WindSpeed[i] <- 0
} else{
wed_data$Temperature[i] <- round(as.matrix(combinations[i,1]), digits = 6)
wed_data$Precipitation[i] <- round(as.matrix(combinations[i,2]), digits = 4)
wed_data$GlobalRadiation[i] <- round(as.matrix(combinations[i,3]), digits = 1)
wed_data$WindSpeed[i] <- round(as.matrix(combinations[i,4]), digits = 4)
}
}
Mois_WedcentrePoints <- matrix()
DWF_File(wed_data)
DaisyOutputs <- Daisy(wed_data)
Mean_DaisyOutputs <- Mean_Daisy(DaisyOutputs)
Mean_DaisyOutputs$Mean_SoilWaterContent[2] <- Mean_DaisyOutputs$Mean_SoilWaterContent[2]*100
wed_data <- wed_data[c(-1, -(dim(wed_data)[1]-1), -dim(wed_data)[1]),]
temp_wed_data <- as.data.frame(matrix(nrow = dim(wed_data)[1] , ncol = 5))
colnames(temp_wed_data) <- c("datetime", "Temperature", "Precipitation", "GlobalRadiation", "WindSpeed")
for (i in 1:dim(wed_data)[1]){
temp_wed_data[i,]<- wed_data[i,]
}
wed_data <- temp_wed_data
dim <- dim(Mean_DaisyOutputs$Mean_SoilWaterContent)[1]
Mois_WedcentrePoints <- as.data.frame(matrix(nrow = dim , ncol = 2))
colnames(Mois_WedcentrePoints) <- c("datetime", "Moisture")
for (j in 1:dim(Mois_WedcentrePoints)[1]){
date <- as.Date(Mean_DaisyOutputs$Mean_SoilWaterContent[j,1])
Mois_WedcentrePoints[j,1] <- as.numeric(date)
Mois_WedcentrePoints[j,2] <- as.numeric(Mean_DaisyOutputs$Mean_SoilWaterContent[j,2])
}
wed_data <- as.matrix(wed_data)
Mois_WedcentrePoints <- as.matrix(Mois_WedcentrePoints)
model$wed_data <- wed_data
model$Mois_WedcentrePoints <- Mois_WedcentrePoints
SCIout <- matrix(nrow = dim(disAvgMat)[1], ncol=1)
for (i in 1:dim(disAvgMat)[1]){
SCIout[i,1] <- Terranimo_SCI(disAvgMat[i,1])[5,1]
}
model$SCIout <- SCIout
return(model)
}
ParisaBagheriTookanlou/FieldReadiness documentation built on Dec. 18, 2021, 6:40 a.m.
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Defines functions setParam
Documented in setParam
#' Set the parameters used when build the MDP model
#'
#' @param opNum Number of operations for scheduling
#' @param tMax Maximum length of growing cycle
#' @param StartDay The first day of growing cycle
#' @param LastDay The last day of growing cycle
#' @param opSeq A vector showing operations indexes and sequence
#' @param opE A vector showing earliest start time of operations
#' @param opL A vector showing latest start time of operationss
#' @param opD A vector showing the time needed to complete operation (obtained by machine capacity and the field area)
#' @param opDelay A vector including the delay times after finishing the operations (except the last operation)
#' @param opFixCost A vector including the fixed costs of tillage operations
#' @param watTh A vector including threshold values used as the optimal level of soil-water content for performing tillage operations
#' @param coefLoss Coeficiant showing yield reduction when soil-water content is not appropriate
#' @param priceYield Price per kg yield (DDK)
#' @param machCap A vector showing the machine capacity for tillage operations
#' @param yieldHa Estimated yield per hetar in field (kg)
#' @param fieldArea Area of field (ha)
#' @param coefTimeliness Timeliness Coeficiant showing yield reduction resulting from postponing a tillage operation.
#' @param costSkip Cost of skipping tillage operations for the current cropping period
#' @param watUpper Upper limits of water content for workability criterion calculated based on the method in \url{http://www.sciencedirect.com/science/article/pii/S0167198700001549}
#' @param watLower Lower limits of water content for workability criterion calculated based on the method in \url{http://www.sciencedirect.com/science/article/pii/S0167198700001549}
#' @param weightCompletion Weight of the completion criterion used in the reward function of the MDP.
#' @param weightWorkable Weight of the workability criterion used in the reward function of the MDP.
#' @param weightTraffic Weight of the trafficability criterion used in the reward function of the MDP.
#' @param minOpt The lower tail of the interval related to the best time period for finishing tillage operations.
#' @param maxOpt The upper tail of the interval related to the best time period for finishing tillage operations.
#' @param centerPointsAvgWat Center points for discritization of estimated mean of soil-water content using historical weather data.
#' @param centerPointsAvgMat Center points for discritization of estimated mean of matric water potential using historical weather data.
#' @param centerPointsSdWat Center points for discritization of estimated standard deviation of soil-water content
#' @param centerPointsSdPos Center points for discritization of estimated standard deviation of posterior distribution in SSM
#' @param centerPointsMeanPos Center points for discritization of estimated mean of posterior distribution in SSM
#' @param centerPointsTem Center points for discritization of weather forecast data regarding air temperature
#' @param centerPointsPre Center points for discritization of weather forecast data regarding precipitation
#' @param centerPointsGlobRd Center points for discritization of weather forecast data regarding global radiation
#' @param centerPointsWind Center points for discritization of weather forecast data regarding wind speed
#' @param temMeanDry Mean of air temprature in dry days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param temMeanWet Mean of air temprature in wet days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param temVarDry Variance of air temprature in dry days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param temVarWet Variance of air temprature in wet days (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param GRdMeanDry Mean of global radiation in dry days
#' @param GRdMeanWet Mean of global radiation in wet days
#' @param GRdVarDry Variance of global radiation in dry days
#' @param GRdVarWet Variance of global radiation in wet days
#' @param WindMeanDry Mean of evatranspiration in dry days
#' @param WindMeanWet Mean of evatranspiration in wet days
#' @param WindVarDry Variance of evatranspiration in dry days
#' @param WindVarWet Variance of evatranspiration in wet days
#' @param observedSoilMois observed soil moisture
#' @param dryDayTh Threshold of precipitation amount for being a dry day (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param precShape Shape parameter of gamma distribution for precipitation amount (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param precScale Scale parameter of gamma distribution for precipitation amount (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param WindShape_Wet Shape parameter of Weibull distribution of Wind speed amount for wet day
#' @param WindScale_Wet Scale parameter of Weibull distribution of Wind speed amount for wet day
#' @param WindShape_Dry Shape parameter of Weibull distribution of Wind speed amount for dry day
#' @param WindScale_Dry Scale parameter of Weibull distribution of Wind speed amount for dry day
#' @param prDryWet Probability of a wet day when the previous day is dry (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param prWetWet Probability of a wet day when the previous day is wet (\url{http://link.springer.com/article/10.1007/BF00142466}).
#' @param hydroWatR Residual soil moisture value (\url{http://onlinelibrary.wiley.com/doi/10.1002/hyp.6629/abstract}).
#' @param hydroWatS Soil moisture value at saturation (\url{http://onlinelibrary.wiley.com/doi/10.1002/hyp.6629/abstract}).
#' @param hydroVanAlpha Parameter used in van Genuchten equation to calculate the matric water potensial (\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param hydroVanM Parameter used in van Genuchten equation to calculate the matric water potensial (\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param hydroVanN Parameter used in van Genuchten equation to calculate the matric water potensial (\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param hydroBulkDensity Parameter used to change the unit of soil-water content from kg/kg to cm^3/cm^3(\url{http://www.sciencedirect.com/science/article/pii/S0016706198001323})
#' @param gSSMW System variance of Gaussian SSM.
#' @param gSSMV Observation variance of Gaussian SSM.
#' @param gSSMm0 Initial posterior mean of Gaussian SSM.
#' @param gSSMc0 Initial posterior variance of Gaussian SSM.
#' @param nGSSMm0 Initial posterior mean of non-Gaussian SSM.
#' @param nGSSMc0 Initial posterior mean of non-Gaussian SSM.
#' @param nGSSMK Number of observations in non-Gaussian SSM.
#' @param rewRisk A boolean variable specifing to calculate the reward based on cost parameters or the satisfaction level for trafficability, workability and completion criteria.
#' @param check Check model e.g. do trans pr sum to one.
#'
#' @export
#' @return A list containing all the parameters used in three-level MDP
setParam <- function(
opNum=2,
tMax=14,
StartDay=13,
LastDay=30,
opSeq=c(1,2),
opD=c(6,4),
opE=c(1,1+opD[1]),
opL=c(tMax-opD[2],tMax),
opDelay=c(0,0),
opFixCost=c(1000,1000),
watTh=c(50,50),
coefLoss=0.3,
priceYield=10,
machCap=50,
yieldHa=100,
fieldArea=100,
coefTimeliness=0.01,
costSkip=80000,
watUpper=c(0.3390464,0.3390464)*100, # %
watLower=c(0.2084356,0.2084356)*100, # %
weightCompletion=1,
weightWorkable=1,
weightTraffic=1,
minOpt=18,
maxOpt=23,
# 2016
centerPointsAvgWat=c(22.26053, 24.52712, 26.79373, 29.06032, 31.32693, 33.59352, 35.86013),
centerPointsAvgMat=c(29.89776, 45.31459, 60.73142, 76.14826, 91.56509, 106.98192, 122.39876),
centerPointsSdWat=seq(1,1, by=1),
centerPointsSdPos=c(0.005,0.1),
centerPointsMeanPos=seq(0.8,1.2, by=0.12),
centerPointsTem=c(8.717964, 10.676148, 12.634332, 14.592516),
centerPointsPre= c(0.0000, 0.2500, 3.0375, 9.1125, 15.1875, 21.2625),
centerPointsGlobRd=c(39.73461, 75.43570, 111.13678, 146.83787),
centerPointsWind=c(0.9471526, 2.0897995, 3.2324465, 4.3750934),
temMeanDry=11.96235, #It is estimated based on histrocial data.
temMeanWet=11.42029, #It is estimated based on histrocial data.
temVarDry= 7.780112, #It is estimated based on histrocial data.
temVarWet=3.441829, #It is estimated based on histrocial data.
GRdMeanDry=119.9671, #It is estimated based on histrocial data.
GRdMeanWet=90.46169, #It is estimated based on histrocial data.
GRdVarDry=1640.874, #It is estimated based on histrocial data.
GRdVarWet=1468.409, #It is estimated based on histrocial data.
WindMeanDry=1.672297, #It is estimated based on histrocial data.
WindMeanWet=2.643447, #It is estimated based on histrocial data.
WindVarDry=0.9678253, #It is estimated based on histrocial data.
WindVarWet=0.9314723, #It is estimated based on histrocial data.
dryDayTh=0.25, #It is estimated based on histrocial data.
precShape=0.4183805, #It is estimated based on histrocial data.
precScale=7.366368, #It is estimated based on histrocial data.
prDryWet=0.4494048, #It is estimated based on histrocial data.
prWetWet=0.7750947, #It is estimated based on histrocial data.
WindShape_Wet=3.059134, #It is estimated based on histrocial data.
WindScale_Wet=2.994871, #It is estimated based on histrocial data.
WindShape_Dry=2.084098, #It is estimated based on histrocial data.
WindScale_Dry=1.989019, #It is estimated based on histrocial data.
# 2020
# centerPointsAvgWat=c(15.51984, 18.82241, 22.12498, 25.42755, 28.73012, 32.03269, 35.33526),
# centerPointsAvgMat=c(78.73775, 191.70698, 304.67621, 417.64544, 530.61467, 643.58390, 756.55313),
# centerPointsSdWat=seq(1,1, by=1),
# centerPointsSdPos=c(0.005,0.1),
# centerPointsMeanPos=seq(0.8,1.2, by=0.12),
# centerPointsTem=c(9.34062, 12.54412, 15.74761, 18.95111),
# centerPointsPre=c(0.0000, 0.2500, 3.0375, 9.1125, 15.1875, 21.2625),
# centerPointsGlobRd=c(38.25177, 79.57014, 120.88850, 162.20686),
# centerPointsWind=c(1.063851, 2.439893, 3.815936, 5.191979),
# temMeanDry=12.37325, #It is estimated based on histrocial data.
# temMeanWet=10.71889, #It is estimated based on histrocial data.
# temVarDry=6.873857, #It is estimated based on histrocial data.
# temVarWet=3.381453, #It is estimated based on histrocial data.
# GRdMeanDry=119.6411, #It is estimated based on histrocial data.
# GRdMeanWet=79.07292, #It is estimated based on histrocial data.
# GRdVarDry=1600.626, #It is estimated based on histrocial data.
# GRdVarWet=1059.53, #It is estimated based on histrocial data.
# WindMeanDry=2.117543, #It is estimated based on histrocial data.
# WindMeanWet=2.53611, #It is estimated based on histrocial data.
# WindVarDry=1.020088, #It is estimated based on histrocial data.
# WindVarWet=0.7630564, #It is estimated based on histrocial data.
# dryDayTh=0.25,
# precShape=0.2982603, #It is estimated based on histrocial data.
# precScale=6.9151, #It is estimated based on histrocial data.
# prDryWet=0.2949405, #It is estimated based on histrocial data.
# prWetWet=0.5920928, #It is estimated based on histrocial data.
# WindShape_Wet=3.055488, #It is estimated based on histrocial data.
# WindScale_Wet=3.158224, #It is estimated based on histrocial data.
# WindShape_Dry=1.983153, #It is estimated based on histrocial data.
# WindScale_Dry=2.715127, #It is estimated based on histrocial data.
# Sandy loam
hydroWatR=0.041, # cm3/cm3
hydroWatS= 0.412, # cm3/cm3
hydroVanAlpha=0.068,
hydroVanM=0.2436,
hydroVanN=1.322,
hydroBulkDensity=1.53, # (g/cm3)- The value of the bulk density that is related to A horizon
# 2016
gSSMW=0.1528,
# 2020
# gSSMW=0.1704,
# 2016
gSSMV=12.81,
# 2020
# gSSMV=41.66,
gSSMm0=1,
gSSMc0=0.001,
nGSSMm0=4,
nGSSMc0=2,
nGSSMK=20,
rewRisk=TRUE,
check=FALSE
){
model <- list(opNum=opNum)
model$opSeq <- opSeq
model$tMax <- tMax
model$StartDay <- StartDay
model$LastDay <- LastDay
model$opE <- opE
model$opL <- opL
model$opD <- opD
model$opDelay <- opDelay
model$opFixCost <- opFixCost
model$watTh <- watTh
model$coefLoss <- coefLoss
model$priceYield <- priceYield
model$machCap <- machCap
model$yieldHa <- yieldHa
model$fieldArea <- fieldArea
model$coefTimeliness <- coefTimeliness
model$costSkip <- costSkip
model$watUpper <- watUpper
model$watLower <- watLower
model$weightCompletion <- weightCompletion
model$weightWorkable <- weightWorkable
model$weightTraffic <- weightTraffic
model$minOpt <- minOpt
model$maxOpt <- maxOpt
model$temMeanDry <- temMeanDry
model$temMeanWet <- temMeanWet
model$temVarDry <- temVarDry
model$temVarWet <- temVarWet
model$GRdMeanDry <- GRdMeanDry
model$GRdMeanWet <- GRdMeanWet
model$WindMeanDry <- WindMeanDry
model$WindMeanWet <- WindMeanWet
model$GRdVarDry <- GRdVarDry
model$GRdVarWet <- GRdVarWet
model$WindVarDry <- WindVarDry
model$WindVarWet <- WindVarWet
model$dryDayTh <- dryDayTh
model$precShape <- precShape
model$precScale <- precScale
model$prDryWet <- prDryWet
model$prWetWet <- prWetWet
model$WindShape_Wet <- WindShape_Wet
model$WindScale_Wet <- WindScale_Wet
model$WindShape_Dry <- WindShape_Dry
model$WindScale_Dry <- WindScale_Dry
model$hydroWatR <- hydroWatR
model$hydroWatS <- hydroWatS
model$hydroVanAlpha <- hydroVanAlpha
model$hydroVanM <- hydroVanM
model$hydroVanN <- hydroVanN
model$hydroBulkDensity <- hydroBulkDensity
model$gSSMW <- gSSMW
model$gSSMV <- gSSMV
model$gSSMm0 <- gSSMm0
model$gSSMc0 <- gSSMc0
model$nGSSMm0 <- nGSSMm0
model$nGSSMc0 <- nGSSMc0
model$nGSSMK <- nGSSMK
model$rewRisk <- rewRisk
model$check <- check
model$centerPointsAvgWat <- centerPointsAvgWat
model$centerPointsAvgMat <- centerPointsAvgMat
model$centerPointsSdWat <- centerPointsSdWat
model$centerPointsSdPos <- centerPointsSdPos
model$centerPointsMeanPos <- centerPointsMeanPos
model$centerPointsTem <- centerPointsTem
model$centerPointsPre <- centerPointsPre
model$centerPointsGlobRd <- centerPointsGlobRd
model$centerPointsWind <- centerPointsWind
#Discritization of continious states:
obj <- discretizeGaussian::Discretize()
disAvgWat <- matrix()
disAvgMat <- matrix()
disSdWat <- matrix()
disSdPos <- matrix()
disMeanPos <- matrix()
disTem <- matrix()
disPre <- matrix()
disGR <- matrix()
disWind <- matrix()
disAvgWat <- as.matrix(obj$discretize1DVec(centerPointsAvgWat, mInf=-1, inf=100, asDF=F), ncol=3)
disAvgMat <- as.matrix(obj$discretize1DVec(centerPointsAvgMat, mInf=-1, inf=100, asDF=F), ncol=3)
disSdWat <- as.matrix(obj$discretize1DVec(centerPointsSdWat, inf=100, mInf=0.01, asDF=F), ncol=3)
disSdPos <- as.matrix(obj$discretize1DVec(centerPointsSdPos, inf=100, mInf=0, asDF=F), ncol=3)
disMeanPos <- as.matrix(obj$discretize1DVec(centerPointsMeanPos, inf=100, asDF=F), ncol=3)
disTem <- as.matrix(obj$discretize1DVec(centerPointsTem, inf=100, asDF=F), ncol=3)
disPre <- as.matrix(obj$discretize1DVec(centerPointsPre, inf=50, mInf=0, asDF=F), ncol=3)
disGR <- as.matrix(obj$discretize1DVec(centerPointsGlobRd, inf=250, mInf=0, asDF=F), ncol=3)
disWind <- as.matrix(obj$discretize1DVec(centerPointsWind, inf=10, mInf=0, asDF=F), ncol=3)
model$disAvgWat <- disAvgWat
model$disAvgMat <- disAvgMat
model$disSdWat <- disSdWat
model$disSdPos <- disSdPos
model$disMeanPos <- disMeanPos
model$disTem <- disTem
model$disPre <- disPre
model$disGR <- disGR
model$disWind <- disWind
#Soil water contents based on weather info. from centerpoints:
Combn_centrpoints <- as.matrix(expand.grid(disTem[,1], disPre[,1], disGR[,1], disWind[,1]))
combinations <- rbind(rbind(rbind(0,Combn_centrpoints),0),0)
days <- format(as.Date(seq(from=as.Date("2015-09-06"), to=as.Date("2016-09-26"), by='days')), "%m/%d/%Y") # The number of days is based on the number of combinations of centerpoints
days <- gsub('(?<=\\b|-)0', '', days, perl=TRUE)
wed_data <- as.data.frame(matrix(nrow = length(days) , ncol = 5))
colnames(wed_data) <- c("datetime", "Temperature", "Precipitation", "GlobalRadiation", "WindSpeed")
for (i in 1:length(days)){
date <- as.Date(days[i],'%m/%d/%Y')
wed_data$datetime[i] <- as.numeric(date)
if (i==1){
wed_data$Temperature[i] <- 0
wed_data$Precipitation[i] <- 0
wed_data$GlobalRadiation[i] <- 0
wed_data$WindSpeed[i] <- 0
} else{
wed_data$Temperature[i] <- round(as.matrix(combinations[i,1]), digits = 6)
wed_data$Precipitation[i] <- round(as.matrix(combinations[i,2]), digits = 4)
wed_data$GlobalRadiation[i] <- round(as.matrix(combinations[i,3]), digits = 1)
wed_data$WindSpeed[i] <- round(as.matrix(combinations[i,4]), digits = 4)
}
}
Mois_WedcentrePoints <- matrix()
DWF_File(wed_data)
DaisyOutputs <- Daisy(wed_data)
Mean_DaisyOutputs <- Mean_Daisy(DaisyOutputs)
Mean_DaisyOutputs$Mean_SoilWaterContent[2] <- Mean_DaisyOutputs$Mean_SoilWaterContent[2]*100
wed_data <- wed_data[c(-1, -(dim(wed_data)[1]-1), -dim(wed_data)[1]),]
temp_wed_data <- as.data.frame(matrix(nrow = dim(wed_data)[1] , ncol = 5))
colnames(temp_wed_data) <- c("datetime", "Temperature", "Precipitation", "GlobalRadiation", "WindSpeed")
for (i in 1:dim(wed_data)[1]){
temp_wed_data[i,]<- wed_data[i,]
}
wed_data <- temp_wed_data
dim <- dim(Mean_DaisyOutputs$Mean_SoilWaterContent)[1]
Mois_WedcentrePoints <- as.data.frame(matrix(nrow = dim , ncol = 2))
colnames(Mois_WedcentrePoints) <- c("datetime", "Moisture")
for (j in 1:dim(Mois_WedcentrePoints)[1]){
date <- as.Date(Mean_DaisyOutputs$Mean_SoilWaterContent[j,1])
Mois_WedcentrePoints[j,1] <- as.numeric(date)
Mois_WedcentrePoints[j,2] <- as.numeric(Mean_DaisyOutputs$Mean_SoilWaterContent[j,2])
}
wed_data <- as.matrix(wed_data)
Mois_WedcentrePoints <- as.matrix(Mois_WedcentrePoints)
model$wed_data <- wed_data
model$Mois_WedcentrePoints <- Mois_WedcentrePoints
SCIout <- matrix(nrow = dim(disAvgMat)[1], ncol=1)
for (i in 1:dim(disAvgMat)[1]){
SCIout[i,1] <- Terranimo_SCI(disAvgMat[i,1])[5,1]
}
model$SCIout <- SCIout
return(model)
}
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