flood_risk_models/svm.R at master · izzyvera/flood_risk_models · GitHub

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# imports
library(kernlab)
library(e1071)        # SVM methodology
library(RColorBrewer) # customized coloring of plots
library(ggplot2)
library(caret)
library(pROC)

# data extraction
uniondata = read.csv("05054000+05082500.csv")
uniondata = uniondata[-1][-1]

# data split
set.seed(3033)
uniondata$Severity = factor(uniondata$Severity)
intrain <- createDataPartition(y = uniondata$Severity, p= 0.75, list = FALSE)
training <- uniondata[intrain,]
testing <- uniondata[-intrain,]

#names(uniondata)
#svmdata = uniondata[-1]
#svmdata <- scale(svmdata)
#svmdata <- data.frame(svmdata)
#svmdata$Severity = factor(svmdata$Severity)
#x=model.matrix(svmdata$Severity~.,svmdata)
#y=svmdata$Severity
#svmtrain = sample(1:nrow(svmdata),0.5*nrow(svmdata))
#svmrest = svmdata[-svmtrain,]
#svmvalid = sample(1:nrow(svmrest), 0.5*nrow(svmrest))
#svmtest = -svmvalid
#svmtv = c(svmtrain, svmvalid)
#svmcolnames = c("TurbidityMin", "TurbidityMean", "TurbidityMax", "Discrage", "OxygenMax", "OxygenMin", "OxygenMean", "TempMax", "TempMin", "TempMean", "ConductMax", "ConductMin", "ConductMean", "pHMax", "pHMin", "pHMed", "Flood", "Severity", "Month")
#svmdata$Severity = ifelse(svmdata$Severity == 1, "Yes", "No")

# linar svm
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(3233)

svm_Linear <- train(Severity ~., data = training, method = "svmLinear",
                    trControl=trctrl,
                    preProcess = c("center", "scale"),
                    tuneLength = 10)

svmlinear.pre <- predict(svm_Linear,testing)
confusionMatrix(svmlinear.pre, testing$Severity)
y_pre = as.numeric(svmlinear.pre)
y_acc = as.numeric(testing$Severity)
svmlinear.roc <- roc(y_acc, y_pre)
plot(svmlinear.roc, print.auc=TRUE, auc.polygon=TRUE,
     grid=c(0.1, 0.2),grid.col=c("green", "red"),
     max.auc.polygon=TRUE,auc.polygon.col="skyblue", print.thres=TRUE)
svmlinear.imp <- varImp(svm_Linear,scale=F)
plot(svmlinear.imp,top=10)

# reduce flood type, gage height and discharge
uniondata = uniondata[-18]
uniondata = uniondata[-8][-4]

# data split
set.seed(3033)
uniondata$Severity = factor(uniondata$Severity)
intrain <- createDataPartition(y = uniondata$Severity, p= 0.75, list = FALSE)
training <- uniondata[intrain,]
testing <- uniondata[-intrain,]
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(3233)
svm_Linear <- train(Severity ~., data = training, method = "svmLinear",
                    trControl=trctrl,
                    preProcess = c("center", "scale"),
                    tuneLength = 10)

svmlinear.pre <- predict(svm_Linear,testing)
confusionMatrix(svmlinear.pre, testing$Severity)
acc = mean(svmlinear.pre == testing$Severity) # 0.9688359
y_pre = as.numeric(svmlinear.pre)
y_acc = as.numeric(testing$Severity)
svmlinear.roc <- roc(y_acc, y_pre)
plot(svmlinear.roc, print.auc=TRUE, auc.polygon=TRUE,
     grid=c(0.1, 0.2),grid.col=c("green", "red"),
     max.auc.polygon=TRUE,auc.polygon.col="skyblue", print.thres=TRUE)
svmlinear.imp <- varImp(svm_Linear,scale=F)
plot(svmlinear.imp,top=10)

# nonlinear kernel
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
set.seed(3233)

svm_Radial <- train(Severity ~., data = training, method = "svmRadial",
                    trControl=trctrl,
                    preProcess = c("center", "scale"),
                    tuneLength = 10)
svmradial.pre <- predict(svm_Radial,testing)
confusionMatrix(svmradial.pre, testing$Severity)
acc = mean(svmradial.pre == testing$Severity) # 0.9871677
y_pre = as.numeric(svmradial.pre)
y_acc = as.numeric(testing$Severity)
svmradial.roc <- roc(y_acc, y_pre)
plot(svmradial.roc, print.auc=TRUE, auc.polygon=TRUE,
     grid=c(0.1, 0.2),grid.col=c("green", "red"),
     max.auc.polygon=TRUE,auc.polygon.col="skyblue", print.thres=TRUE)
svmradial.imp <- varImp(svm_Radial,scale=F)
plot(svmradial.imp,top=10)