To prepare a Receiver Operating Characteristic (ROC) curve in Origin software for validating a Landslide Susceptibility Model, you can follow these steps:

This will give you a visual and numerical validation of your Landslide Susceptibility Model.

Thank you for your quick response. Can you please tell me how to prepare the dataset of landslide and compute TPR & FPR?

1. Preparing the Landslide Dataset

The dataset should include data points that represent conditions leading to landslides (or not) based on various features. Here’s how you can prepare it:

a. Collect Data

b. Data Labeling

Label the data points as:

• Positive (1): Locations where landslides occurred.
• Negative (0): Locations where landslides did not occur.

c. Data Splitting

• Split the dataset into training and testing sets, typically in a 70-30 or 80-20 ratio.

2. Training the Model

Use a machine learning model to predict landslide occurrences based on your features (e.g., logistic regression, random forest, neural networks).

a. Feature Selection

• Choose relevant features that contribute to the occurrence of landslides.

b. Model Training

• Train your model on the training set using the selected features.

3. Making Predictions

Use the trained model to predict the likelihood of landslides on the test dataset.

4. Computing TPR & FPR

Once you have the predictions, you can compute the True Positive Rate (TPR) and False Positive Rate (FPR).

a. Confusion Matrix

First, create a confusion matrix:

Predicted PositivePredicted NegativeActual PositiveTrue Positive (TP)False Negative (FN)Actual NegativeFalse Positive (FP)True Negative (TN)

b. Formulas

• TPR=TPTP+FN\text{TPR} = \frac{TP}{TP + FN}TPR=TP+FNTP​TPR (True Positive Rate): Also known as sensitivity or recall, it is the proportion of actual positives that are correctly identified.
• FPR=FPFP+TN\text{FPR} = \frac{FP}{FP + TN}FPR=FP+TNFP​FPR (False Positive Rate): It is the proportion of actual negatives that are incorrectly identified as positives.

c. Calculate TPR & FPR

Using your predictions and the actual labels from the test set, you can calculate TP, FN, FP, and TN, and then use the formulas above to compute TPR and FPR.

5. Example Computation

Suppose you have the following confusion matrix after making predictions:

Predicted PositivePredicted NegativeActual Positive8020Actual Negative1090.i

personally used it in my article.. https://www.webology.org/data-cms/articles/20220604080847pmwebology%2018%20(6)%20-%20468%20pdf.pdf

Thank you 😊