How to Approach MATLAB Assignment on Landslide Susceptibility Mapping Using Machine Learning

Daniel Cooper

Australia

Machine Learning

Daniel Cooper holds a Master’s degree in Engineering from Murdoch University, Australia, and has over 9 years of experience. He specializes in machine learning techniques for environmental risk mapping and terrain modeling.

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This end-to-end approach not only demonstrates the power of MATLAB in geospatial analysis but also helps university students understand how to apply theoretical concepts to real-world problems. It’s an ideal resource for those looking to solve their machine learning assignment or gain practical insight into environmental modeling using MATLAB.

Introduction and Project Overview

The primary objective of this project is to build a robust, reusable MATLAB-based pipeline that can generate landslide susceptibility maps. These maps are created using geographical data layers and trained neural network models. Using various terrain and climate parameters, a predictive model is trained to assess the risk level of landslides across a region.

MATLAB was selected as the environment for this project due to its robust support for matrix operations, image processing, neural networks, and geospatial analysis. The entire system, from data preprocessing to model training and visualization, was completed using MATLAB’s built-in capabilities, making it an excellent end-to-end example for students studying engineering or environmental modeling.

Data Collection

The first stage of this project involved gathering relevant raster data maps that represent various environmental and geographical parameters influencing landslides. The data was obtained in raster image formats (primarily .jpg) and covered factors such as:

• Slope
• Aspect
• Plan curvature
• Stream Power Index (SPI)
• Topographic Wetness Index (TWI)
• Elevation
• Distance from fault lines
• Distance from rivers
• Distance from roads
• Rainfall
• Normalized Difference Vegetation Index (NDVI)
• Soil type

Additionally, a raster map of historical landslides in the region was included to serve as the ground truth for training the model.

This raster-based data allows visual representation of parameters over the region and forms the foundation for both training and prediction.

Data Preprocessing

Segmenting the Region of Interest (ROI)

To focus the analysis only on the specific region of concern, the project used MATLAB’s Image Processing Toolbox. The imageSegmenter app was employed to manually define the Region of Interest (ROI). Using the Draw ROI feature, a mask was created and exported as a .m function file.

This function was then used to mask all raster images, ensuring that the analysis remained within the defined boundary. This was a smart technique to reduce processing time and ignore irrelevant data from surrounding areas.

Preparing Training Data

Next, the raster files were converted into matrices, and the pixel data corresponding to the ROI was extracted. Each environmental parameter was treated as an individual layer. Using the ROI mask, the values at each pixel location were isolated and combined to form a feature matrix.

In this project, 12 geographical parameters were considered, and each parameter was represented using RGB channels, resulting in 36 input values per pixel.

Simultaneously, the historical landslide data was clustered into five risk levels using image-based clustering. This data served as the target output, allowing the model to learn how different environmental conditions relate to landslide probability.

The processed data was saved into .csv files, one for inputs and one for targets. This modular design allows future users to swap out datasets or regions with minimal code changes.

Neural Network Model and Training

A Cascade Forward Neural Network (CFNN) was chosen for this task. This type of network allows not only connections from the input layer to hidden and output layers but also direct connections between all layers. It can outperform feedforward networks for certain complex tasks due to this flexible structure.

Model Architecture

• Input Layer: 36 nodes (RGB values for 12 parameters)
• Hidden Layer: 24 neurons
• Output Layer: Initially one node (risk class); later extended to 3 nodes (RGB encoding for color-coded susceptibility)

Each input sample was a row vector of 36 values corresponding to the pixel-level data across all input maps. The output was a single class label, later mapped back to color for visualization.

The network was trained using supervised learning, where the model learned the relationship between environmental parameters and historical landslide occurrences.

Once trained, the weights of the model were saved in .mat format for reuse and testing.

Prediction and Susceptibility Map Generation

With a trained model in place, the next task was to predict the landslide risk for every pixel in the ROI.

The process involved:

1. Extracting all pixel indices from the ROI mask.
2. Pulling the corresponding parameter values for each pixel.
3. Running predictions using the trained network.
4. Mapping each predicted class label back to its corresponding color encoding.
5. Plotting the results over the entire region to generate the Landslide Susceptibility Map.

This map allows quick and easy visual inspection of risk levels across the area. Regions with high susceptibility are clearly distinguished from low-risk zones using consistent color schemes.

Flexibility and Usability

One of the major strengths of this implementation is its flexibility:

• Modular design: The code allows easy substitution of raster files, making it reusable for multiple regions without rewriting core logic.
• Supports multiple formats: Input files can be in .jpg, .png, .webp, .jpeg, or other raster image formats.
• ROI customization: Users can define their own region of interest using MATLAB’s GUI-based image segmenter, which makes the tool user-friendly for students with limited coding experience.
• Scalable training: The model can be retrained with different regions and datasets by simply updating file paths and data arrays.
• Compact output: Final models and output maps are lightweight and can be shared easily.

Key Challenges Faced

The data acquisition phase proved to be the most time-consuming and mentally taxing. Raster files often contain noise or unaligned dimensions that required extensive preprocessing. Converting raw images into a structured matrix format while maintaining spatial consistency across all parameters was crucial for accuracy.

Another hurdle was correctly labeling the target data. Cluster labels for historical landslides needed to be consistent and correctly color-encoded to allow the final susceptibility map to make sense visually.

Despite these challenges, once the data was in usable format, the model training and prediction pipeline worked efficiently and produced satisfactory results.

Final Output and Interpretation

The final susceptibility map gives a pixel-by-pixel classification of landslide risk. The color-coding used in the output map is based on historical landslide clustering, ensuring consistency and interpretability.

For instance:

• Red may denote very high risk
• Orange moderate risk
• Green low or no risk

These maps can help urban planners, civil engineers, and disaster management teams make informed decisions about construction, evacuation planning, and infrastructure placement.

Educational Value

This project offers a rich learning experience for students. Here are some educational takeaways:

• Understanding of raster data and geospatial preprocessing
• Application of image segmentation for region targeting
• Hands-on experience with neural networks in MATLAB
• Data normalization and clustering techniques
• Importance of model training-validation cycles
• Visualization of results using RGB encoding

Whether you’re learning about machine learning, environmental modeling, or geospatial analytics, this project integrates all three with a hands-on, application-driven approach.

Conclusion

Using MATLAB for landslide susceptibility mapping combines the power of machine learning with practical applications in disaster management and geospatial analysis. This project showcased how a student or researcher can start from raster datasets, process them using image tools, train a neural network, and generate risk maps that are visually informative and technically sound. The entire workflow was done using MATLAB, demonstrating its capabilities as a one-stop platform for data science and geospatial engineering. Students pursuing university-level MATLAB assignments or research-based projects can learn a great deal from replicating or extending this implementation. If you're looking help with MATLAB assignment or need help with similar machine learning-based assignments, this project serves as an excellent reference model. Our team is here to assist with custom MATLAB solutions, training, and academic support across disciplines.