RANDOM FOREST IN FORECASTING RAIN-INDUCED LANDSLIDES

Abstract

Recent studies in the Philippines on landslides have primarily focused on susceptibility mapping and generation of hazard maps. Whereas, research on landslide forecasting remains less explored. As artificial intelligence continues to progress, forecasting methods have become more advanced, allowing opportunities to predict landslide occurrences before they happen.

The study focuses on rainfall, a primary triggering factor of landslides, examining its relationship with environmental variables such as slope, soil type, and soil moisture to predict potential landslide events. The study applies the random forest model to forecast landslides using a minimized yet significant set of predictors.

One-Way ANOVA was conducted to assess differences in model performance under various combinations of input variables, followed by post hoc tests to determine the most effective predictive variables. The researchers found out that combining rainfall and environmental variables as predictors yielded the highest accuracy at 90%, outperforming models that use only individual variable inputs. These findings demonstrate the effectiveness of the random forest model in forecasting landslides even with limited resources and data.

Built With

• Framework: Flask
• Data Science: Pandas, NumPy, Scikit-learn
• Geospatial: GeoPandas, Rasterio, Shapely
• AI/LLM: Google GenAI
• APIs: OpenMeteo, OpenStreetMap, MapBox

Prerequisites

• Python 3.8+
• Git
• Git LFS (Required for downloading large files)
  • Windows: Download from git-lfs.com (often included with Git for Windows).
  • macOS: brew install git-lfs
  • Linux (Ubuntu/Debian): sudo apt-get install git-lfs

1. Install Git LFS & Clone the Repository You must initialize LFS before accessing the data files to ensure large assets (models/maps) download correctly.

git lfs install
git clone https://github.com/Lamatz/Thesis-TwoStepsAhead
cd Thesis-TwoStepsAhead
git lfs pull

2. Create a Virtual Environment It is recommended to use a virtual environment to manage dependencies.

• Windows:

  python -m venv venv

• macOS / Linux:

  python3 -m venv venv

3. Activate the Virtual Environment

• Windows (Command Prompt):

  venv\Scripts\activate

• Windows (PowerShell):

  .\venv\Scripts\Activate.ps1

• macOS / Linux:

  source venv/bin/activate

Note

You will know the environment is active when you see (venv) at the start of your command line.

4. Install Dependencies Now that the environment is active, install the required packages:

pip install -r requirements.txt

Warning

Windows Users: Installing geopandas and rasterio via pip on Windows can sometimes fail due to C++ dependency compilation. If pip install fails, it is highly recommended to use Conda instead, or download the pre-compiled .whl files for GDAL and Rasterio from Christoph Gohlke's libs.

Configuration

1. Create a .env file in the root directory.
2. Add your Google GenAI API key:

# .env content
GOOGLE_API_KEY=your_api_key_here

Run Locally

Once you have installed the dependencies and configured your .env file, follow these steps to start the application.

1. Activate the Virtual Environment (If you closed your terminal, activate it again).

• Windows: venv\Scripts\activate
• Mac/Linux: source venv/bin/activate

2. Start the Server Run the server file from the root directory:

# Windows
python backend/server.py

# macOS / Linux
python3 backend/server.py

You should see output indicating the Flask server is running.

3. Open the Application

you may open the HTML files directly, but ensure the backend server is running first to handle API requests