SLIPER – Soil Landslide Information and Prediction & Early Response

Version 3.1.0 – October 2025
SLIPER is a modular open-source toolkit developed by CIMA Research Foundation and ARPAL Liguria for landslide forecasting, early warning, and risk assessment. It integrates rainfall, soil moisture, soil slips, indicators, scenarios, and predictors into a fully automated end-to-end workflow for environmental monitoring and forecasting systems.

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Workflow Overview

SLIPER operates through a structured multi-stage processing chain, enabling the transformation of raw geospatial and observational data into operational prediction products.

1. Data Processing – Preprocessing of rainfall, soil moisture, and soil slips datasets.
2. Indicators – Computation of rainfall and soil moisture indicators over time.
3. Scenarios – Integration of indicators and observed soil slips to define hazard scenarios.
4. Predictors – Generation of predictors for nowcasting and forecasting applications.
5. Viewer – Visualization of predictors, time-series trends, and validation metrics.

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Modules and Documentation

All SLIPER modules are independent yet interoperable. Detailed documentation for each component is available under the /docs directory.

1. Data Processing

Modules responsible for converting raw environmental inputs into harmonized datasets.

• Rainfall Data Processing
• Soil Moisture Data Processing
• Soil Slips Data Processing

2. Indicators

Indicator modules compute time-based and cumulative statistics from processed datasets.

• Rainfall Indicators
• Soil Moisture Indicators

3. Scenarios and Predictors

Modules that combine multiple sources and produce predictors for landslide hazard evaluation.

• Scenarios Generation
• Predictors Computation

4. Visualization

Interactive tools for analysis, validation, and visual communication.

• Viewer Tool

5. Tools

Utilities that support data handling, configuration management, and operational automation.

• Merger
• Organizer
• Transfer
• Realtime Configuration Scripts

6. Workflow Description

• SLIPER Workflow Overview

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Installation and Setup

Clone from GitHub

git clone https://github.com/c-hydro/sliper.git
cd sliper

Conda Environment (recommended)

conda create -n sliper_env python=3.9
conda activate sliper_env
pip install -r requirements.txt

This will install all dependencies required to execute SLIPER modules.

Local Setup Scripts

The repository also provides helper scripts for initializing local runners and configurations:

bash setup_sliper_system_runner_data.sh

These scripts simplify environment setup and data directory structuring.

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Execution

SLIPER applications can be run as standalone modules or integrated pipelines.

Python Modules

Each Python module can be executed as follows:

python sliper_<module>_main.py -settings_file configuration.json -time "YYYY-MM-DD HH:MM"

Example:

python sliper_data_rain_main.py -settings_file sliper_config_data_rain.json -time "2025-10-01 00:00"

Shell Tools

Shell utilities simplify file management and real-time operations.

bash sliper_tools_organizer_sm_file2folders.sh [SRC] [DST]
bash sliper_tools_scenarios_configuration_realtime.sh
bash sliper_tools_predictors_configuration_realtime.sh

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Inputs and Outputs

Inputs

• Rainfall grids (GeoTIFF)
• Soil moisture data (NetCDF)
• Soil slips data (CSV)
• Configuration JSONs (model parameters, thresholds, and IO paths)

Outputs

• Processed data (GeoTIFF)
• Indicators, scenarios, and predictors (CSV)
• Visualization plots (JPEG, PNG)
• Logs and diagnostic files

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Extended Workflow (including Tools)

graph LR
  A[Raw Datasets] --> B(Data Processing)
  B --> C(Indicators)
  C --> D(Scenarios)
  D --> E(Predictors)
  E --> F(Viewer)

  subgraph Tools
    T1[Organizer] --> B
    T2[Transfer] --> A
    T3[Merger] --> E
    T4[Realtime Config] --> D & E
  end

Loading

The Tools modules extend the workflow to support real-time operations, file synchronization, and automated configuration updates.

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Key Features

• Modular and scalable architecture
• Fully configurable through JSON settings
• Compatible with gridded and point-based datasets
• End-to-end integration from data ingestion to prediction visualization
• Designed for operational landslide forecasting systems

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Repository Structure

sliper/
├── docs/                       # Documentation files
├── package_sliper/             # Python package code
├── notebook/                   # Example notebooks
├── setup_sliper_system_runner_data.sh
├── notes_sliper_system_runner_data.txt
├── README.md
├── LICENSE.md
├── CHANGELOG.md
├── AUTHORS.md
├── CODEOWNERS.md

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Additional References

• LICENSE.md: License terms and conditions
• CHANGELOG.md: Release notes and version history
• AUTHORS.md: Contributors and collaborators
• CODEOWNERS.md: Maintainers responsible for repository modules

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Contacts

For inquiries, collaboration, or bug reports:

• Fabio Delogu – fabio.delogu@cimafoundation.org
• CIMA Research Foundation – https://www.cimafoundation.org

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© 2025 CIMA Research Foundation and ARPAL Liguria. All rights reserved.
SLIPER is distributed under the terms of the LICENSE.md.