MultiHazard-Shiny App

🌟 Overview

The MultiHazard-Shiny App is an interactive web application for conducting bivariate joint probability analyses. Powered by the MultiHazard R package, this application provides a user-friendly interface for conducting advanced statistical analyses on time series data, supporting researchers and practitioners to explore the relationships between hydrological and meteorological variables, aiding in the assessment of multi-hazard risks such as compound flooding.

🛠 Key Features

• Interactive Analysis: Perform bivariate joint probability analysis with an intuitive Shiny interface.
• Flexible Data Support: Analyze time series data with a Date class date/time column.
• Data Compatibility: Works seamlessly with rainfall, water levels, and other hazard-related datasets.
• Visualization: Generate clear and concise visual outputs to aid in decision-making.
• Example Dataset: Includes example time series of rainfall (Miami Airport) and water levels (coastal control structure S-22) in the Data folder.
• Built-in MultiHazard Package Integration: Seamless use of advanced statistical tools provided by the MultiHazard R package.

🔗 Quick Links

• MultiHazard R Package: Core package providing statistical tools and methods.
• Hosted App: Access the live application.
• Shiny App Repository: Explore the codebase for the Shiny app.

📦 Installation & Usage

Option 1: Online Access

• Hosted App: Access the app directly through this link: MultiHazard Shiny App

Option 2: Local Installation

1. Clone the repository:

   git clone https://github.com/rjaneUCF/MultiHazard-Shiny.git
   cd MultiHazard-Shiny

2. Install the required R packages:

   install.packages(c("shiny", "MultiHazard", "ggplot2", "dplyr", "gamlss", "gamlss.mx", "fitdistrplus"))

3. Launch the Shiny app locally:

   shiny::runApp()

📊 Example Dataset

The repository includes example data files located in the Data folder, containing:

• Rainfall data from Miami Airport: Miami_Airport_Rainfall_df.csv.
• Water level data at the S-22 coastal control structure: S22_Tailwater_df.csv.

Notes:

• Rainfall Detrending: We recommend not detrending rainfall data before analysis.
• Ensure the date/time column in your dataset is of class Date for compatibility.

📝 Citation

If you use this app in your work, please cite the MultiHazard package and any associated publications. Proper citation supports the development of tools like this one!

Jane, R., Cadavid, L., Obeysekera, J., and Wahl, T. (2020). Multivariate statistical modelling of the drivers of compound flood events in South Florida, Nat. Hazards Earth Syst. Sci., 20, 2681–2699, https://doi.org/10.5194/nhess-20-2681-2020.

🤝 Contributing

We welcome contributions! Whether it’s fixing bugs, suggesting new features, or improving documentation, your input is valuable. Please submit a pull request or open an issue on the GitHub repository.

📄 License

This project is licensed under the MIT License. Feel free to use, modify, and distribute it in accordance with the license terms.

💡 Contact

For questions or feedback, please contact the repository maintainers.

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Empowering informed decision-making through robust hazard analysis.