Project AMIHAN

Web Application for Project AMIHAN: Adaptive Model for Inundation Height Analysis using Neuro-Fuzzy

About The Project

Project AMIHAN is a web application that implements an AI model to predict flood heights in the Philippines—a country highly susceptible to flooding.

The goal is to make flood height predictions accessible and user-friendly through a web interface. This project was also developed as a final requirement for our Introduction to AI course.

Built With

• React.js
• Python Flask
• Google Maps API
• Open Elevation API
• Open Meteo API

Getting Started

Prerequisites

• Node.js and npm
• Python 3.10 – 3.11

Installation

Clone the repo:

git clone git@github.com:AkzechKyla/ProjectAMIHAN.git
cd ProjectAMIHAN

Configuration

Create a .env file in the client folder with:

 VITE_GOOGLE_MAPS_API_KEY=your_google_maps_api_key
 VITE_GOOGLE_MAPS_MAP_ID=your_google_maps_map_id

Running the App

1. Frontend

   cd client
   npm install
   npm run dev

2. Backend

   cd server
   pip install -r requirements.txt
   python main.py

Usage

1. Open the frontend in your browser.
2. Click a location on the map.
3. Input or verify:
   • Latitude, Longitude
   • Elevation
   • Precipitation
4. Click the "Submit" button to see the results:
   • Flood height level prediction
   • Risk level & category
   • Elevation & precipitation level

Roadmap

• Refactor code
• Add search bar
• Add PAGASA Rainfall Advisory
• Add reset button
• Add loading screen

Contributors:

Name	Role	Contributions
AkzechKyla	Full-Stack Developer	Developed both frontend and backend systems, integrated third-party APIs, and handled application architecture.
Kyla Valoria	UI Developer	Designed the overall user interface and created the landing page.
John Lloyd Legaspi	AI Model Developer	Researched, designed, and implemented the Neuro-Fuzzy AI model that predicts flood heights based on geographical and meteorological data inputs.