💧 AquaOS — AI Water Quality Monitoring System

Intelligent water analysis powered by Hybrid AI (Machine Learning + Explainable Logic)

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🚀 Overview

AquaOS is a data-driven water quality monitoring platform designed to evaluate, interpret, and track water safety using a combination of machine learning models and AI-based reasoning.

The system transforms raw sensor inputs into:

• Clear safety decisions
• Quantifiable risk scores
• Structured explanations

It is built to simulate real-world monitoring environments such as:

• Water treatment facilities
• Environmental monitoring systems
• Reservoir and river analysis

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🧠 Core Concept — Hybrid AI

Unlike traditional systems that only classify results, AquaOS uses a multi-layered AI architecture:

Layer	Function
ML Model (Random Forest)	Predicts water quality score
Rule-Based Engine	Explains decisions
Risk Engine	Evaluates future degradation
AI Advisor	Provides domain-level insights

This enables the system to be both: ✔ Predictive
✔ Explainable

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🔬 Parameters Analyzed

The system evaluates key indicators of water safety:

• pH Level — acidity/alkalinity balance
• Turbidity — suspended particles
• Total Dissolved Solids (TDS) — dissolved substances
• Dissolved Oxygen — oxygen availability

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⚙️ Core Capabilities

🤖 AI-Based Water Analysis

• Predicts Water Quality Score (0–100)
• Classifies:
  • SAFE
  • UNSAFE
• Processes real-time or simulated data

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🧠 Explainable AI (XAI)

• Identifies root causes of contamination
• Provides scientific reasoning
• Generates corrective recommendations

Not just what happened, but why it happened

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📊 Water Quality Index (WQI)

Score Range	Classification
90 – 100	Excellent
70 – 89	Good
50 – 69	Fair
25 – 49	Poor
0 – 24	Hazardous

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🔮 Risk Assessment

• Evaluates future degradation probability
• Categorizes risk:
  • LOW
  • MODERATE
  • HIGH

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🧪 Scenario Simulation

• Modify parameters dynamically
• Observe impact on water safety
• Enables predictive experimentation

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📊 Reports & History (UPDATED)

• Persistent storage using SQL (SQLite + SQLAlchemy)
• User-specific report tracking
• AI explanations stored with each record
• Downloadable analysis reports

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🔐 Authentication & Access Control

• JWT-based authentication
• Role-based system:

Role	Access
Admin	Full system control
Staff	Analysis + monitoring
Student	Limited access

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🧠 AI Advisor

• Answers water-related queries
• Provides contextual explanations
• Supports learning and decision-making

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🖥 Desktop Application

• Runs as standalone software
• Built using:
  • PyWebView
  • PyInstaller

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🏗 System Architecture

Frontend (Streamlit UI)
↓
FastAPI Backend (API Layer)
↓
Hybrid AI Engine
├── ML Model (Prediction)
├── Rule Engine (Explainability)
└── Risk Engine
↓
Database (SQLite)
↓
Reports & Visualization

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🛠 Tech Stack

Backend

• Python
• FastAPI
• SQLAlchemy
• Uvicorn

Frontend

• Streamlit
• Plotly
• Pandas

AI / ML

• Scikit-learn (Random Forest)
• Hybrid AI Logic

Security

• JWT Authentication
• Role-Based Access Control

Database

• SQLite

Desktop

• PyWebView
• PyInstaller

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📂 Project Structure

backend/
├── main.py
├── ai_engine.py
├── ai_advisor.py
├── auth.py
├── models.py
├── database.py
├── report_generator.py
├── train_model.py
├── model.pkl

frontend/
├── pages/
│   ├── _Dashboard.py
│   ├── _AI_Analysis.py
│   ├── _Reports.py
│   ├── _Devices.py
│   ├── _About_System.py
│   └── _User_Management.py
│
├── components/

desktop_launcher.py
water_quality.db

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⚙️ Installation

git clone https://github.com/your-username/aquaos-water-monitoring.git
cd aquaos-water-monitoring

pip install -r requirements.txt

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▶️ Run the System

Full Desktop App

python desktop_launcher.py

OR Run Separately

Backend:

uvicorn backend.main:app --reload

Frontend:

streamlit run frontend/pages/_Dashboard.py

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📌 Example Input

Parameter	Value
pH	7.2
Turbidity	3
TDS	350
Dissolved Oxygen	6

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🔥 Key Highlights

• Hybrid AI (ML + Explainability)
• Real-time decision system
• Scenario simulation engine
• User-specific data tracking
• Structured reporting system

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🔮 Future Scope

• IoT sensor integration
• Real-time streaming pipelines
• Cloud deployment
• Advanced anomaly detection
• Continuous model retraining

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📜 License

For educational and research purposes.