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River Sense | Sistem Pendeteksi Kualitas Air Sungai Berbasis AIoT

RIVER SENSE

River Water Quality Detection System Based on AIoT​

📖 Project Overview

This project develops a real-time River Water Quality Monitoring System based on AIoT. It uses pH, TDS, and turbidity sensors connected to an ESP32 microcontroller, with data sent via MQTT to AWS Cloud for live monitoring and analysis.
The system applies decision tree machine learning optimized with TinyML for both local and cloud processing. It is designed with fault tolerance to maintain reliability and features a user-friendly web dashboard for real-time alerts and data visualization.

🧑‍🤝‍🧑 Meet Our Team

Regas Ryandhi Aditama

Project Manager

Ahmad Azhar Kaffi

Hardware & Embedded System Engineer

Andika Rama Aprianto

Machine Learning & Dashboard Developer

Muhammad Nabil Hafidh

Cloud & Backend Developer

Daffa Raihan Dwi Ari Putra

Documentation

🌊 Problem Statement

Monitoring river water quality is often conducted manually, which is :

  • ⏱️ Time-consuming
  • 📊 Lacks real-time data
  • ⚠️ Delays pollution detection

This creates risks of late responses to water contamination.

🎯 Goals

  • 🚀 Develop an AIoT-based system for real-time river water quality monitoring.

  • ⚙️ Automate water quality detection and classification.

  • 🖥️ Provide instant alerts and continuous data visualization via a web dashboard.

💡 Solution Statements

  • 🔗 Integrate pH, TDS, and turbidity sensors with an ESP32 microcontroller.
  • 🌐 Transmit sensor data to AWS Cloud via MQTT protocol for real-time analysis.
  • 🧠 Apply Machine Learning decision tree models optimized with TinyML for classification.
  • 🛡️ Implement fault-tolerant mechanisms to ensure system reliability.
  • 📊 Visualize results on a responsive web dashboard with live monitoring and notifications.

⚙️ Prerequisites – Component Preparation

🔩 Hardware : 

  • 📟 ESP32 Microcontroller
  • 🌡️ pH Sensor
  • 💧 TDS Sensor
  • 🌀 Turbidity Sensor
  • 🔌 Supporting modules (wiring, power supply)

💻 Software/Platform : 

  • 🐍 Python
  • 🧮 Scikit-Learn
  • ☁️ AWS IoT Core
  • 🖧 Node-RED
  • 🪶 TinyML Library
  • 🔗 MQTT Protocol

🗂️ Block Diagram & Schematic

🗺️  Block Diagram

🔌  Wiring Schematic

🎬 Video Demo

✅ Conclusion

This project successfully delivers a :

  • 🛠️ Reliable, real-time river water monitoring system using AIoT and fault-tolerant architecture.
  • 🧠 Provides accurate classification and instant alerts.
  • 🌱 Can significantly improve early pollution detection and environmental management.

📚 Documentation – Tools & Capstone Exhibition

📝   Tools Documentation

  • 📷 Detailed photos of the hardware assembly 
  • 🗂️  Wiring setup .
  • 🧰 Sensor calibration process

🎤 Capstone Exhibition – Team Booth Presentation

  • 🤝 Direct interaction and explanation to visitors and examiners.
  • 📸   Team group photo at the Capstone Project booth.

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