Multi-Modal Edge-AI Framework for Real-Time Industrial Water Quality Monitoring and Automated EPA Compliance

An advanced, edge-deployable artificial intelligence architecture integrating predictive ML, IoT sensor fusion, and Agentic Large Language Models (LLMs) to automate federal environmental compliance.

📖 Overview

Industrial effluent management currently relies on reactive, manual, and laboratory-delayed sampling, frequently resulting in undetected EPA violations and environmental damage. This repository implements a Multi-Modal Edge-AI Framework that shifts environmental oversight from passive observation to proactive intelligence.

By bridging high-frequency IoT sensor telemetry with a stacked machine learning architecture and an Agentic AI reasoning layer, this framework autonomously forecasts sensor drift, detects regulatory breaches, isolates heavy metal speciation, and generates human-readable, legally grounded NetDMR compliance reports.

SEO Keywords: Real-time water quality monitoring, Edge AI IoT, EPA compliance automation, LangChain Agentic AI, LSTM time-series forecasting, environmental engineering, wastewater management, heavy metal speciation, machine learning in sustainability.

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🧠 System Architecture

The project relies on a deeply integrated 3+1 Layer architecture designed for Edge deployment (e.g., NVIDIA Jetson Nano):

1. Layer 1: Predictive Forecasting (PyTorch LSTM)

• Mechanism: A 2-layer stacked Long Short-Term Memory (LSTM) network (128 units).
• Function: Processes rolling windows of Z-score normalized sequential data to forecast critical parameters (pH, DO, TDS) 30 to 90 minutes into the future.
• Value: Enables proactive intervention before a statutory limit is breached.

2. Layer 2: Regulatory Classification (Random Forest)

• Mechanism: Tree-based ensemble learning utilizing engineered derivatives (DO/TDS ratios, rolling standard deviations, rate of change).
• Function: Classifies the current multi-dimensional sensor vector as COMPLIANT or NON-COMPLIANT (VIOLATION) based on US EPA guidelines.
• Performance: High F1-score with prioritized recall to ensure zero false negatives for toxic outfalls.

3. Layer 3: Chemical Speciation (KMeans + Rule-Based Redox)

• Mechanism: Unsupervised KMeans clustering (k=4) fused with deterministic geochemical transition rules.
• Function: Infers the biochemical state of heavy metals (e.g., Arsenate As(V) vs. Arsenite As(III)) based on pH and Oxidation-Reduction Potential (ORP).
• Value: Different chemical species are functionally different toxins and require vastly different physical remediation therapies.

4. Agentic AI Compliance Layer (LangChain)

• Mechanism: An LLM-driven deterministic agent utilizing LCEL (LangChain Expression Language).
• Function: Synthesizes outputs from Layers 1, 2, and 3, applies statutory mapping (40 CFR §141.62), and autonomously drafts NetDMR-ready reporting alongside step-by-step remediation instructions for ground operators.

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📊 Industry-Standard Data Simulation

To evaluate the framework under realistic conditions, this repository includes a highly specialized synthetic data generator that mimics actual industrial IoT outfall probes.

• Sampling Frequency: 0.1 Hz (10-second intervals) representing continuous streaming.
• Signal Drift: Data features authentic autocorrelation (using sinusoidal/cosine functions) simulating gradual sensor drift and industrial flow variations.
• Realistic Noise: Injection of Gaussian noise mimics the hardware imperfections of submerged probes.
• Imbalanced Constraints: Mimicking real-world scenarios, the dataset simulates an ~80% to 90% compliance baseline, with a late-stage gradual temporal drift into an active violation state (e.g., Arsenic creeping above 0.01 mg/L).
• Target Parameters:
  • pH: Safe range 6.5 - 8.5
  • Total Dissolved Solids (TDS): Limit 500 mg/L
  • Dissolved Oxygen (DO): Minimum 6.0 mg/L
  • Arsenic: Maximum Contaminant Level (MCL) 0.01 mg/L

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🚀 Installation & Usage

Prerequisites

• Python 3.9+
• Jupyter Notebook
• PyTorch, Scikit-Learn, Pandas, LangChain-Core

Setup

git clone https://github.com/senthilv83/WaterQuality_IoT_AgenticAI.git
cd WaterQuality_IoT_AgenticAI
pip install -r requirements.txt

Execution

Run the integrated Jupyter Notebook to view the entire pipeline, from data generation to Agentic reporting:

jupyter notebook WaterQuality_EdgeAI_Framework.ipynb

Sample Agentic AI Output

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### [AGENTIC ADVISOR] INTEGRATED EPA REPORT ###
**Timestamp**: 2026-04-08 12:48:20

**1. COMPLIANCE IDENTIFICATION & EXPLANATION**
- Legal Framework: 40 CFR §141.62 (National Primary Drinking Water Regulations)
- EPA Compliance Status: NON-COMPLIANT (ACTIVE VIOLATION)
- Compliance Explanation: Arsenic levels (0.0107 mg/L) have breached the 0.01 mg/L EPA Maximum Contaminant Level (MCL). This is an actionable violation requiring immediate remediation.

**2. ML TELEMETRY (LAYERS 1-3)**
- [Layer 1: LSTM] 30-Min Forecast -> pH: 6.98, DO: 6.60, TDS: 530.76
- [Layer 2: RF] Current Readings  -> pH: 6.92, TDS: 528.7, Arsenic: 0.0107 (Conf: 100.0%)
- [Layer 3: KMeans] Metal Species -> Arsenite (As III) (ORP: 126.8mV, Temp: 19.7C)

**3. HUMAN-READABLE OPERATOR INSTRUCTIONS**
1. HALT standard effluent discharge immediately.
2. INITIATE iron salt co-precipitation sequence (Targeted specifically for Arsenite treatment).
3. INCREASE mechanical aeration to assist oxidation of As(III) to As(V).
4. LOG the violation and DRAFT a NetDMR exception report for the EPA.
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📚 IEEE Citation

If you utilize this framework or architecture in your research, please cite the original foundational paper:

Senthilkumar Vijayakumar, Shaunak Pai Kane, Filious Louis, Sidharth E, Surendran Selvaraj, Kuna Vaiappuri, Vadiveloo Veeramalai, "A Multi-Modal Edge-AI Framework for Real-Time Industrial Water Quality Monitoring and Automated EPA Compliance Using IoT and Regulatory LLMs," Unpublished/Pending.

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