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. 🧠 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. 📊 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 Sample Agentic AI Output ======================================================================= ### [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. ======================================================================= 📚 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.