International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The digital transformation of environmental monitoring represents a critical frontier in computational sustainability, with profound implications for public health, agricultural efficiency, and smart city infrastructure. Traditional methodologies for water quality assessment frequently encounter a "logistical-latency" bottleneck, where high-fidelity laboratory analysis requires significant time and specialized personnel, rendering real-time safety verification for rural and remote areas impractical. Furthermore, conventional manual testing is often compromised by human error, sample degradation during transport, and a lack of contextual data interpretation. This research introduces an Integrated Multi-Modal Prediction Framework that unifies Machine Learning classification, domain-specific rule engines, and Generative AI assistance into a singular, high-performance ecosystem. The system bypasses the limitations of standard "black-box" predictors by adopting a hybrid decision-making approach. By utilizing a Random Forest Classifier alongside a deterministic rule set, the framework achieves high-fidelity assessment of water potability based on 9 critical physicochemical parameters. This transformation of raw chemical data into actionable safety verdicts allows for instant execution without the necessity for expensive laboratory infrastructure. To resolve the challenge of interpreting complex chemical interactions, the system implements a Large Language Model (LLM) interface via the Google Gemini API. This architecture is specifically engineered to model the context of user queries, enabling the system to distinguish between "safe for agriculture" and "safe for drinking" scenarios. A defining innovation of this project is its decoupled modular architecture, which facilitates the independent execution of prediction, mapping, and advisory modules through a shared, optimized backend stream. The integration of geospatial visualization and automated history logging further ensures the utility of the platform for long-term monitoring. Empirical testing confirms that the proposed system delivers a robust, low-latency solution capable of operating on standard web servers. By democratizing access to advanced water safety analysis, this work contributes to the development of inclusive technology that bridges the gap between complex environmental data and public understanding.

Downloads: | DOI: 10.17148/IJARCCE.2026.15177