Water quality information dissemination at real-time in South Africa using language modelling

TL;DR

This paper introduces a conversational AI model that provides real-time water quality information to users in South Africa, utilizing natural language understanding and a chatbot interface for localized information dissemination.

Contribution

It develops a neural embedding-based chatbot system tailored for water quality info dissemination in resource-limited settings, integrating multiple data sources.

Findings

Effective in local water quality information search

Demonstrated utility for real-time dissemination

Applicable to resource-constrained environments

Abstract

We present a conversational model to apprise users with limited access to computational resources about water quality and real-time accessibility for a given location. We used natural language understanding through neural embedding driven approaches. This was integrated with a chatbot interface to accept user queries and decide on action output based on entity recognition from such input query and online information from standard databases and governmental and non-governmental resources. We present results of attempts made for some South African use cases, and demonstrate utility for information search and dissemination at a local level.