Explainable AI-based Water-Quality Monitoring

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Vansh Bansal

1 Introduction

A key challenge to achieving transparent monitoring of water quality is the risk of data manipulation by local communities, bureaucrats or politicians to create a distorted picture. A solution becomes effective only when the water quality measurements are gathered from the grassroots and the community themselves become accountable for the data. We propose an AI based solution for low-cost, reliable monitoring of water quality for the rural communities of India.

2 Problem Statement

There are two separate problems:

• Identifying the most critical factors contributing to water quality. These factors could be different contaminants, alkalinity, chlorine, turbidity, pH, ORP, E.coli, hardness (Ca, Mg), ammonia, Fe, environment/weather parameters, etc., which can be tracked via sensors.

• Monitoring water quality parameters in labs would be expensive and hence infeasible to deploy on-ground. So, we will map the laboratory results onto the less accurate data gathered from inexpensive sensors to get high precision values in the future, using the low-cost sensors and paper dips, even when the lab data is not accessible.

3 Solution Architecture

Due to limited data and impreciseness of data from low cost sensors, we will start with unsupervised and semi-supervised approaches such as human-in-the-loop and active learning.

To take care of the variability due to sensors characteristics and geographical locations, we shall use model adaptation and transfer learning approaches. These models may be used to group high-risk areas and identify specific factors that make these areas vulnerable to poor water quality. In our preliminary literature review, we could only find a limited number of research papers that have applied unsupervised machine learning techniques to assess environmental standards and almost none related to the assessment of water quality in particular.

• We can use Supervised Learning to predict water quality parameters that currently require water sample analysis at a Lab, by using field measurements of water quality using low- cost sensors.

• We can use Unsupervised Learning approaches to find the specific factors contributing the most in determining Water Quality.

• A Spatio-temporal forecasting model can be developed which accurately identifies the most at-risk areas among the communities being monitored as part of the project and helps devise appropriate interventions to tackle the specific challenges.

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4 Impact

The objective is to implement a community-driven, continuous, low-cost monitoring of water quality that would help improve awareness of water status and efficient access to safe drinking water. NGO Workers should be able to focus their efforts on regularly monitoring the water quality and in return, they should be informed about the details about the assessment results, in a simple explainable format. Our approach would achieve greater transparency, leading to accountability in governance. The work is foundational towards a comprehensive water quality assessment dataset from each of the selected communities.

Knowledge Sharing Framework: The results of the project must be easily understood by the experts in the field of water quality assessment, as well as by the community members who are actually impacted by the harmful water quality and its health implications. This will form the ‘interpretable’ part of the project. Explainable AI for identifying the most important factors, and developing a knowledge-sharing platform for sharing information with different categories of people (local community members, policymakers, water quality researchers, AI experts etc.)

5 Resources and Budget

The budget and resources required are summarised in the following table:

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6 Evaluation and Timeline

The following table summarises the tentative timeline of the project.

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