Krishna River Water Quality Analysis using AI

A research initiative by DataMindsLab focused on applying Artificial Intelligence, Attention-Based Neural Networks, and Statistical Forecasting Models for Water Quality Assessment and Prediction.

Project Overview

This project aims to assess and forecast the water quality of the Krishna River using advanced Artificial Intelligence methodologies. The study focuses on Water Quality Index (WQI) computation, time-series analysis, statistical forecasting, and deep learning models capable of identifying temporal environmental patterns.

Research Objectives

Water Quality Assessment

Compute and analyze Water Quality Index across monitoring stations.

Trend Analysis

Identify long-term environmental changes using statistical methods.

Forecasting

Predict future water quality conditions using AI models.

Decision Support

Generate actionable environmental insights for stakeholders.

Methodology Roadmap

Phase 1: Data Collection & Validation

Gather historical water quality measurements and validate data quality.

Phase 2: Exploratory Data Analysis

Trend analysis, seasonal analysis, correlation studies.

Phase 3: Time Series Diagnostics

ADF Test, ACF, PACF, Stationarity Assessment.

Phase 4: Forecasting Models

ARIMA, SARIMA, Attention-Based Neural Networks.

Phase 5: Publication & Dissemination

Journal manuscript preparation and submission.

Models Being Explored

ARIMA SARIMA LSTM Attention-LSTM Transformer Models Hybrid Forecasting

Current Progress

Data Collection & Validation

Exploratory Data Analysis

Forecasting Model Development

Research Collaboration Opportunity

We are inviting a maximum of five contributors interested in AI, Machine Learning, Environmental Analytics, and Time-Series Forecasting. Selected contributors may be considered for co-authorship based on meaningful technical and research contributions.