RainDNA: Autonomous Atmospheric Acidification Analysis

Reenu Elizabeth Manu; Indu Parvathy

doi:10.36948/ijfmr.2026.v08i02.74776

RainDNA: Autonomous Atmospheric Acidification Analysis

Author(s)	Ms. Reenu Elizabeth Manu, Ms. Indu Parvathy
Country	India
Abstract	Historically, acid rain has been monitored independently of all other forms of acid precipitation by primary reliance on mathematical numerical models, resulting in delays for accurate forecasts that determine the risk of acid rain deposition and when water remediation needs to occur after the event has already occurred. The purpose of this research is to create the necessary tools to fill these voids so that an Artificial Intelligence (AI) Framework, labelled as RainDNA, will provide greater accuracy in the short-term and long-term forecasting of the Acid Rain Risk Index (ARRI) and develop standardised water purification protocols confirmed through the procurement of multiple weather-related data sources. To accomplish this task, a fused data set was constructed that included cloud imaging from satellites, real-time atmospheric chemistry data and weather API data. A Hybrid Machine Learning (ML) Technology Architecture was subsequently created to address the extensive spatiotemporal forecasting challenges associated with the generation of the ARRI. While the machine learning algorithms trained their respective models using visual cloud feature extraction by MobileNetV2, Principal Component Analysis (PCA) was utilised to compress the extracted visual features to create a fused data set that can then be used with lagged temporal weather data to generate continuous ARRI forecasts and recursive ARRI prediction for the next ten-days. Finally, an offline generative language model (GLM) was developed to produce accurate neutralization protocols based on simulated chemical analyses of generated water quality metrics. Using multi-modal predictive models has proven to be advantageous over traditional baseline methods, which use only past numbers. The RainDNA model discussed here has demonstrated strong results in continuous risk assessment, with a Root Mean Square Error (RMSE) of 0.1435 and a Coefficient of Determination (R²) of 0.5064. The overall framework is designed to be reliable and fault-tolerant, with a geospatial component focused on regions like Kerala, thereby demonstrating promise for other IoT-based and environmental research applications and further exploring the data collected from atmospheric systems.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-16
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.74776

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

RainDNA: Autonomous Atmospheric Acidification Analysis

Share this