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Blokchain-Based Trust and AI-Driven Water Quality Prediction in River Systems

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Abstract

Water is essential for sustaining life. Across the world, ensuring access to safe and clean water is a major priority. The primary reason for water contamination is industrial activities generating significant amounts of pollutants, including heavy metals, chemicals, and organic compounds. Other reasons include using fertilizers, pesticides, and herbicides in agriculture, inadequate sewage treatment infrastructure, and improper wastewater disposal from households and commercial establishments. The involvement of the general public can minimize these issues. A secured blockchain-based model can be a solution; the oil spill of the Nigeria River is an example. Various water quality metrics like pH, turbidity, temperature, electrical conductivity, and oxidation-reduction potential are recorded by researchers using the Internet of Things (IoT) to measure water quality. However, using a secured platform using blockchain still needs to be included. The data recorded by researchers is not stored on a secured platform nor shared by the general public through a secured platform. Our work developed a secured blockchain platform to record this data and reward the volunteers who contribute to cleaning the water bodies. This is done by making a blockchain model using Ethereum. The model is evaluated for performance using gas cost, transaction cost and execution cost to check the feasibility. In addition, the data set is used to build a Machine learning model using IBM Watson to predict water potability. The integration of blockchain and Machine learning provides security, trust and prediction.

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Funding

This research is supported by a grant from the Uttarakhand State Council for Science and Technology under the project title ’Development of blockchain platform for awareness,verification,validation,record keeping and rewarding for cleaning of holy river Ganga.’ We sincerely appreciate their generous funding contribution.

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  1. Computer Science and Engineering, Gurukul Kangri Deemed to be University, Haridwar, Uttrakhand, 249404, India

    Anjali Arora & Mayank Aggarwal

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  1. Anjali Arora
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Correspondence to Mayank Aggarwal.

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Arora, A., Aggarwal, M. Blokchain-Based Trust and AI-Driven Water Quality Prediction in River Systems. SN COMPUT. SCI. 5, 954 (2024). https://doi.org/10.1007/s42979-024-03315-0

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