Temporal deep learning enhanced remote sensing for environmental degradation monitoring with blockchain in dense mining regions of underdeveloping countries

Environmental deterioration can cause major issues like air pollution, water scarcity, land degradation, and socioeconomic disruptions in heavily mined places like Sindh, Pakistan's Thar coalfields. To overcome these obstacles, a novel strategy using contemporary monitoring and prediction technology is required. This study presents a novel framework for tracking and reducing the environmental effects of mining in poor nations by combining data from blockchain technology, Temporal Convolutional Networks (TCNs), and remote sensing. To ensure stakeholder confidence and accountability, the proposed architecture recodes environmental data using Blockchain Distributed Ledger Technology (BDLT) in a secure, transparent, immutable, and secure manner. The primary potential is to periodically monitor key metrics like vegetation loss, water depletion, and air quality using the Remote Sensing (RS) approach. However, by examining temporal data, TCNs are able to predict trends in environmental degradation and take pre-emptive steps to prevent damage. With a prediction performance of up to 97.3%, metrics such as the Normalised Difference Vegetation Index (NDVI), Air Quality Index (AQI), and water table depth are assessed with great accuracy. In addition to offering politicians and regulators useful information, the proposed architecture uses chaincode to guarantee adherence to environmental regulations. Furthermore, this paper offers a scalable and adaptable solution to environmental limitations in resource-rich places. It supports international sustainability objectives and sets the standard for more ethical mining methods in underdeveloping countries.