Multi-energy system smart tool for ecological water body restoration using an AI-based decision-making framework

Abstract

Abstract Ecological regeneration will reduce air pollution, reverse forest clearing and wilderness, minimize loss of biodiversity, improve urban ecosystems, and probably enhance the livelihoods and ties between mankind and nature. Ecological regeneration risk factors include frequent changes in natural environments, imperfect interpretation of natural systems by humans, and a lack of knowledge on past successes and shortcomings. The Internet of Things (IoT) uses in environmental monitoring are varied: environmental protection, extreme weather monitoring, water safety, conservation of endangered species, and commercial farming. In this paper, artificial intelligence-based environmental decision restoration framework (AI-EDRF) has been proposed to strengthen the continuously evolving natural systems; people are deficient about natural systems and the insufficient knowledge about past achievements and failures. The biological terrestrial collective analysis is introduced to improve natural systems is rapidly evolving, and people are inadequately aware of natural systems. Stochastic water quality analysis is integrated with AI-EDRF to boost past achievements and failures. IoT-enabled smart energy management is an effective approach to provide cost-effective, efficient energy distribution and technologies are used in connection with sustainable, renewable energy sources. The computational analysis is executed based on accuracy, performance ratio, reaction time, and data deployment to verify the developed framework's reliability.