Enhancing environmental sustainability with federated LSTM models for AI-driven optimization

Abstract

Combining artificial intelligence (AI) and optimization techniques in the quest for environmental sustainability has emerged as a promising strategy. This paper explores the potential of a Federated Long Short-Term Memory (Fed LSTM) model in addressing environmental challenges through decentralized learning and efficient intelligence. Fed LSTM, a model tailored for government curricula, offers a novel method for analyzing and optimizing disaggregated environmental data across multiple sites while preserving data privacy. Its applications in environmental sustainability span various domains. Firstly, energy policy enables the creation of accurate local energy consumption forecasting models by integrating data from diverse sources such as buildings, infrastructure, and renewable energy installations. Secondly, in environmental monitoring, Fed LSTM facilitates the quantification of key parameters like biodiversity levels. Thirdly, resource efficiency optimizes the use of resources in agriculture, water management, and waste management, leading to more efficient resource management and reduced environmental impact. The benefits of Fed LSTM have the potential to significantly enhance environmental sustainability by providing adaptive solutions and new options for managing complex environmental challenges through decentralized and privacy-protected approaches. This paper advocates for further research and effective implementation of Fed LSTM in environmental sustainability initiatives to realize its full potential in promoting positive environmental development. With an accuracy of 99.2 %, surpassing existing methods, this approach is implemented using Python.

Keywords

• Federated LSTM
• Environmental sustainability
• Decentralized learning
• Intelligent optimization
• Data privacy