Data-driven peer-to-peer blockchain framework for water consumption management

It is widely believed that effective water resource management can optimize the scheduling of water supply plans, which is essential for sustainable development. The core of management is to accurately predict future water consumption. However, existing studies generally face two challenges. First, a reliable bottom platform for the support of online data integration is absent. In addition, multisource factors that primarily affect water consumption are neglected when modeling. To solve the above problems, this paper proposes a data-driven peer-to-peer blockchain framework to predict water consumption. Fundamentally, it utilizes a blockchain system with a peer-to-peer network to serve as the decision support platform hardware. On this basis, an intelligent prediction algorithm that combines the grey model and long short-term memory model is developed to drive the hardware infrastructure. After that, the performance of the proposed method is evaluated by carrying out experiments on a real-world dataset, and three typical approaches are selected for comparison. The experimental results show that the proposal exceeds general prediction models by approximately 8%.