A Framework for Strategic Planning Adaptation in Smart Cities through Recurrent Neural Networks

In the Smart city environment, sustainable sewage and wastewater management planning plays a crucial role in industry development. Wastewater management is a serious issue with inadequate treatment, which reduces the smart city efficiency. Therefore, this research work concentrates on creating the Strategic Planning Adaption framework (SP-AF) using the Recurrent Neural Networks (RNN). This framework intends to manage the sewage and wastewater in smart cities. The sewage-related information is continuously collected by a recurrent network that identifies and tracks the wastewater and sewage in the smart city. The SP-AF framework analyses sustainable planning and managing wastewater by understanding the waste origin. In addition, the framework has been generated by understanding the wastewater knowledge, and the required actions are carried out. Then the effectiveness of the wastewater management system efficiency is compared with the existing approaches.