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

Marwa S. Mahdi Hussin^1,*, Mohammed Brayyich², Mustafa Al-Tahee³, Tamarah A. Diame⁴, Sajad Ali Zearah⁵, Marwan Qaid Mohammed ⁶, Salem Saleh Bafjaish ⁷

¹Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq

²Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq

³Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq

⁴Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq

⁵ Scientific Research Center, Al-ayen University, Thi-Qar, Iraq

⁶Fraunhofer IVI,85051, Ingolstadt, Germany

⁷College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

Emails: marwa.saad@turath.edu.iq; m.r.brayyich@nust.edu.iq; Mustafa.Al-Tahee@uoalfarahidi.edu.iq; Tamarah.Alaa@Kunoozu.Edu.Iq; sajad@alayen.edu.iq; marwan.qaid.mohammed@ivi.fraunhofer.de; Salem.Bafjaish@gmail.com

^*Corresponding Author: marwa.saad@turath.edu.iq

Abstract

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.

Received: February 19, 2023 Revised: May 13, 2023 Accepted: September 02, 2023

Keywords: Strategic planning Adaption Framework; Recurrent Neural Network; Sewage; Wastewater management and sustainable planning