A framework for strategic planning adaptation in smart cities through Recurrent Neural Networks

Marwa S. Mahdi Hussin; Mohammed Brayyich; Mustafa Al-Tahee; Tamarah A. Diame; Sajad Ali Zearah; Marwan Qaid Mohammed; Salem Saleh Bafjais

doi:10.54216/JISIoT.090205

A framework for strategic planning adaptation in smart cities through Recurrent Neural Networks

Marwa S. Mahdi Hussin^*, Mohammed Brayyich, Mustafa Al-Tahee, Tamarah A. Diame, Sajad Ali Zearah, Marwan Qaid Mohammed, Salem Saleh Bafjais

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	65-77
Number of pages	13
Journal	Journal of Intelligent Systems and Internet of Things
Volume	9
Issue number	2
Early online date	30 Sept 2023
DOIs	https://doi.org/10.54216/JISIoT.090205
Publication status	Published - 30 Sept 2023
Externally published	Yes

Keywords

Strategic planning adaption framework
Recurrent Neural Network
Sewage
Wastewater management and sustainable planning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.54216/JISIoT.090205

Cite this

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title = "A framework for strategic planning adaptation in smart cities through Recurrent Neural Networks",

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.",

author = "\{Mahdi Hussin\}, \{Marwa S.\} and Mohammed Brayyich and Mustafa Al-Tahee and Diame, \{Tamarah A.\} and Zearah, \{Sajad Ali\} and Mohammed, \{Marwan Qaid\} and Bafjais, \{Salem Saleh\}",

note = "{\textcopyright} 2023 the Authors, published by American Scientific Publishing Group (ASPG) Data availability statement: Not present.",

year = "2023",

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doi = "10.54216/JISIoT.090205",

language = "English",

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T1 - A framework for strategic planning adaptation in smart cities through Recurrent Neural Networks

AU - Mahdi Hussin, Marwa S.

AU - Brayyich, Mohammed

AU - Al-Tahee, Mustafa

AU - Diame, Tamarah A.

AU - Zearah, Sajad Ali

AU - Mohammed, Marwan Qaid

AU - Bafjais, Salem Saleh

PY - 2023/9/30

Y1 - 2023/9/30

N2 - 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.

AB - 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.

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DO - 10.54216/JISIoT.090205

M3 - Article

AN - SCOPUS:85173889472

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JF - Journal of Intelligent Systems and Internet of Things

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ER -

A framework for strategic planning adaptation in smart cities through Recurrent Neural Networks

Abstract

Keywords

UN SDGs

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