aspg-18-2081-1792025637 20261015005357 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2023 9 2 Shaid Sheel Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Sarmad Jaafar Naser Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq Hussein Alaa Diame Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq Noor Baqir Hassan Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq Naseer Ali Hussien Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq Seifedine Kadry Department of Applied Data Science, Noroff University College, Kristiansand, Norway; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates ;Depar Wastewater treatment procedures (WWTP) rely heavily on accurate forecasting of treatment results to keep oxygenation levels under control. Conventional biochemical mechanism-driven approaches provide poor results, mainly due to complicated and redundant system factors. As sewage treatment operations expand fast, automated operational solutions are needed to achieve this goal. In the research, data mining was used to model the WWTP to predict the outcomes based on input circumstances and the amount of oxygenation provided to the system. Combined Sustainability Research for Wastewater Treatment procedures (CSR-WWTP) is proposed in this research. Data-driven approaches to modeling WWTP have already been developed but do not consider long-term treatment procedures and structure features. Forecasting and management for the WWTP are described in this article using a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN). The first stage utilizes the CNN structure to dynamically learn and encrypt the local features of each WWTP timestamp in the first phase. The RNN model is applied to the WWTP to express global sequence characteristics using local feature encryption. For this purpose, it conducts a huge number of tests to assess the performance and accuracy of the proposed forecasting framework. 2023 206 221 2081 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.090215 https://americaspg.com/journal/18/article/2081 https://americaspg.com/storage/41694885767.pdf