Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2081 2019 2019 Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Shaid Sheel Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq Sarmad Jaafar Naser Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq Hussein Alaa Diame Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq Noor Baqir Hassan Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq Naseer Ali Hussien Department of Applied Data Science, Noroff University College, Kristiansand, Norway; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates ;Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon Seifedine Kadry 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 2023 206 221 10.54216/JISIoT.090215 https://www.americaspg.com/articleinfo/18/show/2081