Systematic reviews (SR) are crucial in synthesizing and analyzing existing scientific literature to inform evidence-based decision-making. However, traditional SR methods often have limitations, including a lack of automation and decision support, resulting in time-consuming and error-prone reviews. To address these limitations and drive the field forward, we harness the power of the revolutionary language model, ChatGPT, which has demonstrated remarkable capabilities in various scientific writing tasks. By utilizing ChatGPT’s natural language processing abilities, our objective is to automate and streamline the steps involved in traditional SR, explicitly focusing on literature search, screening, data extraction, and content analysis. Therefore, our methodology comprises four modules: (1) Preparation of Boolean research terms and article collection, (2) Abstract screening and articles categorization, (3) Full-text filtering and information extraction, and (4) Content analysis to identify trends, challenges, gaps, and proposed solutions. Throughout each step, our focus has been on providing quantitative analyses to strengthen the robustness of the review process. To illustrate the practical application of our method, we have chosen the topic of IoT applications in water and wastewater management and quality monitoring due to its critical importance and the dearth of comprehensive reviews in this field. The findings demonstrate the potential of ChatGPT in bridging the gap between traditional SR methods and AI language models, resulting in enhanced efficiency and reliability of SR processes. Notably, ChatGPT exhibits exceptional performance in filtering and categorizing relevant articles, leading to significant time and effort savings. Our quantitative assessment reveals the following: (1) the overall accuracy of ChatGPT for article discarding and classification is 88%, and (2) the F-1 scores of ChatGPT for article discarding and classification are 91% and 88%, respectively, compared to expert assessments. However, we identify limitations in its suitability for article extraction. Overall, this research contributes valuable insights to the field of SR, empowering researchers to conduct more comprehensive and reliable reviews while advancing knowledge and decision-making across various domains.

This study aims to model the performance of sewage networks under diverse blockage situations in terms of overflow occurrence using internet-of-things-based technologies in Hong Kong. To this end, a multi-stage methodological approach is employed, starting from collecting required data using smart sensors, utilizing novel data mining techniques, and using a case study simulation. From the results obtained, the following conclusions are drawn: (1) several sites under investigation are imbued with partial blockages, (2) the overall performance of the sewer network has a nonlinear relationship with the blockages in terms of the remaining time to overflow, (3) in cases of complete blockages, the sewer only takes few minutes to reach the manhole cover level that causes the system to experience overflow, and (4) cleaning work significantly improve the performance of the sewage network by 86%. The outcomes of this study provide a solid foundation for the concerned environmental engineers and decision-makers towards reducing the magnitude of sewer overflow and improving different aspects of our environment.

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