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 comprehensive review explores IoT innovations in water, wastewater management, and water quality monitoring, emphasizing the transformative potential of these technologies. Combining sociometric and systematic review (SR) techniques, the study analyzes scientometric trends and co-occurrence networks linked to review topics. Research primarily centers on these aspects, averaging 15 articles annually since 2017, peaking at 24 in 2021. The SR unveils the widespread use of multiple sensors in monitoring, particularly water level, flow, and pH sensors. Common wireless technologies are emphasized for their role in advancing real-time monitoring. Innovative protocols such as Sigfox and Zigbee enhance sensor-IoT connectivity, improving communication in infrastructure management. Common challenges hindering system efficiency and data flow include sensor accuracy, energy optimization, communication reliability, interdisciplinary collaboration, and sensor coverage. Addressing these gaps is crucial for advancing IoT-driven water systems and enhancing decision-making. This study guides IoT practitioners in integrating automation and sustainability in water and wastewater management.

Effective monitoring and prediction systems for sewer overflow are essential for safeguarding public health and the environment. Flow sensors have emerged as valuable tools for understanding and measuring the hydraulic performance of sewer networks, enabling the detection of blockages and overflow events. However, previous research has predominantly focused on large-diameter sewer networks, leaving a gap in understanding the applicability and performance of flow sensors in small and medium-sized systems. Addressing this research gap and motivated by the need to improve the monitoring of small and medium-sized sewer networks, this study comprehensively assesses the performance of flow sensors in such networks, with a focus on detecting blockages and overflow. The study evaluates the performance of flow sensors in 12 locations within the Hong Kong sewer network and identifies challenges affecting accuracy. The findings reveal noteworthy shortcomings when solely relying on flow sensors, including inconsistent and unreliable observations. Notably, the correlation coefficient between the level and flow sensors was 0.36, and the average relative error in flow rate measurement was a substantial 72.14% compared to Manning’s equation. An in-depth analysis reveals key factors hindering flow sensors’ efficiency, such as inconsistent flow directions and pipe size variations. To overcome these limitations, the study introduces a new approach based on real-time measurement of vertical sewage velocities inside manholes. By incorporating level sensors and considering specific network characteristics, this alternative methodology provides a promising solution for detecting operational issues and improving the reliability of overflow monitoring systems.

There are several hotspots of dust production in the central Sahara, the Bodélé Depression (BD) in northern Chad is considered the largest source of aerosol dust worldwide, with the fastest Barchan dunes that migrate southwesterly. Less is known about the complex patterns of dune movement in the BD, especially on a short time scale. Time-series inversion of optical image cross-correlation (TSI-OICC) proved to be a valuable method for monitoring historical movements with low uncertainties, high spatial coverage, and dense temporal coverage. We leveraged ∼8 years of Landsat-8 and ∼6 years of Sentinel-2 data to capture the dune migration patterns at BD. We used TSI-OICC, creating four independent networks of offset maps from Landsat-8 and Sentinel-2 images, and forming three networks by fusing data from the two sensors. We depended on the multi spatial coherence estimated from Sentinel-1 interferograms to automatically discriminate between the active and stagnant regions, which is important for the postprocessing steps. We combined the data from the two sensors in areas of overlap to assess the performance of the fusion between two sensors in increasing the temporal scale of the observations. Our results suggest that dune migration at BD is subject to seasonal and multiyear variations that differed spatially across the dune field. Seasonal variations were observed with migration slowing during the summer months. We estimated the median for velocities belonging to the same season and calculated the seasonal sliding coefficient (SSC) representing the ratio between seasonal velocities. The median SSC reached a maximum value of ∼2 for winter/summer, while the ratios were ∼1.10 and ∼1.35 for winter/spring and winter/autumn, respectively. The seasonal variability of the temporal patterns was strongly supported by the wind observations. Between (1984–1998 and 1998–2007) and (1998–2007 and 2013–2021), decelerations in dune velocities were observed with percentages of ∼4 and ∼28%, respectively, and these decelerations were supported by a deceleration in wind velocities. Inversion of time series provides dense spatiotemporal monitoring of the dune activity. The fusion between two sensors allows condensing the temporal sampling up to a weekly scale especially for locations exposed to contamination of high cloud cover or dust.