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A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models' performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040-4.5828)] and Cubist [3.1736 (1.1012-4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R 2  = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R 2  = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00-83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes., (© 2023. The Author(s).)

The preference of biofloculants over chemical flocculants in water and wastewater remediation systems has gained wider attention due to their biodegradability, innocuousness, safety to human and environmental friendliness. The present study aimed to evaluate research outputs on bioflocculant potentials in wastewater remediation from 1990 to 2019 using bibliometric analyses. To the best of our knowledge, this is the first bibliometric report in bioflocculant research. The subject bibliometric dataset was extracted from the Web of Science Core Collection (WoSCC) and Scopus using the Boolean, 'bioflocculant* and waste*' and analysed for indicators such as a yearly trend, productivity (authors, articles, country, institution and journal source), conceptual framework and collaboration network. We found 119 documents with 347 authors from 78 journal sources on the subject, an annual growth rate of 12·1%, and average citations/document of 15·08. Guo J. and Wang Y. were the top researchers with 15 and 12 outputs respectively. China (42%) and South Africa (9·24%) ranked the top two dominant countries in the field. The top journals were Bioresource Technology (9 papers, 506 citations), Applied Microbiology and Biotechnology (5 papers, 268 citations), whereas, the top institution was Chengdu University of Information and Technology (n = 9 documents) followed by Sichuan Univ. Sci. & Engn, China (n = 8 documents). This study found that lack of intercountry collaboration and research funding adversely affects research participants in the field., (© 2020 The Society for Applied Microbiology.)