A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent

• Published in: Journal of environmental management 2024-11, Vol.370, p.122614, Article 122614
• Main Authors: Ahmed, Yunus, Siddiqua Maya, Akser Alam, Akhtar, Parul, Alam, Md Shafiul, AlMohamadi, Hamad, Islam, Md Nurul, Alharbi, Obaid A., Rahman, Syed Masiur
• Format: Article
• Language: English
• Subjects: Adsorption; Algorithms; Anti-Bacterial Agents - chemistry; Bayes Theorem; Bayesian optimization; Ciprofloxacin - chemistry; Ciprofloxacin adsorption; Gradient Boosting; Machine Learning; Nano adsorbent; Water Pollutants, Chemical - chemistry
• ISSN: 0301-4797; 1095-8630; 1095-8630
• DOI: 10.1016/j.jenvman.2024.122614

The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat this problem, accurate predictive models are essential. This research focused on employing machine learning (ML) techniques to construct some models for analyzing the adsorption capacity of ciprofloxacin (CIP) antibiotic from contaminated water. The robustness of ten machine learning algorithms was evaluated using performance metrics such as the Coefficient of determination (R2), Mean Square Error (MSE), Median Absolute Error (MedAE), Mean Absolute Error (MAE), Correlation coefficient (R), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Square Error (RMSE). The hyperparameters of the ML models were fine-tuned using the Bayesian optimization algorithm. The optimized models were comprehensively evaluated using feature importance analysis to quantify the relative significance of operational variables accurately. After a thorough assessment and comparison of various machine learning models, it was evident that the HistGradientBoosting (HGB) model outperformed others in terms of CIP adsorption performance. This was supported by their low MAE value of 0.1865 and high R2 value of 0.9999. The modeling projected the highest antibiotic adsorption (99.28%) under optimized conditions, including 10 mg/L of CIP, 357 mg/L of CuWO4@TiO2 adsorbent, a contact time of 60 min at room temperature, and near neutral pH (7.5). The combination of advanced ML algorithms and nano adsorbents has great potential for addressing the problem of antibiotic pollution in water sources. [Display omitted] •Ten advanced machine-learning models were selected to predict the ciprofloxacin (CIP) adsorption from contaminated water.•HistGradientBoosting (HGB) and Gradient Boosting (GB) models outperformed as compared to others studied models.•Bayesian optimization was used to tune network hyperparameters and get the best results.•The HGB and GB models achieved MAE values of 0.1865 and 0.2268, respectively, with high R2 values of 0.9999.•The modeling projected the highest antibiotic adsorption (99.28%) under optimized conditions.