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Lin Z, Lim JY, Oh JM. Innovative interpretable AI-guided water quality evaluation with risk adversarial analysis in river streams considering spatial-temporal effects. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 350:124015. [PMID: 38657892 DOI: 10.1016/j.envpol.2024.124015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
Water security remains a critical issue given the looming threats of industrial pollution, necessitating comprehensive assessments of water quality to address seasonal fluctuations and influential factors while formulating effective strategies for decision makers. This study introduces a novel approach for evaluating water quality within a complex riverine zone in South Korea: Han River that encompasses five river streams situated at each junction of North and South streams (including Gyeongan Stream) that ultimately leading towards Paldang Lake. By utilizing the monthly water characteristic data from the year 2013-2022 across 14 different locations, the significant seasonal trends and potential influences on water quality are identified. The water quality here is calculated with the proposed method of sub-index water quality index (s-WQI). A combinatorial prediction approach of s-WQI for each location is conducted through a collective of data preprocessing approaches including Hampel filtering and feature selection in prior to the machine learning predictions. In return, light gradient boosting (LGB) is the most accurate predictor by outperforming other prediction algorithms, especially through LGB-Pearson and LGB-Spearman combinations for North and South stream intersections, and LGB-Pearson for Paldang Lake. To further evaluate the robustness of this evaluation and extending the results to a foreseeable scenario, a seasonal based Monte-Carlo Simulation with 10,000 attempts targeting the water characteristic distributions obtained from each location considered are carried out to identify the risk bounds within. The results are further interpreted with SHAP analysis on identifying the contributions of each water characteristics towards the water quality through local and global spectrum. This research yields practical implications, offering tailored strategies for water quality enhancement and early warning systems. The integration of AI-based prediction and feature selection underscores the transformative potential of computational techniques in advancing data-driven water quality assessments, shaping the future of environmental science research.
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Affiliation(s)
- ZiYu Lin
- Department of Environmental Science and Engineering, Kyung Hee University, Yongin-si, 17104, Gyeonggi, Republic of Korea
| | - Juin Yau Lim
- Korea Biochar Research Center & APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea; School of Business Administration, Korea University, Seoul, 02841, Republic of Korea
| | - Jong-Min Oh
- Department of Environmental Science and Engineering, Kyung Hee University, Yongin-si, 17104, Gyeonggi, Republic of Korea.
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Essamlali I, Nhaila H, El Khaili M. Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon 2024; 10:e27920. [PMID: 38533055 PMCID: PMC10963334 DOI: 10.1016/j.heliyon.2024.e27920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 03/28/2024] Open
Abstract
Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).
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Affiliation(s)
- Ismail Essamlali
- Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Morocco
| | - Hasna Nhaila
- Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Morocco
| | - Mohamed El Khaili
- Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Morocco
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Talukdar S, Shahfahad, Bera S, Naikoo MW, Ramana GV, Mallik S, Kumar PA, Rahman A. Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119866. [PMID: 38147770 DOI: 10.1016/j.jenvman.2023.119866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/28/2023]
Abstract
Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.
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Affiliation(s)
- Swapan Talukdar
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
| | - Shahfahad
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
| | - Somnath Bera
- Department of Geography, Central University of South Bihar, Gaya, Bihar, 823001, India.
| | - Mohd Waseem Naikoo
- Department of Geography & Disaster Management, University of Kashmir, Srinagar, Jammu & Kashmir, 190006, India.
| | - G V Ramana
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
| | - Santanu Mallik
- Department of Civil Engineering, National Institution of Technology, Agaratala, Tripura, 799046, India.
| | - Potsangbam Albino Kumar
- Department of Civil Engineering, National Institution of Technology, Imphal, Manipur, 795004, India.
| | - Atiqur Rahman
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
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Uddin MG, Imran MH, Sajib AM, Hasan MA, Diganta MTM, Dabrowski T, Olbert AI, Moniruzzaman M. Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches. JOURNAL OF CONTAMINANT HYDROLOGY 2024; 261:104307. [PMID: 38278020 DOI: 10.1016/j.jconhyd.2024.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/10/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024]
Abstract
The Rooppur Nuclear Power Plant (RNPP) at Ishwardi, Bangladesh is planning to go into operation within 2024 and therefore, adjacent areas of RNPP is gaining adequate attention from the scientific community for environmental monitoring purposes especially for water resources management. However, there is a substantial lack of literature as well as environmental datasets for earlier years since very little was done at the beginning of the RNPP's construction phase. Therefore, this study was conducted to assess the potential toxic elements (PTEs) contamination in the groundwater and its associated health risk for residents at the adjacent part of the RNPP during the year of 2014-2015. For the purposes of achieving the aim of the study, groundwater samples were collected seasonally (dry and wet season) from nine sampling sites and afterwards analyzed for water quality indicators such as temperature (Temp.), pH, electrical conductivity (EC), total dissolved solid (TDS), total hardness (TH) and for PTEs including Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), Chromium (Cr), Cadmium (Cd) and Arsenic (As). This study adopted the newly developed Root Mean Square water quality index (RMS-WQI) model to assess the scenario of contamination from PTEs in groundwater whereas the human health risk assessment model was utilized to quantify the risk of toxicity from PTEs. In most of the sampling sites, PTEs concentration was found higher during the wet season than the dry season and Fe, Mn, Cd and As exceeded the guideline limit for drinking water. The RMS score mostly classified the groundwater in terms of PTEs contamination into "Fair" condition. The non-carcinogenic risks (expressed as Hazard Index-HI) revealed that around 44% and 89% of samples for adults and 67% and 100% of samples for children exceeded the threshold limit set by USEPA (HI > 1) and possessed risks through the oral pathway during dry and wet season, respectively. Furthermore, the calculated cumulative HI score was found higher for children than the adults throughout the study period. In terms of carcinogenic risk (CR) from PTEs, the magnitude of risk decreased following the pattern of Cr > As > Cd. Although the current study is based on old dataset, the findings might serve as a baseline for monitoring purposes to reduce future hazardous impact from the power plant.
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Affiliation(s)
- Md Galal Uddin
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland; Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh.
| | - Md Hasan Imran
- Department of Environmental Science and Resource Management, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
| | - Abdul Majed Sajib
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | - Md Abu Hasan
- Bangladesh Reference institute for Chemical Measurements (BRiCM), Dr. Qudrat-e-Khuda Road, Dhanmondi, Dhaka 1205, Bangladesh
| | - Mir Talas Mahammad Diganta
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | | | - Agnieszka I Olbert
- Civil Engineering, School of Engineering, College of Science and Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
| | - Md Moniruzzaman
- Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh
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Cao Y, Liu Z, Niu Y, Liu X. A novel transformer-based method for predicting air absorbed dose rates in nuclear radiation environmental monitoring. Heliyon 2023; 9:e19870. [PMID: 37809737 PMCID: PMC10559244 DOI: 10.1016/j.heliyon.2023.e19870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/01/2023] [Accepted: 09/04/2023] [Indexed: 10/10/2023] Open
Abstract
Many studies have used various methods to estimate future nuclear radiation levels to control radiation contamination, provide early warnings, and protect public health and the environment. However, due to the high uncertainty and complexity of nuclear radiation data, existing prediction methods face the challenges of low prediction accuracy and short warning time. Therefore, accurate prediction of nuclear radiation levels is essential to safeguard human health and safety. This study proposes a novel Mixformer model to predict future hourly nuclear radiation data. The seasonality and trend of nuclear radiation data are extracted by data decomposition. To address the slow speed problem common in traditional methods for long-time series prediction tasks, Mixformer simplifies the decoder with convolutional layers to speed up the convergence of the model. The experiments consider the air-absorbed dose rate of nuclear radiation data, spectral data, six climatic conditions, and two other conditions. We use MSE and MAE metrics to verify the effectiveness of Mixformer prediction. The results show that the Mixformer proposed in this paper has better prediction performance compared to the currently popular models. Therefore, the proposed model is a feasible method for industrial nuclear radiation data processing and prediction.
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Affiliation(s)
- Yizhi Cao
- Zhejiang University, HangZhou, 310027, Zhejiang, China
| | - Zhaoran Liu
- Zhejiang University, HangZhou, 310027, Zhejiang, China
| | - Yunlong Niu
- Radiation Environment Monitoring Technology Center, Ministry of Ecology and Environment, Hangzhou 310012, PR China
| | - Xinggao Liu
- Zhejiang University, HangZhou, 310027, Zhejiang, China
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