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Das CR, Das S. Coastal groundwater quality prediction using objective-weighted WQI and machine learning approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:19439-19457. [PMID: 38355860 DOI: 10.1007/s11356-024-32415-w] [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: 09/28/2023] [Accepted: 02/07/2024] [Indexed: 02/16/2024]
Abstract
The water quality index (WQI) is a globally accepted guideline to indicate the water quality standard of any groundwater resource. Water levels in existing groundwater sources are declining in several coastal zones. Therefore, for monitoring water quality and improving water management, the prediction and identification of groundwater status by an effective technique with higher accuracy is urgently needed. Therefore, this research aims to find an effective model for WQI prediction by comparing entropy and critic weight-based WQI (ENW-WQI and CRITIC-WQI) with multi-layer perceptron artificial neural network (MLP-ANN) technique and also to identify contaminated zones using GIS. Initially, 1000 water sampling datasets with concentrations of several water quality parameters of different coastal blocks of eastern India during 2018 to 2022 are considered for the estimation of ENW-WQI and CRITIC-WQI. It shows 65% and 67% of the samples are excellent to good for drinking. ENW-WQI and CRITIC-WQI-based MLP-ANN models have been established considering different data portioning and hidden neuron numbers. Input variables and appropriate dataset partitioning with hidden neurons for models obtained from correlation and trial-error analysis. Spatial distribution maps are also produced for calculated WQIs using inverse distance weighted interpolation approaches. Three fitting models are obtained: ENW-WQI-MLP-ANN, CRITIC-WQI-MLP-ANN-I and CRITIC-WQI-MLP-ANN-II. CRITIC-WQI-MLP-ANN-II model (data ratio 85:15, network structure 6-12-1, R2 = 0.986, NSE = 0.98, and error rate 0.49%) provides the best accuracy in WQI prediction. The GIS-based WQI maps record several areas related to drinking water quality. The results of this research can help in planning the provision of safe drinking water in the future.
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Affiliation(s)
- Chinmoy Ranjan Das
- School of Water Resources Engineering, Jadavpur University, Kolkata, India
- Civil Engineering Department, Global Institute of Science & Technology, Purba Medinipur 721657, Haldia, West Bengal, India
| | - Subhasish Das
- School of Water Resources Engineering, Jadavpur University, Kolkata, India.
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Wang J, Xue B, Wang Y, A Y, Wang G, Han D. Identification of pollution source and prediction of water quality based on deep learning techniques. JOURNAL OF CONTAMINANT HYDROLOGY 2024; 261:104287. [PMID: 38219283 DOI: 10.1016/j.jconhyd.2023.104287] [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: 10/31/2023] [Revised: 12/10/2023] [Accepted: 12/19/2023] [Indexed: 01/16/2024]
Abstract
Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.
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Affiliation(s)
- Junping Wang
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Baolin Xue
- Innovation Research Center of Satellite Application (IRCSA), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
| | - Yuntao Wang
- Innovation Research Center of Satellite Application (IRCSA), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Yinglan A
- Innovation Research Center of Satellite Application (IRCSA), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Guoqiang Wang
- Innovation Research Center of Satellite Application (IRCSA), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Dongqing Han
- Hohhot Environmental Monitoring Branch Station of Inner Mongolia, Hohhot 010030, China
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El-Shafeiy E, Alsabaan M, Ibrahem MI, Elwahsh H. Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique. SENSORS (BASEL, SWITZERLAND) 2023; 23:8613. [PMID: 37896705 PMCID: PMC10610887 DOI: 10.3390/s23208613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/08/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies.
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Affiliation(s)
- Engy El-Shafeiy
- Department of Computer Science, Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City 32897, Monufia, Egypt;
| | - Maazen Alsabaan
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;
| | - Mohamed I. Ibrahem
- School of Computer and Cyber Sciences, Augusta University, Augusta, GA 30912, USA;
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Haitham Elwahsh
- Computer Science Department, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
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Maneechot L, Wong YJ, Try S, Shimizu Y, Bharambe KP, Hanittinan P, Ram-Indra T, Usman M. Evaluating the necessity of post-processing techniques on d4PDF data for extreme climate assessment. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:102531-102546. [PMID: 37670092 DOI: 10.1007/s11356-023-29572-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/25/2023] [Indexed: 09/07/2023]
Abstract
The occurrence and severity of extreme precipitation events have been increasing globally. Although numerous projections have been proposed and developed for evaluating the climate change impacts, most models suffer from significant bias error due to the coarse resolution of the climate datasets, which affects the accuracy of the climate change assessment. Therefore, in this study, post-processing techniques (interpolation and bias correction methods) were adopted on the database for Policy Decision Making for Future Climate Change (d4PDF) model for extreme climatic flood events simulation in the Chao Phraya River Basin, Thailand, under + 4-K future climate simulation. Due to the limited number of the rain gages, the gradient plus inverse distance squared interpolation method (combination of multiple linear regression and distance weighting methods) was applied in this study. In the bias correction methods, the additional setting of monthly and seasonal periods was adjusted. The proposed bias correction approach deployed gamma distribution combined with generalized Pareto distribution setting with the seasonal period for the rainy season datasets, whereas only the gamma setting was applied with the monthly period during the dry season. The outcomes revealed that the proposed method could react to extreme rainfall events, expand dry days during dry season, and intensify rainfall amount during rainy season. The post-processing d4PDF trends of six sea surface temperature (SST) patterns (consists of 90 ensemble members) of two periods (near future: 2051-2070 and far future: 2091-2110) recorded the highest and lowest amounts of annual rainfalls of 4,450 mm/year in mid-stream of Nan River and 710 mm/year in the lower CPRB, respectively. Notably, the significant variances noted in the rainfall patterns among ensembles, demanding further investigation in future climate change, impact studies. The findings of the study provided novel insights on the importance of proper post-processing techniques for improving the robustness of d4PDF in climate change impacts assessment.
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Affiliation(s)
- Luksanaree Maneechot
- Climate Action for Sustainability Office, Sustainability Engineering, Department of Corporate Engineering, Charoen Pokphand Foods PCL, Bangkok, Thailand
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, Kyoto, Japan
| | - Yong Jie Wong
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, Kyoto, Japan.
- Department of Bioenvironmental Design, Faculty of Bioenvironmental Sciences, Kyoto University of Advanced Science, Kameoka, 606-8501, Japan.
| | - Sophal Try
- Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, 611-0011, Japan
| | - Yoshihisa Shimizu
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, Kyoto, Japan
| | - Khagendra Pralhad Bharambe
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, Kyoto, Japan
- Socio and Eco Environment Risk Management, Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan
| | - Patinya Hanittinan
- Engineering Department, Gulf Energy Development Public Company Limited, Bangkok, Thailand
| | - Teerawat Ram-Indra
- Department of Bioenvironmental Design, Faculty of Bioenvironmental Sciences, Kyoto University of Advanced Science, Kameoka, 606-8501, Japan
| | - Muhammad Usman
- School of Engineering, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC, 3216, Australia
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Barzegar Y, Gorelova I, Bellini F, D’Ascenzo F. Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6522. [PMID: 37569062 PMCID: PMC10418417 DOI: 10.3390/ijerph20156522] [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: 06/28/2023] [Revised: 07/26/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023]
Abstract
Drinking water quality assessment is a major issue today, as it is crucial to supply safe drinking water to ensure the well-being of society. Predicting drinking water quality helps strengthen water management and fight water pollution; technologies and practices for drinking water quality assessment are continuously improving; artificial intelligence methods prove their efficiency in this domain. This research effort seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system is applied with different defuzzification methods. The proposed model includes three fuzzy intermediate models and one fuzzy final model. Each model consists of three input parameters and 27 fuzzy rules. A water quality assessment model is developed with a dataset that considers nine parameters (alkalinity, hardness, pH, Ca, Mg, fluoride, sulphate, nitrates, and iron). These nine parameters of drinking water are anticipated to be within the acceptable limits set to protect human health. Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; they are an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The proposed method can provide an effective solution for complex systems; this method can be modified easily to improve performance.
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Affiliation(s)
| | - Irina Gorelova
- Department of Management, Sapienza University of Rome, 00161 Rome, Italy; (Y.B.); (F.B.); (F.D.)
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Maxhuni A, Lazo P, Berisha L. Assessment of the Anthropogenic and Natural Factors on the Level of the Heavy Metals and Biogenic Elements in Soils in Kosovo. WATER, AIR, & SOIL POLLUTION 2023; 234:452. [DOI: 10.1007/s11270-023-06443-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/14/2023] [Indexed: 07/25/2023]
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7
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Tselemponis A, Stefanis C, Giorgi E, Kalmpourtzi A, Olmpasalis I, Tselemponis A, Adam M, Kontogiorgis C, Dokas IM, Bezirtzoglou E, Constantinidis TC. Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6216. [PMID: 37444064 PMCID: PMC10341787 DOI: 10.3390/ijerph20136216] [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: 05/12/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.
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Affiliation(s)
- Athanasios Tselemponis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Christos Stefanis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Elpida Giorgi
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Aikaterini Kalmpourtzi
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Ioannis Olmpasalis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Antonios Tselemponis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Maria Adam
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Christos Kontogiorgis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Ioannis M. Dokas
- Department of Civil Engineering, Democritus University of Thrace, 69100 Komotini, Greece;
| | - Eugenia Bezirtzoglou
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
| | - Theodoros C. Constantinidis
- Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, 68100 Alexandroupoli, Greece; (A.T.); (E.G.); (A.K.); (I.O.); (A.T.); (M.A.); (C.K.); (E.B.); (T.C.C.)
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Khdre AM, Ramadan SA, Ashry A, Alaraby M. Chironomus sp. as a Bioindicator for Assessing Microplastic Contamination and the Heavy Metals Associated with It in the Sediment of Wastewater in Sohag Governorate, Egypt. WATER, AIR, AND SOIL POLLUTION 2023; 234:161. [PMID: 36855709 PMCID: PMC9951840 DOI: 10.1007/s11270-023-06179-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
The consequences of plastic waste pollution have imposed wide global concerns. One of these consequences is the production of micro- and nanosized particles (MNPLs) from aged plastics. The problem of MNPLs is magnified by their potential to transport various contaminants due to their large surface area and other variable physiochemical properties. From this point on, it is important to know the real concentration of MNPLs in our environment and their potential to internalize wild organisms as well as transfer contaminants that are completely highlighted. As a result, our study is the first to detect MP pollution in the upper Egypt wastewater environment. It could be utilized as a baseline to estimate MP wastes and develop management techniques, particularly in Sohag Governorate. The concentration and characterization of MPs in sludge, water, Chironomus sp. larvae, and their tubes were studied in this work. Chironomus sp. is a reliable bioindicator prevalent in such contaminated environments, and it was used to demonstrate how MPs invade biological barriers. Our results found that red and blue polyester fibers are much more prevalent than other polymers, colors, and shapes of MPs. While each dry kilogram of wastewater sludge contains 310 ± 84 particles, this amount is reduced to 1.55 ± 0.7 per liter in the water column. Biologically, the present study succeeded in detecting the MPs inside the wild organism, with concentrations reaching 71 ± 21 and 4.41 ± 1.1 particles per gram wet weight in Chironomus sp. larvae and their tubes (chironomid tubes), respectively. The potential hazard of MPs stems from their propensity to transfer pollutants. At this point, our findings revealed a corresponding and significant concentration of various heavy metals (Cu, Pb, Cd, and Ni) detected in MPs or Chironomus sp. versus sludge. In conclusion, our findings not only proved the presence of MPs in wastewater but also demonstrated their ability to internalize cross-wild organisms, allowing toxins to accumulate inside their bodies, raising concerns about the possible health impacts of plastic pollution.
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Affiliation(s)
- Azza M. Khdre
- Entomology and Environmental Toxicology Group, Zoology Department, Faculty of Science, Sohag University, Sohag, 82524) Egypt
| | - Somaia A. Ramadan
- Entomology and Environmental Toxicology Group, Zoology Department, Faculty of Science, Sohag University, Sohag, 82524) Egypt
| | - Ali Ashry
- Entomology and Environmental Toxicology Group, Zoology Department, Faculty of Science, Sohag University, Sohag, 82524) Egypt
| | - Mohamed Alaraby
- Entomology and Environmental Toxicology Group, Zoology Department, Faculty of Science, Sohag University, Sohag, 82524) Egypt
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Yan T, Zhou A, Shen SL. Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 318:120870. [PMID: 36526051 DOI: 10.1016/j.envpol.2022.120870] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/26/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Water quality assessment is critical to better recognise the importance of water in human society. In this study, a new framework to predict long-term water quality is proposed by using Bayesian-optimised machine learning methods and key pollution indicators collected from monitoring stations in the Pearl River Estuary, Guangdong, China. The optimised stacked generalisation (SG-op) model achieved the best performance with the highest accuracy (0.992) and Kappa coefficient (0.987). Feature importance of the prediction model was consistent with key pollution indicators. The Spearman rank correlation coefficient was used to determine the significance level of the variation trends of different pollution indicators. The results show that the total phosphorus (TOP), dissolved oxygen (DO), chemical oxygen demand (COD), and petroleum (PET) among the key pollution indicators were on an upward trend in the study area. This framework can be applied to efficiently predict future water quality and to provide technical support for emergency pollution control.
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Affiliation(s)
- Tao Yan
- MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China; Discipline of Civil and Infrastructure Engineering, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria, 3001, Australia.
| | - Annan Zhou
- Discipline of Civil and Infrastructure Engineering, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria, 3001, Australia.
| | - Shui-Long Shen
- MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China.
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Jadhav AR, Pathak PD, Raut RY. Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:321. [PMID: 36689041 DOI: 10.1007/s10661-022-10904-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.
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Affiliation(s)
| | - Pranav D Pathak
- MIT School of Bioengineering Sciences & Research, MIT-Art, Design and Technology University, Pune, Maharashtra, India.
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Wong YJ, Shiu HY, Chang JHH, Ooi MCG, Li HH, Homma R, Shimizu Y, Chiueh PT, Maneechot L, Nik Sulaiman NM. Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions. JOURNAL OF CLEANER PRODUCTION 2022; 365:132893. [PMID: 35781986 PMCID: PMC9234473 DOI: 10.1016/j.jclepro.2022.132893] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 06/01/2022] [Accepted: 06/24/2022] [Indexed: 05/19/2023]
Abstract
The unprecedented outbreak of COVID-19 significantly improved the atmospheric environment for lockdown-imposed regions; however, scant evidence exists on its impacts on regions without lockdown. A novel research framework is proposed to evaluate the long-term monthly spatiotemporal impact of COVID-19 on Taiwan air quality through different statistical analyses, including geostatistical analysis, change detection analysis and identification of nonattainment pollutant occurrence between the average mean air pollutant concentrations from 2018-2019 and 2020, considering both meteorological and public transportation impacts. Contrary to lockdown-imposed regions, insignificant or worsened air quality conditions were observed at the beginning of COVID-19, but a delayed improvement occurred after April in Taiwan. The annual mean concentrations of PM10, PM2.5, SO2, NO2, CO and O3 in 2020 were reduced by 24%, 18%, 15%, 9.6%, 7.4% and 1.3%, respectively (relative to 2018-2019), and the overall occurrence frequency of nonattainment air pollutants declined by over 30%. Backward stepwise regression models for each air pollutant were successfully constructed utilizing 12 meteorological parameters (R2 > 0.8 except for SO2) to simulate the meteorological normalized business-as-usual concentration. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model simulated the fate of air pollutants (e.g., local emissions or transboundary pollution) for anomalous months. The changes in different public transportation usage volumes (e.g., roadway, railway, air, and waterway) moderately reduced air pollution, particularly CO and NO2. Reduced public transportation use had a more significant impact than meteorology on air quality improvement in Taiwan, highlighting the importance of proper public transportation management for air pollution control and paving a new path for sustainable air quality management even in the absence of a lockdown.
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Affiliation(s)
- Yong Jie Wong
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 520-0811, Japan
| | - Huan-Yu Shiu
- Graduate Institute of Environmental Engineering, National Taiwan University, 10617, Taiwan
| | - Jackson Hian-Hui Chang
- Department of Atmospheric Sciences, National Central University, 32001, Taiwan
- Preparatory Center for Science and Technology (PPST), Universiti Malaysia Sabah, 88400, Malaysia
| | - Maggie Chel Gee Ooi
- Institute of Climate Change, National University of Malaysia (UKM), Bangi, 43600, Malaysia
| | - Hsueh-Hsun Li
- Graduate Institute of Environmental Engineering, National Taiwan University, 10617, Taiwan
| | - Ryosuke Homma
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 520-0811, Japan
| | - Yoshihisa Shimizu
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 520-0811, Japan
| | - Pei-Te Chiueh
- Graduate Institute of Environmental Engineering, National Taiwan University, 10617, Taiwan
| | - Luksanaree Maneechot
- Environmental Engineering and Disaster Management Program, School of Interdisciplinary Studies, Mahidol University Kanchanaburi Campus (MUKA), Kanchanaburi, 71150, Thailand
| | - Nik Meriam Nik Sulaiman
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
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12
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Yan T, Shen SL, Zhou A. Indices and models of surface water quality assessment: Review and perspectives. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 308:119611. [PMID: 35716892 DOI: 10.1016/j.envpol.2022.119611] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/09/2022] [Accepted: 06/09/2022] [Indexed: 06/15/2023]
Abstract
Many technologies have been designed to monitor, evaluate, and improve surface water quality, as high-quality water is essential for human activities including agriculture, livestock, and industry. As such, in this study, we investigated water quality indices (WQIs), trophic status indices (TSIs), and heavy metal indices (HMIs) for assessing surface water quality. Based on these indices, we summarised and compared water assessment models using expert system (ES) and machine learning (ML) methods. We also discussed the current status and future perspectives of water quality management. The results of our analyses showed that assessment indices can be used in three aspects of surface water quality assessment: WQIs are aggregated from multiple parameters and commonly used in surface water quality classification; TSIs are calculated from the concentrations of different nutrients required for algae and bacteria, and employed to evaluate the eutrophication levels of lakes and reservoirs; HMIs are mainly applied for human health risk assessment and the analysis of correlation of heavy metal sources. ES- and ML-based assessment models have been developed to efficiently generate assessment indices and predict water quality status based on big data obtained from new techniques. By implementing dynamic monitoring and analysis of water quality, we designed a next-generation water quality management system based on the above indices and assessment models, which shows promise for improving the accuracy of water quality assessment.
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Affiliation(s)
- Tao Yan
- MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China; Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria, 3001, Australia.
| | - Shui-Long Shen
- MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China.
| | - Annan Zhou
- Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria, 3001, Australia.
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13
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Sakaa B, Elbeltagi A, Boudibi S, Chaffaï H, Islam ARMT, Kulimushi LC, Choudhari P, Hani A, Brouziyne Y, Wong YJ. Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:48491-48508. [PMID: 35192167 DOI: 10.1007/s11356-022-18644-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
The water quality index is one of the prominent general indicators to assess and classify surface water quality, which plays a critical role in river water resources practices. This research constructs a hybrid artificial intelligence model namely sequential minimal optimization-support vector machine (SMO-SVM) along with random forest (RF) as a benchmark model for predicting water quality values at the Wadi Saf-Saf river basin in Algeria. The fifteen input water quality datasets such as biochemical oxygen demand (BOD), oxygen saturation (OS), the potential for hydrogen (pH), chemical oxygen demand (COD), chloride (Cl-), dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), nitrate-nitrogen (NO3-N), nitrite-nitrogen (NO2-N), phosphate (PO43-), ammonium (NH4+), temperature (T), turbidity (NTU), and suspended solids (SS) were employed for constructing the predictive models. Different input data combinations are evaluated in terms of predictive performance, using a set of statistical metrics and graphical representation. Results show that less than 40% of samples were observed to be poor quality water during the dry season in downstream northeastern part of the basin. The findings also show that the RF model mostly generates more precise water quality index predictions than the SMO-SVM model for both training and testing stages. Although thirteen input parameters attain the optimal predictive performance (R2 testing = 0.82, RMSE testing = 5.17), a couple of five input parameters, e.g., only pH, EC, TDS, T, and saturation, gives the second optimal predictive precision (R2 test = 0.81, RMSE testing = 5.55). The sensitivity analysis results indicate a greater sensitivity by the all input variables chosen except NO2- of the predictive outcomes to the earlier influencing water quality parameters. Overall, the RF model reveals an improvement on earlier tools for predicting water quality index, according to predictive performance and reducing in the number of input variables.
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Affiliation(s)
- Bachir Sakaa
- Scientific and Technical Research Center on Arid Regions (CRSTRA), BP 1682 RP, 07000, Biskra, Algeria
- Faculty of Earth Sciences, Laboratory of Water Resource and Sustainable Development (REED), Badji Mokhtar University, BP 12, 23000, Annaba, Algeria
| | - Ahmed Elbeltagi
- Agricultural Engineering Dept, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt
| | - Samir Boudibi
- Scientific and Technical Research Center on Arid Regions (CRSTRA), BP 1682 RP, 07000, Biskra, Algeria.
| | - Hicham Chaffaï
- Faculty of Earth Sciences, Laboratory of Water Resource and Sustainable Development (REED), Badji Mokhtar University, BP 12, 23000, Annaba, Algeria
| | | | - Luc Cimusa Kulimushi
- Department of Environmental Studies, University of Lay Adventists of Kigali, P.O. Box: 6392, Kigali, Rwanda
| | | | - Azzedine Hani
- Faculty of Earth Sciences, Laboratory of Water Resource and Sustainable Development (REED), Badji Mokhtar University, BP 12, 23000, Annaba, Algeria
| | - Youssef Brouziyne
- International Water Research Institute, Mohammed VI Polytechnic University (UM6P), 43150, Benguerir, Morocco
| | - Yong Jie Wong
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga, 520-0811, Japan
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