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Zarajabad AM, Hadi M, Nodehi RN, Moradi M, Ghalhari MR, Zeraatkar A, Mahvi AH. Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain. Heliyon 2024; 10:e31493. [PMID: 38841507 PMCID: PMC11152681 DOI: 10.1016/j.heliyon.2024.e31493] [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: 12/17/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/07/2024] Open
Abstract
Groundwater pollution can occur due to both anthropogenic and natural causes, leading to a decline in water quality and posing a threat to human health and the environment. The pollution of ground water resources with chemical pollutants is often considered. To manage water resources sustainably, ensuring their quality and quantity is crucial. Yet, testing groundwater can be expensive and time-consuming. So, using modeling to predict the chemical parameters of groundwater resources is considered to be an efficient and economical method. In this study, we examined three models to predict groundwater quality in dry regions by using R programming language. The random forest (RF) outperformed the other models in developing predictive models for water quality. Also, the multiple linear regression (MLR) model demonstrated strong performance, particularly in predicting total hardness (TH) in Aran Va Bidgol groundwater resources. The decision tree (DT) model did well but had lower performance than the RF model in predicting quality parameters. This approach can be efficacious in the field of effective management and protection of groundwater resources and enables the assessment of risks related to water resources.
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Affiliation(s)
- Aysan Morovvati Zarajabad
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Hadi
- Center for Water Quality Research (CWQR), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran
| | - Ramin Nabizadeh Nodehi
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahsa Moradi
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Rezvani Ghalhari
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Zeraatkar
- Center for Monitoring Water and Wastewater Sanitation, Kashan Water and Wastewater Company, Kashan, Iran
| | - Amir Hossein Mahvi
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Maurya BM, Yadav N, T A, J S, A S, V P, Iyer M, Yadav MK, Vellingiri B. Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges. CHEMOSPHERE 2024; 353:141474. [PMID: 38382714 DOI: 10.1016/j.chemosphere.2024.141474] [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: 11/02/2023] [Revised: 01/17/2024] [Accepted: 02/14/2024] [Indexed: 02/23/2024]
Abstract
Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.
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Affiliation(s)
- Brij Mohan Maurya
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Nidhi Yadav
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Amudha T
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Satheeshkumar J
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Sangeetha A
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - Parthasarathy V
- Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Pollachi Main Road, Eachanari Post, Coimbatore, 641021, Tamil Nadu, India
| | - Mahalaxmi Iyer
- Centre for Neuroscience, Department of Biotechnology, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamil Nadu, India; Department of Microbiology, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Mukesh Kumar Yadav
- Department of Microbiology, Central University of Punjab, Bathinda, 151401, Punjab, India
| | - Balachandar Vellingiri
- Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Bathinda, 151401, Punjab, India.
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Hu Y, Lyu L, Wang N, Zhou X, Fang M. Application of hybrid improved temporal convolution network model in time series prediction of river water quality. Sci Rep 2023; 13:11260. [PMID: 37438608 DOI: 10.1038/s41598-023-38465-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/08/2023] [Indexed: 07/14/2023] Open
Abstract
Time series prediction of river water quality is an important method to grasp the changes of river water quality and protect the river water environment. However, due to the time series data of river water quality have strong periodicity, seasonality and nonlinearity, which seriously affects the accuracy of river water quality prediction. In this paper, a new hybrid deep neural network model is proposed for river water quality prediction, which is integrated with Savitaky-Golay (SG) filter, STL time series decomposition method, Self-attention mechanism, and Temporal Convolutional Network (TCN). The SG filter can effectively remove the noise in the time series data of river water quality, and the STL technology can decompose the time series data into trend, seasonal and residual series. The decomposed trend series and residual series are input into the model combining the Self-attention mechanism and TCN respectively for training and prediction. In order to verify the proposed model, this study uses opensource water quality data and private water quality data to conduct experiments, and compares with other water quality prediction models. The experimental results show that our method achieves the best prediction results in the water quality data of two different rivers.
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Affiliation(s)
- Yankun Hu
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Li Lyu
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ning Wang
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Xiaolei Zhou
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Meng Fang
- Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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Hasanpour Kashani M, Nikpour MR, Jalali R. Water quality prediction using data-driven models case study: Ardabil plain, Iran. Soft comput 2022. [DOI: 10.1007/s00500-022-07684-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Valenca R, Garcia L, Espinosa C, Flor D, Mohanty SK. Can water composition and weather factors predict fecal indicator bacteria removal in retention ponds in variable weather conditions? THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156410. [PMID: 35662595 DOI: 10.1016/j.scitotenv.2022.156410] [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: 02/16/2022] [Revised: 05/16/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
Retention ponds provide benefits including flood control, groundwater recharge, and water quality improvement, but changes in weather conditions could limit the effectiveness in improving microbial water quality metrics. The concentration of fecal indicator bacteria (FIB), which is used as regulatory standards to assess microbial water quality in retention ponds, could vary widely based on many factors including local weather and influent water chemistry and composition. In this critical review, we analyzed 7421 data collected from 19 retention ponds across North America listed in the International Stormwater BMP Database to examine if variable FIB removal in the field conditions can be predicted based on changes in these weather and water composition factors. Our analysis confirms that FIB removal in retention ponds is sensitive to weather conditions or seasons, but temperature and precipitation data may not describe the variable FIB removal. These weather conditions affect suspended solid and nutrient concentrations, which in turn could affect FIB concentration in the ponds. Removal of total suspended solids and total P only explained 5% and 12% of FIB removal data, respectively, and TN removal had no correlation with FIB removal. These results indicate that regression-based modeling with a single parameter as input has limited use to predict FIB removal due to the interactive nature of their effects on FIB removal. In contrast, machine learning algorithms such as the random forest method were able to predict 65% of the data. The overall analysis indicates that the machine learning model could play a critical role in predicting microbial water quality of surface waters under complex conditions where the variation of both water composition and weather conditions could deem regression-based modeling less effective.
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Affiliation(s)
- Renan Valenca
- Department of Civil and Environmental Engineering, University of California Los Angeles, CA, USA.
| | - Lilly Garcia
- Department of Civil and Environmental Engineering, University of California Los Angeles, CA, USA
| | - Christina Espinosa
- Department of Civil and Environmental Engineering, University of California Los Angeles, CA, USA
| | - Dilara Flor
- Department of Civil and Environmental Engineering, University of California Los Angeles, CA, USA
| | - Sanjay K Mohanty
- Department of Civil and Environmental Engineering, University of California Los Angeles, CA, USA.
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A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions. ENVIRONMENTS 2022. [DOI: 10.3390/environments9070085] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.
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Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam. WATER 2022. [DOI: 10.3390/w14101552] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.
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