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Xu J, Mo Y, Zhu S, Wu J, Jin G, Wang YG, Ji Q, Li L. Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China. Heliyon 2024; 10:e33695. [PMID: 39044968 PMCID: PMC11263670 DOI: 10.1016/j.heliyon.2024.e33695] [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: 01/21/2024] [Revised: 06/14/2024] [Accepted: 06/25/2024] [Indexed: 07/25/2024] Open
Abstract
The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3-72.0 and 56.4-67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.
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Affiliation(s)
- Jing Xu
- College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China
| | - Yuming Mo
- School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang, China
| | - Senlin Zhu
- College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China
| | - Jinran Wu
- Institute for Positive Psychology and Education, Australian Catholic University, North Sydney, Australia
| | - Guangqiu Jin
- The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China
| | - You-Gan Wang
- School of Mathematics and Physics, The University of Queensland, Queensland, Australia
| | - Qingfeng Ji
- College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China
| | - Ling Li
- Key Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER), School of Engineering, Westlake University, Hangzhou, China
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Sarkar SK, Rudra RR, Talukdar S, Das PC, Nur MS, Alam E, Islam MK, Islam ARMT. Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh. Sci Rep 2024; 14:10328. [PMID: 38710767 DOI: 10.1038/s41598-024-60560-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/24/2024] [Indexed: 05/08/2024] Open
Abstract
The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study's results to inform evidence-based choices on water management and planning at the national level.
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Affiliation(s)
- Showmitra Kumar Sarkar
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.
| | - Rhyme Rubayet Rudra
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
| | - Swapan Talukdar
- Department of Geography, Asutosh College, University of Calcutta, Kolkata, 700026, India
| | - Palash Chandra Das
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
- Department of Geography, Texas A&M University, College Station, USA
| | - Md Sadmin Nur
- Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
| | - Edris Alam
- Faculty of Resilience, Rabdan Academy, 22401, Abu Dhabi, United Arab Emirates
- Department of Geography and Environmental Studies, University of Chittagong, Chittagong, 4331, Bangladesh
| | - Md Kamrul Islam
- Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, AlAhsa, 31982, Saudi Arabia
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh
- Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh
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Nasiri Khiavi A, Mostafazadeh R, Adhami M. Groundwater quality modeling and determining critical points: a comparison of machine learning to Best-Worst Method. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:115758-115775. [PMID: 37889408 DOI: 10.1007/s11356-023-30530-8] [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: 07/05/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023]
Abstract
In Iran, similar to other developing countries, groundwater quality has been seriously threatened. Therefore, this study aimed to apply Machine Learning Algorithms (MLAs) in Groundwater Quality Modeling (GQM) and determine the optimal algorithm using the Best-Worst Method (BWM) in Ardabil province, Iran. Groundwater quality parameters included calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), chlorine (Cl-), sulfate (SO4-), total dissolved solids (TDS), bicarbonate (HCO3-), electrical conductivity (EC), and acidity (pH). In the following, seven MLAs, including Support Vector Regression (SVR), Random Forest (RF), Decision Tree Regressor (DTR), K-Nearest Neighbor (KNN), Naïve Bayes, Simple Linear Regression (SLR), and Support Vector Machine (SVM), were used in the Python programming language, and groundwater quality was modeled. Finally, BWM was used to validate the results of MLAs. The results of examining the error statistics in determining the optimal algorithm in groundwater quality modeling showed that the RF algorithm with values of MAE = 0.28, MSE = 0.12, RMSE = 0.35, and AUC = 0.93 was selected as the most optimal MLA. The Schoeller diagram also showed that various ion ratios, including Na+K, Ca2+, Mg2+, Cl-, and HCO3+CO3, in most of the sampled points had upward average values. Based on the results of the BWM method, it can be concluded that a great similarity was observed between the results of the RF algorithm and the classification of the BWM method. These results showed that more than 50% of the studied area had low quality based on hydro-chemical parameters of groundwater quality. The findings of this research can assist managers and planners in developing suitable management models and implementing appropriate strategies for the optimal exploitation of groundwater resources.
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Affiliation(s)
- Ali Nasiri Khiavi
- Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran
| | - Raoof Mostafazadeh
- Department of Natural Resources and Member of Water Managements Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
| | - Maryam Adhami
- Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran
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Li P, Sabarathinam C, Elumalai V. Groundwater pollution and its remediation for sustainable water management. CHEMOSPHERE 2023; 329:138621. [PMID: 37031835 DOI: 10.1016/j.chemosphere.2023.138621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Affiliation(s)
- Peiyue Li
- School of Water and Environment, Chang'an University, No.126 Yanta Road, Xi'an, 710054, Shannxi, China.
| | - Chidambaram Sabarathinam
- Water Research Center, Kuwait Institute for Scientific Research, P.O. Box 24885, Safat, 13109, Kuwait
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Mitra R, Das J. A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:16036-16067. [PMID: 36180798 DOI: 10.1007/s11356-022-23168-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
In the Sub-Himalayan foothills region of eastern India, floods are considered the most powerful annually occurring natural disaster, which cause severe losses to the socio-economic life of the inhabitants. Therefore, the present study integrated geographic information system (GIS) and three comprehensive and systematic multicriteria decision-making (MCDM) techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) in Koch Bihar district for comparative assessment of the flood-susceptible zones. The multi-dimensional 21 indicators were considered, and multicollinearity statistics were employed to erase the issues regarding highly correlated parameters (i.e., MFI and long-term annual rainfall). Results of MCDM models depicted that the riparian areas and riverine "chars" (islands) are the most susceptible sectors, accounting for around 40% of the total area. The microlevel assessment revealed that flooding was most susceptible in the Tufanganj-I, Tufanganj-II, and Mathabhanga-I blocks, while Haldibari, Sitalkuchi, and Sitai blocks were less susceptible. Spearman's rank (rs) tests among the three MCDM models revealed that TOPSIS-EDAS persisted in a high correlation (rs = 0.714) in contrast to the relationships between VIKOR-EDAS (rs = 0.651) and TOPSIS-VIKOR (rs = 0.639). The model's efficiency was statistically judged by applying the receiver operating characteristic-area under the curve (ROC-AUC), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) techniques to recognize the better-suited models for mapping the flood susceptibility. The performance of all techniques is found good enough (ROC-AUC = > 0.700 and MAE, MSE and RMSE = < 0.300). However, TOPSIS and VIKOR have manifested an excellent outcome and are highly recommended for identifying flood susceptibility in such active flood-prone areas. Thus, this kind of study addresses the role of GIS in the construction of the flood susceptibility of the region and the performance of the respective models in a very lucid manner.
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Affiliation(s)
- Rajib Mitra
- Department of Geography and Applied Geography, University of North Bengal, PO- North Bengal University, Dist- Darjeeling, 734013, India
| | - Jayanta Das
- Department of Geography, Rampurhat College, PO- Rampurhat, Dist- Birbhum, 731224, India.
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