Ground Water Quality Analysis using Machine Learning Techniques: a Critical Appraisal | ||
| Journal of Mining and Environment | ||
| مقاله 2، دوره 15، شماره 2، تیر 2024، صفحه 419-426 اصل مقاله (554.85 K) | ||
| نوع مقاله: Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.13452.2484 | ||
| نویسندگان | ||
| Naman Chandel1؛ Sushindra Kumar Gupta* 2؛ Anand Kumar Ravi3 | ||
| 1Environmental Engineering, Department of Civil Engineering, Chandigarh University, Mohali, Punjab, India | ||
| 2Department of Civil Engineering, Chandigarh University, Mohali, Punjab, India | ||
| 3Health, Safety and Environmental Engineering, Department of Civil Engineering, Chandigarh University, Mohali, Punjab, India | ||
| چکیده | ||
| Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can be used to predict groundwater quality and leachate characteristics to manage this issue efficiently. This study proposes a machine learning-based model for the prediction of groundwater quality and leachate characteristics using the effective water quality index (EWQI). The leachate dataset used in this study was obtained from a landfill site, and the groundwater quality dataset was collected from literature review. The mean values of TDS, Ca, Mg, NO3-, and PO4- exceeded the prescribed limit for drinking water purposes. The proposed model utilizes a machine learning architecture based on a convolutional neural network (CNN) to extract relevant features from the input data. The extracted features are then fed into a fully connected network to estimate the EWQI of the input samples. The model, trained and tested on leachate and groundwater quality datasets, achieves a high accuracy and computational efficiency, aiding in predicting groundwater quality and leachate characteristics for waste management. | ||
| کلیدواژهها | ||
| Groundwater Quality؛ Machine Learning؛ Effective Water Quality Index (EWQI)؛ Support Vector Machines (SVM) | ||
| مراجع | ||
|
[1]. Raj, C., & Singh, V. (2021). Spatial and temporal variation of fluvial islands and sandbars in River Ganga from Bhagalpur to Farakka during 1955–2019. Sustainable Water Resources Management, 7(3), 38.
[2]. Dwivedi, S. L., & Pathak, V. (2007). A preliminary assignment of water quality index to Mandakini River, Chitrakoot. Indian Journal of Environmental Protection, 27(11), 1036.
[3]. Gupta, S. K., Singh, P. K., Tyagi, J., Sharma, G., & Jethoo, A. S. (2020). Rainstorm‐generated sediment yield model based on soil moisture proxies (SMP). Hydrological Processes, 34(16), 3448-3463.
[4]. Gupta, S. K., Tyagi, J., Sharma, G., Jethoo, A. S., & Singh, P. K. (2019). An event-based sediment yield and runoff modeling using soil moisture balance/budgeting (SMB) method. Water Resources Management, 33, 3721-3741.
[5]. Gupta, S. K., Tyagi, J., Singh, P. K., Sharma, G., & Jethoo, A. S. (2019). Soil Moisture Accounting (SMA) based sediment graph models for small watersheds. Journal of Hydrology, 574, 1129-1151.
[6]. Gupta, N. K., Jethoo, A. S., & Gupta, S. K. (2016). Rainfall and surface water resources of Rajasthan State, India. Water Policy, 18(2), 276-287.
[7]. Sihag, P., & Gupta, S. K. (2023). Discussion of “Prediction of Maximum Scour Depth near Spur Dikes in Uniform Bed Sediment Using Stacked Generalization Ensemble Tree-Based Frameworks” by Manish Pandey, Mehdi Jamei, Masoud Karbasi, Iman Ahmadianfar, and Xuefeng Chu. Journal of Irrigation and Drainage Engineering, 149(1), 07022019.
[8]. Gaikwad, S. K., Kadam, A. K., Ramgir, R. R., Kashikar, A. S., Wagh, V. M., Kandekar, A. M., ... & Kamble, K. D. (2020). Assessment of the groundwater geochemistry from a part of west coast of India using statistical methods and water quality index. HydroResearch, 3, 48-60.
[9]. He, S., Li, P., Wu, J., Elumalai, V., & Adimalla, N. (2020). Groundwater quality under land use/land cover changes: a temporal study from 2005 to 2015 in Xi’an, northwest China. Human and Ecological Risk Assessment: An International Journal, 26(10), 2771-2797.
[10]. Choque-Quispe, D., Froehner, S., Palomino-Rincón, H., Peralta-Guevara, D. E., Barboza-Palomino, G. I., Kari-Ferro, A., ... & Choque-Quispe, Y. (2022). Proposal of a water-quality index for high Andean basins: application to the Chumbao river, Andahuaylas, Peru. Water, 14(4), 654.
[11]. Mishra, A., & Lal, B. (2023). Assessment of groundwater quality in Ranchi district, Jharkhand, India, using water evaluation indices and multivariate statistics. Environmental Monitoring and Assessment, 195(4), 472.
[12]. Singh, K. P., Malik, A., Mohan, D., & Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study. Water research, 38(18), 3980-3992.
[13]. Chu, C., Ritter, W., & Sun, X. (2019). Spatial variances of water-energy nexus in China and its implications for provincial resource interdependence. Energy Policy, 125, 487-502.
[14]. Xu, H. S., Xu, Z. X., Wu, W., & Tang, F. F. (2012). Assessment and spatiotemporal variation analysis of water quality in the Zhangweinan River Basin, China. Procedia Environmental Sciences, 13, 1641-1652.
[15]. Singha, S., Pasupuleti, S., Singha, S. S., Singh, R., & Kumar, S. (2021). Prediction of groundwater quality using efficient machine learning technique. Chemosphere, 276, 130265.
[16]. El Bilali, A., Taleb, A., & Brouziyne, Y. (2021). Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agricultural Water Management, 245, 106625.
[17]. Spijker, J., Fraters, D., & Vrijhoef, A. (2021). A machine learning based modelling framework to predict nitrate leaching from agricultural soils across the Netherlands. Environmental Research Communications, 3(4), 045002.
[18]. Mishra, A., & Lal, B. (2023). Assessment of groundwater quality in Ranchi district, Jharkhand, India, using water evaluation indices and multivariate statistics. Environmental Monitoring and Assessment, 195(4), 472.
[19]. Knoll, L., Breuer, L., & Bach, M. (2020). Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning. Environmental Research Letters , 15 (6), 064004.
[20]. Elbeltagi, A., Pande, C. B., Kouadri, S., & Islam, A. R. M. T. (2022). Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India. Environmental Science and Pollution Research, 1-15.
[21]. Agrawal, P., Sinha, A., Kumar, S., Agarwal, A., Banerjee, A., Villuri, V. G. K., ... & Pasupuleti, S. (2021). Exploring artificial intelligence techniques for groundwater quality assessment. Water, 13(9), 1172.
[22]. Bedi, S., Samal, A., Ray, C., & Snow, D. (2020). Comparative evaluation of machine learning models for groundwater quality assessment. Environmental Monitoring and Assessment, 192, 1-23.
[23]. Raheja, H., Goel, A., & Pal, M. (2022). Prediction of groundwater quality indices using machine learning algorithms. Water Practice & Technology, 17(1), 336-351. | ||
|
آمار تعداد مشاهده مقاله: 283 تعداد دریافت فایل اصل مقاله: 727 |
||