Modifying the DRASTIC Model and Groundwater Vulnerability Assessment of Nitrate: A Case Study in Lopburi province
Keywords:
Assessment of Groundwater Vulnerability, Weights of Evidence, Nitrate, DRASTIC modelAbstract
The DRASTIC model is widely used to assess vulnerability to groundwater contamination. This research aims to improve the model to make it more accurate and user-friendly. To achieve this, the Weight of Evidence--WoE was used to enhance the DRASTIC model, and four factors affecting nitrate contamination in the groundwater of Lop Buri Province were added. These factors were land use, infiltration rate, distance from the river, and density of the waterway. To validate the model, nitrate analysis results from 358 groundwater wells with nitrates exceeding 45 mg/L were used, which were collected between 2015 and 2023. The dataset was divided into two parts: the first part (2015–2021) of 277 wells was used for modeling, and the second part (2022–2023) of 81 wells was used to validate the model using the Receiver Operating Characteristic--ROC method. The performance of the original DRASTIC model was compared with the improved model using the Area Under the Curve--AUC. The results show that the WoE-enhanced model has a higher accuracy with an AUC of 0.85, while the original model has an AUC of 0.52. The ROC analysis confirms that the WOE-enhanced model provides more accurate predictions compared to the original DRASTIC model.
References
Aller, L., Bennett, T., Jay, L. H., & Rebecca, P. J. (1987). Drastic: A standardized system for evaluating ground water pollution potential using hydrogeologic settings: EPA/600/2-87/035. Retrieved from https://bit.ly/46RuyJD
Armaş, I. (2011). Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania. Natural Hazards, 60(3), 937-950. doi:10.1007/s11069-011-9879-4
Cabello, P., Roldan, M. D., & Moreno-Vivian, C. (2004). Nitrate reduction and the nitrogen cycle in archaea. Microbiology (reading), 150(Pt 11), 3527-3546. doi:10.1099/mic.0.27303-0
Chotpantarat, S., Parkchai, T., & Wisitthammasri, W. (2020). Multivariate statistical analysis of Hydrochemical data and stable isotopes of groundwater contaminated with nitrate at Huay Sai Royal Development Study Center and Adjacent Areas in Phetchaburi Province, Thailand. Water, 12(4). doi:10.3390/w12041127
Department of Groundwater Resources. (2007). Groundwater exploration and mapping of limestone aquifer project: Area 2. Bangkok: Department of Groundwater Resources. (in Thai)
Department of Water Resources. (2023). Water source information. Retrieved from https://webgis.dwr.go.th/downloads
Ducci, D., Della Morte, R., Mottola, A., Onorati, G., & Pugliano, G. (2019). Nitrate trends in groundwater of the Campania region (southern Italy). Environmental Science and Pollution Research, 26(3), 2120-2131. doi:10.1007/s11356-017-0978-y
Elmeknassi, M., El Mandour, A., Elgettafi, M., Himi, M., Tijani, R., El Khantouri, F. A., & Casas, A. (2021). A GIS-based approach for geospatial modeling of groundwater vulnerability and pollution risk mapping in Bou-Areg and Gareb aquifers, Northeastern Morocco. Environmental Science and Pollution Research, 28(37), 51612-51631. doi:10.1007/s11356-021-14336-0
Fan, J., Upadhye, S., & Worster, A. (2006). Understanding receiver operating characteristic (ROC) curves. Canadian Journal of Emergency Medicine, 8(1), 19-20. doi:10.1017/s1481803500013336
Fraga, C. G., Melville, A. M., & Wright, B. W. (2007). ROC-curve approach for determining the detection limit of a field chemical sensor. Analyst, 132(3), 230-236. doi:10.1039/b607843e
Gajowniczek, K., & Ząbkowski, T. (2014). Estimating the ROC curve and its significance for classification models’ assessment. Quantitative Methods in Economics, 15(2), 382-391.
Goyal, D., Haritash, A. K., & Singh, S. K. (2021). A comprehensive review of groundwater vulnerability assessment using index-based, modelling, and coupling methods. Journal of Environmental Management, 296, 113161. doi: 10.1016/j.jenvman.2021.113161
Hamza, S. M., Ahsan, A., & Sulaiman, W. N. A. (2019). GIS-based spatial analysis accurately predicts alluvial well depletion and effectively establishes relationship between aquifer parameters. International Journal of Water Resources and Arid Environments, 8(1), 49-60.
Hoehn, E., & Scholtis, A. (2011). Exchange between a river and groundwater, assessed with hydrochemical data. Hydrology and Earth System Sciences, 15(3), 983-988. doi:10.5194/hess-15-983-2011
Huang, Y., Chang, Q., & Li, Z. (2018). Land use change impacts on the amount and quality of recharge water in the loess tablelands of China. Science Total Environ, 628, 443-452. doi: 10.1016/j.scitotenv.2018.02.076
Hussain, H. M. (2004). Assessment of groundwater vulnerability in an alluvial interfluve using GIS. (Doctoral dissertation). Indian Institute of Technology Roorkee. Roorkee.
Jia, Z., Bian, J., & Wang, Y. (2018). Impacts of urban land use on the spatial distribution of groundwater pollution, Harbin City, Northeast China. Journal of Contaminant Hydrology, 215, 29-38. doi: 10.1016/j.jconhyd.2018.06.005
Jia, Z., Bian, J., Wang, Y., Wan, H., Sun, X., & Li, Q. (2019). Assessment and validation of groundwater vulnerability to nitrate in porous aquifers based on a DRASTIC method modified by projection pursuit dynamic clustering model. Journal of Contaminant Hydrology, 226, 103522. doi: 10.1016/j.jconhyd.2019.103522
Khosravi, K., Sartaj, M., Karimi, M., Levison, J., & Lotfi, A. (2021). A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models. Environmental Science and Pollution Research, 28(36), 50525-50541. doi: 10.1007/s11356-021-13706-y
Lakshminarayanan, B., Ramasamy, S., Anuthaman, S. N., & Karuppanan, S. (2021). New DRASTIC framework for groundwater vulnerability assessment: bivariate and multi-criteria decision-making approach coupled with metaheuristic algorithm. Environmental Science and Pollution Research, 29(3), 4474-4496. doi:10.1007/s11356-021-15966-0
Lee, C. M., Choi, H., Kim, Y., Kim, M., Kim, H., & Hamm, S. Y. (2021). Characterizing land use effect on shallow groundwater contamination by using self-organizing map and buffer zone. Science of The Total Environment, 800, 149632. doi: 10.1016/j.scitotenv.2021.149632
Nakagawa, K., Amano, H., Persson, M., & Berndtsson, R. (2021). Spatiotemporal variation of nitrate concentrations in soil and groundwater of an intensely polluted agricultural area. Scientifc Reports, 11, 2598. https://doi.org/10.1038/s41598-021-82188-2
Navarro, J., Teramoto, E. H., Engelbrecht, B. Z., & Kiang, C. H. (2020). Assessing hydrofacies and hydraulic properties of basaltic aquifers derived from geophysical logging. Brazilian Journal of Geology, 50(4). doi:10.1590/2317-4889202020200013
O’Driscoll, M., Johnson, P., & Mallinson, D. (2010). Geological controls and effects of floodplain asymmetry on river–groundwater interactions in the southeastern Coastal Plain, USA. Hydrogeology Journal, 18(5), 1265-1279. doi:10.1007/s10040-010-0595-z
Piscopo, G. (2001). Groundwater vulnerability map explanatory notes. Centre for Natural Resources NSW Department of Land and Water Conservation: Lachlan Catchment. Retrieved from https://water.dpie.nsw.gov.au/__data/assets/pdf_file/0004/151762/Castlereagh-map-notes.pdf
Pourghasemi, H. R., Pradhan, B., Gokceoglu, C., Mohammadi, M., & Moradi, H. R. (2012). Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences, 6(7), 2351-2365. doi: 10.1007/s12517-012-0532-7
Rahmati, O., Pourghasemi, H. R., & Zeinivand, H. (2015). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42-70. doi:10.1080/10106049.2015.1041559
Sarakarn, P., & Munpolsri, P. (2021). Optimal cut-off points for receiver operating characteristic (ROC) curve analysis in developing tools of health innovations:example using STATA. Thai Bulletin of Pharmaceutical Sciences, 16(1), 93-108 (in Thai)
Trauth, N., Musolff, A., Knoller, K., Kaden, U. S., Keller, T., Werban, U., & Fleckenstein, J. H. (2018). River water infiltration enhances denitrification efficiency in riparian groundwater. Water research, 130, 185-199. doi: 10.1016/j.watres.2017.11.058
Udomsri, S., & Hoontrakul, K. (n.d). Surface texture of the soil series in Thailand. Bangkok: Land Development Departmen. (in Thai)
U.S. Geological Survey. (2014). Digital Elevation Model (DEM). Retrieved from https://earthexplorer.usgs.gov/.
Wang, K., Chen, H., Li, F., Guo, F., & Jiang, G. (2020). Spatial distribution characteristics of nitrogen pollution in a typical karst groundwater system. Arabian Journal of Geosciences, 13(10), 351. https://doi.org/10.1007/s12517-020-05401-y
