SpringerLink
Log in

GIS-Based Modelling for Estimation of Water Quality Parameters: A Review

  • Chapter
  • First Online:
Geospatial Analytics for Environmental Pollution Modeling

Abstract

This chapter offers a comprehensive review of geographic information system (GIS)-based approaches for estimating water quality parameters. It highlights the advantages of using GIS such as integrating satellite imagery and spatial data and conducting spatial analysis. The chapter emphasizes the significance of water quality monitoring and the limitations of traditional analysis methods. It explores various types of GIS-based models, including empirical, process-based, and hybrid models. Additionally, it suggests the use of remote sensing and machine learning techniques, such as deep learning, for more accurate and timely water quality forecasting. The chapter covers the estimation of both optically active and inactive parameters through remote sensing. It summarizes previous studies utilizing GIS-based approaches, including machine learning, for water quality estimation. The limitations and challenges, such as uncertainty and validation, are discussed, along with recommendations for future research. The chapter highlights the potential of GIS-based modelling in improving water quality management and stresses the importance of interdisciplinary collaboration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Abayazid, H. O., & El-Adawy, A. (2019). Assessment of a nonoptical water quality property using space-based imagery in Egyptian coastal lake. International Journal of Advanced Network, Monitoring and Controls, 4(3), 53–64.

    Article  Google Scholar 

  • Adimalla, N., & Taloor, A. K. (2020). Hydrogeochemical investigation of groundwater quality in the hard rock terrain of South India using Geographic Information System (GIS) and groundwater quality index (GWQI) techniques. Groundwater for Sustainable Development, 10, 100288. https://doi.org/10.1016/j.gsd.2019.100288

    Article  Google Scholar 

  • Ahmed, A. A. M. (2022). Development of deep learning hybrid models for hydrological predictions. Doctoral dissertation, University of Southern Queensland.

    Google Scholar 

  • Ahn, Y. H., Shanmugam, P., Ryu, J. H., & Jeong, J. C. (2006). Satellite detection of harmful algal bloom occurrences in Korean waters. Harmful Algae, 5(2), 213–231. https://doi.org/10.1016/j.hal.2005.07.007

    Article  CAS  Google Scholar 

  • Alam, M. J. B., Islam, M. R., Muyen, Z., Mamun, M., & Islam, S. (2007). Water quality parameters along rivers. International journal of Environmental Science and Technology, 4, 159–167.

    Article  CAS  Google Scholar 

  • Aldhyani, T. H. H., Al-Yaari, M., Alkahtani, H., & Maashi, M. (2020). Water quality prediction using artificial intelligence algorithms. Applied Bionics and Biomechanics, 2020. https://doi.org/10.1155/2020/6659314

  • Aleynik, D., Dale, A. C., Porter, M., & Davidson, K. (2016). A high resolution hydrodynamic model system suitable for novel harmful algal bloom modelling in areas of complex coastline and topography. Harmful Algae, 53, 102–117. https://doi.org/10.1016/j.hal.2015.11.012

    Article  Google Scholar 

  • Anderson, D. M. (2009). Approaches to monitoring, control and management of harmful algal blooms (HABs). Ocean and Coastal Management, 52(7), 342–347. https://doi.org/10.1016/j.ocecoaman.2009.04.006

    Article  Google Scholar 

  • Anoh, K. A., Jourda, J. P., Kouamé, K. J., Koua, T. J. J., Eba, A. E., & Lazar, G. (2012). Demarcation of protection perimeters for surface waters of Taabo (Ivory Coast) watershed using gis and multicriteria analysis. Environmental Engineering and Management Journal, 11(12), 2123–2131. https://doi.org/10.30638/eemj.2012.264

    Article  Google Scholar 

  • APHA. (1995). Standard methods for the examination of water and waste water (19th ed.). American Public Health Association.

    Google Scholar 

  • Arora, M., Mudaliar, A., & Pateriya, B. (2022). Assessment and monitoring of optically active water quality parameters on wetland ecosystems based on remote sensing approach: A case study on Harike and Keshopur wetland over Punjab region. India. Engineering Proceedings, 27(1), 84.

    Google Scholar 

  • Bahrami, M., & Zarei, A. R. (2023). Assessment and modelling of groundwater quality for drinking, irrigation, and industrial purposes using water quality indices and GIS technique in fasarud aquifer (Iran). Modelling Earth Systems and Environment, 1–15. https://doi.org/10.1007/S40808-023-01725-2/METRICS

  • Balch, W. M., Eppley, R. W., Abbott, M. R., & Reid, F. M. H. (1989). Bias in satellite-derived pigment measurements due to coccolithophores and dinoflagellates. Journal of Plankton Research, 11(3), 575–581.

    Article  Google Scholar 

  • Bouchard, D., Knightes, C., Chang, X., & Avant, B. (2017). Simulating multiwalled carbon nanotube transport in surface water systems using the water quality analysis simulation program (WASP). Environmental Science and Technology, 51(19), 11174–11184. https://doi.org/10.1021/ACS.EST.7B01477

    Article  CAS  Google Scholar 

  • Bukata, R. P. (2013). Retrospection and introspection on remote sensing of inland water quality: “Like Déjà Vu All Over Again”. Journal of Great Lakes Research, 39, 2–5.

    Article  Google Scholar 

  • Cha, Y., Park, S. S., Kim, K., Byeon, M., & Stow, C. A. (2014). Water Resources Research, 5375–5377. https://doi.org/10.1002/2013WR014979

  • Chen, Z., Hu, C., Conmy, R. N., Muller-Karger, F., & Swarzenski, P. (2007). Colored dissolved organic matter in Tampa Bay, Florida. Marine Chemistry, 104(1–2), 98–109.

    Article  CAS  Google Scholar 

  • Cusack, C., Dabrowski, T., Lyons, K., Berry, A., Westbrook, G., Salas, R., Duffy, C., Nolan, G., & Silke, J. (2016). Harmful algal bloom forecast system for SW Ireland. Part II: Are operational oceanographic models useful in a HAB warning system. Harmful Algae, 53, 86–101. https://doi.org/10.1016/j.hal.2015.11.013

    Article  Google Scholar 

  • Demlie, M. (2015). Assessment and estimation of groundwater recharge for a catchment located in highland tropical climate in Central Ethiopia using catchment soil–water balance (SWB) and chloride mass balance (CMB) techniques. Environmental Earth Sciences, 74(2), 1137–1150. https://doi.org/10.1007/S12665-015-4099-Y/METRICS

    Article  Google Scholar 

  • Dodds, W. K., Bouska, W. W., Eitzmann, J. L., Pilger, T. J., Pitts, K. L., Riley, A. J., Schloesser, J. T., & Thornbrugh, D. J. (2009). Eutrophication of U. S. freshwaters: Analysis of potential economic damages. Environmental Science and Technology, 43(1), 12–19. https://doi.org/10.1021/es801217q

    Article  CAS  Google Scholar 

  • Donaghay, P. L., & Osborn, T. R. (1997). Toward a theory of biological-physical control of harmful algal bloom dynamics and impacts. Limnology and Oceanography, 42(5), 1283–1296. https://doi.org/10.4319/lo.1997.42.5_part_2.1283

    Article  Google Scholar 

  • Donia, N., & Hussein, M. (2004). Eutrophication Assessment of Lake Manzala Using Gis Techniques. Eighth International Water Technology Conference, IWTC8, 393–408.

    Google Scholar 

  • Ecol, M., Ser, P., Tang, D.-L, Ni, I-Hsun, Ester, D. R., & Muller-Karger, F. E. (1999). MARINE ECOLOGY PROGRESS SERIES remote sensing observations of winter phytoplankton blooms southwest of the Luzon Strait in the South China Sea. December 1979.

    Google Scholar 

  • Farjad, B., Gupta, A., & Marceau, D. J. (2016). Annual and seasonal variations of hydrological processes under climate change scenarios in two sub-catchments of a complex watershed. Water Resources Management, 30(8), 2851–2865. https://doi.org/10.1007/S11269-016-1329-3/METRICS

    Article  Google Scholar 

  • Fleming, L. E., Kirkpatrick, B., Backer, L. C., Walsh, C. J., Nierenberg, K., Clark, J., Reich, A., Hollenbeck, J., Benson, J., Cheng, Y. S., Naar, J., Pierce, R., Bourdelais, A. J., Abraham, W. M., Kirkpatrick, G., Zaias, J., Wanner, A., Mendes, E., Shalat, S., et al. (2011). Review of Florida red tide and human health effects. Harmful Algae, 10(2), 224–233. https://doi.org/10.1016/j.hal.2010.08.006

    Article  Google Scholar 

  • Gaafar, M., Mahmoud, S. H., Gan, T. Y., & Davies, E. G. R. (2020). A practical GIS-based hazard assessment framework for water quality in stormwater systems. Journal of Cleaner Production, 245, 118855.

    Article  CAS  Google Scholar 

  • Ghanbari, M., Jami, M., Domig, K. J., & Kneifel, W. (2012). Long-term effects of water pH changes on hematological parameters in the common carp (Cyprinus carpio L.). African Journal of Biotechnology, 11(13), 3153–3159.

    CAS  Google Scholar 

  • Glibert, P. M., Harrison, J., Heil, C., & Seitzinger, S. (2006). Escalating worldwide use of urea – A global change contributing to coastal eutrophication. Biogeochemistry, 77(3), 441–463. https://doi.org/10.1007/s10533-005-3070-5

    Article  CAS  Google Scholar 

  • Gorde, S. P., & Jadhav, M. V. (2013). Assessment of water quality parameters: A review. Journal of Engineering and Applied Science, 3(6), 2029–2035.

    Google Scholar 

  • Gower, J. F. R., Brown, L., & Borstad, G. A. (2004). Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Canadian Journal of Remote Sensing, 30(1), 17–25.

    Article  Google Scholar 

  • Grunwald, S., & Qi, C. (2006). GIS-based water quality modelling in the Sandusky watershed, Ohio, USA. JAWRA Journal of the American Water Resources Association, 42(4), 957–973.

    Article  CAS  Google Scholar 

  • Guéguen, C., Guo, L., & Tanaka, N. (2005). Distributions and characteristics of colored dissolved organic matter in the Western Arctic Ocean. Continental Shelf Research, 25(10), 1195–1207.

    Article  Google Scholar 

  • Guo, H., Huang, J. J., Chen, B., Guo, X., & Singh, V. P. (2021). A machine learning-based strategy for estimating nonoptically active water quality parameters using Sentinel-2 imagery. International Journal of Remote Sensing, 42(5), 1841–1866.

    Article  Google Scholar 

  • Gupta, N., Aktaruzzaman, M., & Wang, C. (2012). GIS-based assessment and Management of Nitrogen and Phosphorus in Rönneå River Catchment, Sweden. Journal of the Indian Society of Remote Sensing, 40(3), 457–466. https://doi.org/10.1007/s12524-011-0167-2

    Article  Google Scholar 

  • Gurlin, D., Gitelson, A. A., & Moses, W. J. (2011). Remote estimation of chl-a concentration in turbid productive waters—Return to a simple two-band NIR-red model? Remote Sensing of Environment, 115(12), 3479–3490.

    Article  Google Scholar 

  • Hameed, H. (2010). GIS as a tool for classification Lake’s acidification-and eutrophication degree. Mesopotamian Journal of Marine Science, 25(1), 53–64.

    Article  Google Scholar 

  • Hiscock, J. G., Thourot, C. S., & Zhang, J. (2003). Phosphorus budget – Land use relationships for the northern Lake Okeechobee watershed, Florida. Ecological Engineering, 21(1), 63–74. https://doi.org/10.1016/j.ecoleng.2003.09.005

    Article  Google Scholar 

  • Howartw, R. W., Billen, G., Swaney, D., & Townsend, A. (1996). Regional nitrogen budgets and riverine N and P fluxes for the drainages to the North Atlantic Ocean: Natural and human influences. Biogeochemistry, 35(1), 75–139.

    Article  Google Scholar 

  • Hu, C., Muller-karger, F. E., Judd, C., Carder, K. L., Kelble, C., Johns, E., & Heil, C. A. (2005). Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sensing of Environment, 97, 311–321. https://doi.org/10.1016/j.rse.2005.05.013

    Article  Google Scholar 

  • Izadi, M., Sultan, M., El Kadiri, R., Ghannadi, A., & Abdelmohsen, K. (2021). A remote sensing and machine learning-based approach to forecast the onset of harmful algal bloom. Remote Sensing, 13(19). https://doi.org/10.3390/rs13193863

  • Jakeman, A. J., Barreteau, O., Hunt, R. J., Rinaudo, J. D., & Ross, A. (2016). Integrated groundwater management: Concepts, approaches and challenges. Integrated Groundwater Management: Concepts, Approaches and Challenges, 1–762. https://doi.org/10.1007/978-3-319-23576-9/COVER

  • Jenifer, M. A., & Jha, M. K. (2022). A novel GIS-based modelling approach for evaluating aquifer susceptibility to anthropogenic contamination. Sustainability, 14(8), 4538.

    Article  CAS  Google Scholar 

  • Jeong, S., Yeon, K., Hur, Y., & Oh, K. (2010). Salinity intrusion characteristics analysis using EFDC model in the downstream of Geum River. Journal of Environmental Sciences, 22(6), 934–939. https://doi.org/10.1016/S1001-0742(09)60201-1

    Article  Google Scholar 

  • Karul, C., Soyupak, S., Çilesiz, A. F., Akbay, N., & Germen, E. (2000). Case studies on the use of neural networks in eutrophication modelling. Ecological Modelling, 134(2–3), 145–152. https://doi.org/10.1016/S0304-3800(00)00360-4

    Article  CAS  Google Scholar 

  • KC, A., Chalise, A., Parajuli, D., Dhital, N., Shrestha, S., & Kandel, T. (2019). Surface water quality assessment using remote sensing, GIS and artificial intelligence. Technical Journal, 1(1), 113–122.

    Article  Google Scholar 

  • Kurup, R. G., Hamilton, D. P., & Phillips, R. L. (2000). Comparison of two 2-dimensional, laterally averaged hydrodynamic model applications to the Swan River Estuary. Mathematics and Computers in Simulation, 51(6), 627–638. https://doi.org/10.1016/S0378-4754(99)00146-9

    Article  Google Scholar 

  • Landsberg, J. H. (2002). The effects of harmful algal blooms on aquatic organisms. Reviews in Fisheries Science, 10(2), 113–390.

    Article  Google Scholar 

  • Lee, J. H. W., Huang, Y., Dickman, M., & Jayawardena, A. W. (2003). Neural network modelling of coastal algal blooms. Ecological Modelling, 159(2–3), 179–201. https://doi.org/10.1016/S0304-3800(02)00281-8

    Article  CAS  Google Scholar 

  • Lehning, D. W., Corradini, K. J., Petersen, G. W., Nizeyimana, E., Hamlett, J. M., Robillard, P. D., & Day, R. L. (2002). A comprehensive GIS-based modelling approach for predicting nutrient loads in watersheds. Journal of Spatial Hydrology, 2(2).

    Google Scholar 

  • Lei, B., Huang, S., Qiao, M., Li, T., & Wang, Z. (2008). Prediction of the environmental fate and aquatic ecological impact of nitrobenzene in the Songhua River using the modified AQUATOX model. Journal of Environmental Sciences, 20(7), 769–777. https://doi.org/10.1016/S1001-0742(08)62125-7

    Article  CAS  Google Scholar 

  • Li, L., Wu, J., Wang, X., Zhou, H. L., & Fang, B. (2007). Application of the three-dimensional environmental fluid dynamics code model in Manwan reservoir. New Trends in Fluid Mechanics Research, 414–414. https://doi.org/10.1007/978-3-540-75995-9_135

  • Lloyd, D. S. (1987). Turbidity as a water quality standard for salmonid habitats in Alaska. North American Journal of Fisheries Management, 7(1), 34–45.

    Article  Google Scholar 

  • Lu, F., Zhang, H., & Liu, W. (2020). Development and application of a GIS-based artificial neural network system for water quality prediction: A case study at the Lake Champlain area. Journal of Oceanology and Limnology, 38, 1835–1845.

    Article  Google Scholar 

  • Mathew, M. M., Srinivasa Rao, N., & Mandla, V. R. (2017). Development of regression equation to study the Total Nitrogen, Total Phosphorus and Suspended Sediment using remote sensing data in Gujarat and Maharashtra coast of India. Journal of Coastal Conservation, 21, 917–927.

    Article  Google Scholar 

  • Mbuh, M. J., Mbih, R., & Wendi, C. (2019). Water quality modelling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed. Physical Geography, 40(2), 127–148. https://doi.org/10.1080/02723646.2018.1507339

    Article  Google Scholar 

  • McGillicuddy, D. J., Anderson, D. M., Lynch, D. R., & Townsend, D. W. (2005). Mechanisms regulating large-scale seasonal fluctuations in Alexandrium fundyense populations in the Gulf of Maine: Results from a physical-biological model. Deep-Sea Research Part II: Topical Studies in Oceanography, 52(19–21 SPEC. ISS), 2698–2714. https://doi.org/10.1016/j.dsr2.2005.06.021

    Article  Google Scholar 

  • Mohammed, M. A. A., Khleel, N. A. A., Szabó, N. P., & Szűcs, P. (2022). Modelling of groundwater quality index by using artificial intelligence algorithms in northern Khartoum State. Modelling Earth Systems and Environment. https://doi.org/10.1007/s40808-022-01638-6

    Book  Google Scholar 

  • Moses, S. A., Janaki, L., Joseph, S., & Joseph, J. (2015). Water quality prediction capabilities of WASP model for a tropical lake system. Lakes and Reservoirs: Research and Management, 20(4), 285–299. https://doi.org/10.1111/LRE.12110

    Article  CAS  Google Scholar 

  • Mushtaq, F., & Nee Lala, M. G. (2017). Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery. Geocarto International, 32(3), 274–285.

    Article  Google Scholar 

  • Mushtaq, F., Nee Lala, M. G., & Mantoo, A. G. (2022). Trophic State assessment of a freshwater Himalayan Lake using Landsat 8 OLI satellite imagery: A case study of Wular Lake, Jammu and Kashmir (India). Earth and Space Science, 9(3), e2021EA001653.

    Article  Google Scholar 

  • Niculae, M.-I., Avram, S., Corpade, A.-M., Dedu, S., Gheorghe, C. A., Pascu, I. S., Ontel, I., & Rodino, S. (2021). Evaluation of the quality of lentic ecosystems in Romania by a GIS based WRASTIC model. Scientific Reports, 11(1), 1–10.

    Article  Google Scholar 

  • Noori, N., Kalin, L., & Isik, S. (2020). Water quality prediction using SWAT-ANN coupled approach. Journal of Hydrology, 590. https://doi.org/10.1016/J.JHYDROL.2020.125220

  • Park, R. A., Clough, J. S., & Wellman, M. C. (2008). AQUATOX: Modelling environmental fate and ecological effects in aquatic ecosystems. Ecological Modelling, 213(1), 1–15. https://doi.org/10.1016/J.ECOLMODEL.2008.01.015

    Article  CAS  Google Scholar 

  • Patil, P. N., Sawant, D. V., & Deshmukh, R. N. (2012). Physico-chemical parameters for testing of water–A review. International Journal of Environmental Sciences, 3(3), 1194–1207.

    CAS  Google Scholar 

  • Pisinaras, V., Petalas, C., Gikas, G. D., Gemitzi, A., & Tsihrintzis, V. A. (2010). Hydrological and water quality modelling in a medium-sized basin using the Soil and Water Assessment Tool (SWAT). Desalination, 250(1), 274–286. https://doi.org/10.1016/J.DESAL.2009.09.044

    Article  CAS  Google Scholar 

  • Presented, E. E., Fulfillment, P., Science, A., & Engineering, C. (2015, April). Concordia University A GIS based modelling approach to assess lake eutrophication.

    Google Scholar 

  • Raine, R., McDermott, G., Silke, J., Lyons, K., Nolan, G., & Cusack, C. (2010). A simple short range model for the prediction of harmful algal events in the bays of southwestern Ireland. Journal of Marine Systems, 83(3–4), 150–157. https://doi.org/10.1016/j.jmarsys.2010.05.001

    Article  Google Scholar 

  • Raine, R., O’Boyle, S., O’Higgins, T., White, M., Patching, J., Cahill, B., & McMahon, T. (2001). A satellite and field portrait of a Karenia mikimotoi bloom off the south coast of Ireland, August 1998. Hydrobiologia, 465, 187–193. https://doi.org/10.1023/A:1014524420705

    Article  Google Scholar 

  • Rajaee, T., Khani, S., & Ravansalar, M. (2020). Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. In Chemometrics and intelligent laboratory systems (Vol. 200). https://doi.org/10.1016/j.chemolab.2020.103978

    Chapter  Google Scholar 

  • Rawat, K. S., & Singh, S. K. (2018). Water quality indices and GIS-based evaluation of a decadal groundwater quality. Geology, Ecology, and Landscapes, 2(4), 240–255.

    Article  Google Scholar 

  • Recknagel, F., French, M., Harkonen, P., & Yabunaka, K. I. (1997). Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling, 96(1–3), 11–28. https://doi.org/10.1016/S0304-3800(96)00049-X

    Article  CAS  Google Scholar 

  • Ritchie, J. C., Zimba, P. V., & Everitt, J. H. (2003). Remote sensing techniques to assess water quality. Photogrammetric Engineering & Remote Sensing, 69(6), 695–704.

    Article  Google Scholar 

  • Ringwood, A. H., & Keppler, C. J. (2002). Water quality variation and clam growth: Is pH truly a nonissue in estuaries? Estuaries, 25, 901–907.

    Article  Google Scholar 

  • Rudd, M. A., Moore, A. F. P., Rochberg, D., Bianchi-Fossati, L., Brown, M. A., D’Onofrio, D., Furman, C. A., Garcia, J., Jordan, B., Kline, J., Risse, L. M., Yager, P. L., Abbinett, J., Alber, M., Bell, J. E., Bhedwar, C., Cobb, K. M., Cohen, J., Cox, M., et al. (2018). Climate research priorities for policy-makers, practitioners, and scientists in Georgia, USA. Environmental Management 2018 62:2, 62(2), 190–209. https://doi.org/10.1007/S00267-018-1051-4

    Article  Google Scholar 

  • Ruhela, M., Sharma, K., Bhutiani, R., Chandniha, S. K., Kumar, V., Tyagi, K., Ahamad, F., & Tyagi, I. (2022). GIS-based impact assessment and spatial distribution of air and water pollutants in mining area. Environmental Science and Pollution Research, 29(21), 31486–31500. https://doi.org/10.1007/S11356-021-18009-W/METRICS

    Article  CAS  Google Scholar 

  • Seo, D., Kim, M., & Ahn, J. H. (2012). Prediction of chlorophyll-a changes due to weir constructions in the Nakdong River using EFDC-WASP modelling. Environmental Engineering Research, 17(2), 95–102.

    Article  Google Scholar 

  • Shabani, A., Woznicki, S. A., Mehaffey, M., Butcher, J., Wool, T. A., & Whung, P. Y. (2021). A coupled hydrodynamic (HEC-RAS 2D) and water quality model (WASP) for simulating flood-induced soil, sediment, and contaminant transport. Journal of Flood Risk Management, 14(4). https://doi.org/10.1111/JFR3.12747

  • Shammaa, Y., & Zhu, D. Z. (2001). Techniques for controlling total suspended solids in stormwater runoff. Canadian Water Resources Journal, 26(3), 359–375.

    Article  Google Scholar 

  • Shen, L., Xu, H., & Guo, X. (2012). Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework. Sensors (Switzerland), 12(6), 7778–7803. https://doi.org/10.3390/s120607778

    Article  CAS  Google Scholar 

  • Sheng, Z. (2013). Impacts of groundwater pum** and climate variability on groundwater availability in the Rio Grande Basin. Ecosphere, 4(1), 1–25. https://doi.org/10.1890/ES12-00270.1

    Article  Google Scholar 

  • Singh, S. K., Srivastava, P. K., Pandey, A. C., & Gautam, S. K. (2013). Integrated assessment of groundwater influenced by a Confluence River system: Concurrence with remote sensing and geochemical modelling. Water Resources Management, 27(12), 4291–4313. https://doi.org/10.1007/S11269-013-0408-Y/METRICS

    Article  Google Scholar 

  • Skogen, M. D., Svendsen, E., Berntsen, J., Aksnes, D., & Ulvestad, K. B. (1995). Modelling the primary production in the North Sea using a coupled three-dimensional physical-chemical-biological ocean model. Estuarine, Coastal and Shelf Science, 41(5), 545–565. https://doi.org/10.1016/0272-7714(95)90026-8

    Article  CAS  Google Scholar 

  • Slonecker, E. T., Jones, D. K., & Pellerin, B. A. (2016). The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM). Marine Pollution Bulletin, 107(2), 518–527.

    Article  CAS  Google Scholar 

  • Stigter, T. Y., Ribeiro, L., & Carvalho Dill, A. M. M. (2006). Application of a groundwater quality index as an assessment and communication tool in agro-environmental policies – Two Portuguese case studies. Journal of Hydrology, 327(3–4), 578–591. https://doi.org/10.1016/j.jhydrol.2005.12.001

    Article  Google Scholar 

  • Stumpf, R. P., Culver, M. E., Tester, P. A., Tomlinson, M., Kirkpatrick, G. J., Pederson, B. A., Truby, E., Ransibrahmanakul, V., & Soracco, M. (2003). Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data. Harmful Algae, 2(October 2001), 147–160.

    Article  CAS  Google Scholar 

  • Stumpf, R. P., & Tomlinson, M. C. (2005). Use of remote sensing in monitoring and forecasting of harmful algal blooms. Remote Sensing of the Coastal Oceanic Environment, 5885, 58850I. https://doi.org/10.1117/12.614376

    Article  Google Scholar 

  • Tamene, L., Park, S. J., Dikau, R., & Vlek, P. L. G. (2006). Reservoir siltation in the semi-arid highlands of northern Ethiopia: Sediment yield–catchment area relationship and a semi-quantitative approach for predicting sediment yield. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 31(11), 1364–1383.

    Article  Google Scholar 

  • Tang, D. L., Kawamura, H., Hai, D. N., & Takahashi, W. (2004). Remote sensing oceanography of a harmful algal bloom off the coast of southeastern Vietnam. Journal of Geophysical Research: Oceans, 109(3), 1–7. https://doi.org/10.1029/2003jc002045

    Article  Google Scholar 

  • Tiwari, S., Babbar, R., & Kaur, G. (2018). Performance evaluation of two ANFIS models for predicting water quality index of River Satluj (India). Advances in Civil Engineering, 2018.

    Google Scholar 

  • Troudi, N., Hamzaoui-Azaza, F., Tzoraki, O., Melki, F., & Zammouri, M. (2020). Assessment of groundwater quality for drinking purpose with special emphasis on salinity and nitrate contamination in the shallow aquifer of Guenniche (Northern Tunisia). Environmental Monitoring and Assessment, 192(10), 1–19. https://doi.org/10.1007/S10661-020-08584-9/METRICS

    Article  Google Scholar 

  • Ullrich, A., & Volk, M. (2009). Application of the Soil and Water Assessment Tool (SWAT) to predict the impact of alternative management practices on water quality and quantity. Agricultural Water Management, 96(8), 1207–1217. https://doi.org/10.1016/J.AGWAT.2009.03.010

    Article  Google Scholar 

  • Van Soesbergen, A. J. J., & Mulligan, M. (2014). Modelling multiple threats to water security in the Peruvian Amazon using the WaterWorld policy support system. Earth System Dynamics, 5(1), 55–65. https://doi.org/10.5194/ESD-5-55-2014

    Article  Google Scholar 

  • Vogiatzakis, I. N. (2003). GIS-based modelling and ecology: A review of tools and methods. Department of Geography, University of Reading.

    Google Scholar 

  • Wang, J., Chen, Z., & Wu, J. (2004). Stream water quality models and its development trend. Journal of Anhui Normal University (Natural Science), 27(3), 242–247.

    CAS  Google Scholar 

  • Wang, P., Yao, J., Wang, G., Hao, F., Shrestha, S., Xue, B., et al. (2019). Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants. Science of the Total Environment, 693, 133440.

    Article  CAS  Google Scholar 

  • Wu, C., Wu, J., Qi, J., Zhang, L., Huang, H., Lou, L., & Chen, Y. (2010). Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data. International Journal of Remote Sensing, 31(9), 2309–2324.

    Article  Google Scholar 

  • Xu, F., Tao, S., Dawson, R. W., & Li, B. (2001). A GIS-based method of lake eutrophication assessment. Ecological Modelling, 144, 231–244.

    Article  CAS  Google Scholar 

  • Zeiger, S. J., Owen, M. R., & Pavlowsky, R. T. (2021). Simulating nonpoint source pollutant loading in a karst basin: A SWAT modelling application. Science of the Total Environment, 785, 147295.

    Article  CAS  Google Scholar 

  • Żelazny, M., Bryła, M., Ozga-Zielinski, B., & Walczykiewicz, T. (2023). Applicability of the WASP model in an assessment of the impact of anthropogenic pollution on water quality—Dunajec River case study. Sustainability, 15(3), 2444. https://doi.org/10.3390/SU15032444

    Article  Google Scholar 

  • Zhang, B., Qin, Y., Huang, M., Sun, Q., Li, S., Wang, L., & Yu, C. (2011). SD–GIS-based temporal–spatial simulation of water quality in sudden water pollution accidents. Computers and Geosciences, 37(7), 874–882.

    Article  CAS  Google Scholar 

  • Zhang, R., Qian, X., Yuan, X., Ye, R., **a, B., & Wang, Y. (2012). Simulation of water environmental capacity and pollution load reduction using QUAL2K for water environmental management. International Journal of Environmental Research and Public Health, 9(12), 4504–4521.

    Article  CAS  Google Scholar 

  • Zhang, H., Xue, B., Wang, G., Zhang, X., & Zhang, Q. (2022). Deep learning-based water quality retrieval in an impounded lake using landsat 8 imagery: An application in Dong** lake. Remote Sensing, 14(18), 4505.

    Google Scholar 

  • Zhu, S., Zhang, Z., & Liu, X. (2017). Enhanced two dimensional hydrodynamic and water quality model (CE-QUAL-W2) for simulating mercury transport and cycling in water bodies. Water, 9(9), 643. https://doi.org/10.3390/W9090643

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia for overall support to complete this book chapter.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this book chapter.

Author information

Authors and Affiliations

  1. Department of Oceanography, Noakhali Science and Technology University, Noakhali, Bangladesh

    Jaber Bin Abdul Bari

  2. Applied Research Center for Environment and Marine Studies, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

    Surya Prakash Tiwari, Alaeldeen Ibrahim Elhaj, Abdurrahman Siddiqui, Omer Rehman Reshi & Syed Masiur Rahman

  3. Department of Geography and Environmental Studies University of Chittagong, Chittagong, Bangladesh

    Bijoy Mitra

Authors
  1. Jaber Bin Abdul Bari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surya Prakash Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bijoy Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alaeldeen Ibrahim Elhaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdurrahman Siddiqui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Omer Rehman Reshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Syed Masiur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Surya Prakash Tiwari .

Editor information

Editors and Affiliations

  1. Department of Ecology, Environment and Remote Sensing, Government of Jammu and Kashmir, Srinagar, Jammu and Kashmir, India

    Fayma Mushtaq

  2. Department of Ecology, Environment and Remote Sensing, Government of Jammu and Kashmir, Srinagar, Jammu and Kashmir, India

    Majid Farooq

  3. LeadsConnect Services Pvt Ltd, Noida, Uttar Pradesh, India

    Alok Bhushan Mukherjee

  4. Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

    Mili Ghosh Nee Lala

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bari, J.B.A. et al. (2023). GIS-Based Modelling for Estimation of Water Quality Parameters: A Review. In: Mushtaq, F., Farooq, M., Mukherjee, A.B., Ghosh Nee Lala, M. (eds) Geospatial Analytics for Environmental Pollution Modeling. Springer, Cham. https://doi.org/10.1007/978-3-031-45300-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation