Long-term monitoring of surface water quality and groundwater po-tential using computational intelligence, GIS technologies, and remote sensing
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2026.1.09Keywords:
computational intelligence, fuzzy logic, remote sensing, satellite imagery, surface water quality monitoring, groundwater potential assessment, hybrid neural networks, NEFCLASS-EM, TS-FNN, Fuzzy C-Means, K-MeansAbstract
Water scarcity and declining water quality due to population growth, urbanization, industrialization, and climate change highlight the importance of effective water management. Advances in remote sensing, cloud computing, and computational intelligence underscore the need to utilize modern technologies for monitoring surface water quality. This research involves the development of hybrid intelligent models using Landsat and Sentinel-2 images and WISE data with hybrid deep learning networks to evaluate surface water quality and groundwater potential. Correlation analysis revealed strong connections between remote sensing data and water quality parameters (such as chlorophyll-a, dissolved oxygen, nitrogen, and phosphorus). The hybrid models surpassed traditional machine learning methods, demonstrating their effectiveness in real-world water management.
References
UN General Assembly. Transforming Our World: The 2030 Agenda for Sustainable Development. 21 October 2015. Available: https://www.refworld.org/legal/resolution/unga/2015/en/111816
Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy; Eu-ropean Parliament: Bruxelles, Belgium, 2003. Available: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000L0060
F. Faur, M. Lazăr, I.-M. Apostu, O. Pinchuk, S. Klimov, “Monitoring the water quali-ty of Jiu River in Dolj County,” E3S Web Conf., vol. 280, article no. 10002, 2021. doi: https://doi.org/10.1051/e3sconf/202128010002
J. He, Y. Chen, J. Wu, D.A. Stow, G. Christakos, “Space-Time Chlorophyll-a Retriev-al in Optically Complex Waters that Accounts for Remote Sensing and Modeling Un-certainties and Improves Remote Estimation Accuracy,” Water Research, vol. 171, 115403, 2019. doi: https://doi.org/10.1016/j.watres.2019.115403
B. Nas, S. Ekercin, H. Karabörk, A. Berktay, D.J. Mulla, “An Application of Landsat-5TM Image Data for Water Quality Mapping in Lake Beysehir, Turkey,” Water, Air, & Soil Pollution, vol. 212, pp. 183–197, 2010. doi: https://doi.org/10.1007/s11270-010-0331-2
M. Govedarica, G. Jakovljevic, “Monitoring spatial and temporal variation of water quality parameters using time series of open multispectral data,” in Seventh Internation-al Conference on Remote Sensing and Geoinformation of the Environment, Paphos, Cyprus, 18–21 March 2019, vol. 11174. doi: https://doi.org/10.1117/12.2533708
C. Wu et al., “Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data,” Int. J. Remote Sens., vol. 31, issue 9, pp. 2309–2324, 2010. doi: https://doi.org/10.1080/01431160902973873
Gordana Jakovljevic, Flor Álvarez-Taboada, Miro Govedarica, “Long-Term Monitoring of Inland Water Quality Parameters Using Landsat Time-Series and Back-Propagated ANN: Assessment and Usability in a Real-Case Scenario,” Remote Sensing, vol. 16, is-sue 1, 68, 2024. doi: https://doi.org/10.3390/rs16010068
N. Ha, K. Koike, M. Nhuan, “Improved Accuracy of Chlorophyll-a Concentration Es-timates from MODIS Imagery Using a Two-Band Ratio Algorithm and Geostatistics: As Applied to the Monitoring of Eutrophication Processes over Tien Yen Bay (Norther Vietnam),” Remote Sensing, vol. 6, issue 1, pp. 421–442, 2013. doi: https://doi.org/10.3390/rs6010421
B. Nechad, K. Ruddick, Y. Park, “Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters,” Remote Sensing of Environment, vol. 114, issue 4, pp. 854–866, 2010. doi: https://doi.org/10.1016/j.rse.2009.11.022
K.T. Peterson, V. Sagan, J.J. Sloan, “Deep learning-based water quality estimation and anomaly detection using Land-sat-8/Sentinel-2 virtual constellation and cloud compu-ting,” GIScience & Remote Sensing, vol. 57, issue 4, pp. 510–525, 2020. doi: https://doi.org/10.1080/15481603.2020.1738061
S. Hafeez et al., “Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong,” Remote Sensing, vol. 11, issue 6, 617, 2019. doi: https://doi.org/10.3390/rs11060617
L.F. Arias-Rodriguez, U.F. Tüzün, Z. Duan, J. Huang, Y. Tuo, M. Disse, “Global Wa-ter Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning,” Remote Sensing, vol. 15, issue 5, 1390, 2023. doi: https://doi.org/10.3390/rs15051390
D. Gómez, P. Salvador, J. Sanz, J.L. Casanova, “A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning meth-ods,” Environmental Pollution, vol. 286, 117489, 2021. doi: https://doi.org/10.1016/j.envpol.2021.117489
G. Jakovljevic, M. Govedarica, F. Alvarez-Taboada, “Water body mapping: A compari-son of remotely sensed and GIS open data sources,” International Journal of Remote Sensing, vol. 40, issue 8, pp. 2936–2964, 2018. doi: https://doi.org/10.1080/01431161.2018.1538584
DHI Education Resource. Available: https://raw.githubusercontent.com/DHI/Intro_ML_course/main/module_6/Brahmaputra_images.zip
Z. Zhou, The Application of Fuzzy Neural Network Based on T-S Model in Water Quality Evaluation. Master’s Thesis. East China University of Political Science and Law, Nanjing, China, 2007.
X. Wang, MATLAB Neural Networks 43 Case Studies; 1st ed. Beijing, China: Beijing University of Aeronautics and Astronautics Press, 2013, pp. 288–289.
O.I. Chumachenko, “Deep learning classifier based on nefclass neural network,” Elec-tronics and Control Systems, vol. 3, no. 49, 2016. doi: https://doi.org/10.18372/1990-5548.49.11242
Jamileh Yousefi, “A modified NEFCLASS classifier with enhanced accuracy-interpretability trade-off for datasets with skewed feature values,” Fuzzy Sets and Sys-tems, vol. 413, pp. 99–113, 15 June 2021. doi: https://doi.org/10.1016/j.fss.2020.07.011
H.Y. Hong, A. Jaafari, E.K. Zenner, “Predicting spatial patterns of wildfire susceptibil-ity in the Huichang County, China: An integrated model to analysis of landscape indi-cators,” Ecological Indicators, vol. 101, pp. 878–891, 2019. doi: https://doi.org/10.1016/j.ecolind.2019.01.056
Jinyue Chen et al., “Remote Sensing Big Data for Water Environment Monitoring: Cur-rent Status, Challenges, and Future Prospects,” Earth’s Future, vol. 10, issue 2, 2022. doi: https://doi.org/10.1029/2021EF002289
L.L. Tang, S. Zhang, J.H. Zhang, Y. Liu, Y. Bai, “Estimating evapotranspiration based on the satellite-retrieved near-infrared reflectance of vegetation (NIRv) over croplands,” GIScience and Remote Sensing, vol. 58, issue 6, pp. 889–913, 2021. doi: https://doi.org/10.1080/15481603.2021.1947622
Normalized difference turbidity index. Available: https://developers.arcgis.com/python/latest/samples/river-turbidity-estimation-using-sentinel2-data-/
Turbidity Index (NTU). Available: https://www.summerland.ca/docs/default-source/works-and-utilities/water/turbidity-index.pdf?sfvrsn=2
Nazarij Buławka, Hector A. Orengo, Iban Berganzo-Besga, “Deep learning-based detection of qanat underground water distribution systems using HEXAGON spy satellite imagery,” Journal of Archaeological Science, vol. 171, 106053, 2024. doi: https://doi.org/10.1016/j.jas.2024.106053
Bingxue Zhao, Lei Wang, “Surface water monitoring from 1984 to 2021 based on Landsat time-series images and Google Earth Engine,” Heliyon, vol. 10, issue 17, e36660, 2024. doi: https://doi.org/10.1016/j.heliyon.2024.e36660
