Boyd, C. Dissolved Solids, chap. 16, 339–347 (Springer, Cham, 2020).
Greenberg, A., Clesceri, L. & Eato, A. Standard methods for the examination of water and waste water (APHA, 1992).
Wang, B. B. Research on drinking water purification technologies for household use by reducing total dissolved solids (tds). PLoS One 16, e0257865 (2021).
Google Scholar
Godson Ebenezer Adjovu, S. A., Haroon Stephen. Spatiotemporal variability in total dissolved solids and total suspended solids along the colorado river. Hydrology 10, 125, https://doi.org/10.3390/hydrology10060125 (2023).
Ocheli, A., Otuya, O. B. & Umayah, S. O. Appraising the risk level of physicochemical and bacteriological twin contaminants of water resources in part of the western niger delta region. Environ. Monit. Assess. 192, 324 (2020).
Google Scholar
ORSANCO. Characterization of dissolved solids in the ohio river and selected tributaries. Tech. Rep. Technical report, 42, Ohio River Valley Water Sanitation Commission, Cincinnati, Ohio (2014).
Nachshon, U. Cropland soil salinization and associated hydrology: Trends, processes and examples. Water 10, 1030 (2018).
He, W., Chen, S., Liu, X. & Chen, J. Water quality monitoring in a slightly-polluted inland water body through remote sensing – case study of the guanting reservoir in beijing, china. Front. Environ. Sci. Eng. China 2, 163–171 (2008).
Liu, Q., Huang, C., Shi, Z. & Zhang, S. Probabilistic river water mapping from landsat-8 using the support vector machine method. Remote Sens. 12, 1374 (2020).
Google Scholar
Hafeez, S., Wong, M., Abbas, S. & Jiang, G. Assessing the potential of geostationary himawari-8 for mapping surface total suspended solids and its diurnal changes. Remote Sensing 13, 1–20 (2021).
Adusei, Y. Y., Quaye-Ballard, J., Adjaottor, A. A. & Mensah, A. A. Spatial prediction and mapping of water quality of owabi reservoir from satellite imageries and machine learning models. Egypt. J. Remote Sens. Space Sci. 24, 825–833 (2021).
Kabolizadeh, M., Rangzan, K., Zareie, S., Rashidian, M. & Delfan, H. Evaluating quality of surface water resources by ann and anfis networks using sentinel-2 satellite data. Earth Sci. Inform. 15, 523–540 (2022).
Google Scholar
Maciel, D. A., Barbosa, C. C. F., Novo, E. M. L. d. M., Flores Júnior, R. & Begliomini, F. N. Water clarity in brazilian water assessed using sentinel-2 and machine learning methods. ISPRS J. Photogramm. Remote Sens. 182, 134–152 (2021).
Shokati, H. et al. Random forest-based soil moisture estimation using sentinel-2, landsat-8/9, and uav-based hyperspectral data. Remote Sens. 16, 1962 (2024).
Li, S. et al. Quantification of chlorophyll-a in typical lakes across china using sentinel-2 msi imagery with machine learning algorithm. Science of the Total Environment 778, 146271 (2021).
Google Scholar
Zhang, H. et al. Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model. Environmental Pollution 347, 123771 (2024).
Google Scholar
Zhao, F., Feng, S., Xie, F., Zhu, S. & Zhang, S. Extraction of long time series wetland information based on google earth engine and random forest algorithm for a plateau lake basin – a case study of dianchi lake, yunnan province, china. Ecological Indicators 146, 109813 (2023).
Waleed, M. & Sajjad, M. Machine learning-based spatial-temporal assessment and change transition analysis of wetlands: An application of google earth engine in sylhet, bangladesh (1985–2022). Ecological Informatics 75, 102075 (2023).
Pourhosseini, F. A., Ebrahimi, K. & Omid, M. H. Prediction of total dissolved solids, based on optimization of new hybrid svm models. Engineering Applications of Artificial Intelligence 126, 106780 (2023).
OEPA. Ohio 2024 integrated water quality monitoring and assessment report. Tech. Rep. 403, Ohio Environmental Protection Agency, Division of Surface Water and Monitoring Assessment Section, Columbus, Ohio (2024).
el Naggar. M.E., Shaaban-Dessouki, S., Abdel-Hamid, M. & Elham, M. Effect of treated sewage on the water quality and phytoplankton population of lake manzala (egypt) with emphasis on biological assessment of water quality. Microbiologica 20, 253–276 (1997).
Evans, R. & Miller, M. Nutrients, eutrophic response, and fish anomalies in the little miami river, ohio. The Ohio Journal of Science 106, 146–155 (2006).
Google Scholar
Guyon, I. & Elissee, A. An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003).
Nembrini, S., König, I. R. & Wright, M. N. The revival of the gini importance?. Bioinformatics 34, 3711–3718 (2018).
Google Scholar
Nikoo, M. R. et al. Mapping reservoir water quality from sentinel-2 satellite data based on a new approach of weighted averaging: Application of bayesian maximum entropy. Sci. Rep. 14, 16438 (2024).
Google Scholar
Rahul, T. S. & Brema, J. Assessment of water quality parameters in muthupet estuary using hyperspectral prisma satellite and multispectral images. Environ. Monit. Assess. 195, 880 (2023).
Google Scholar
Adugna, T., Xu, W. & Fan, J. Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sensing 14, 574 (2022).
Google Scholar
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Ham, J., Chen, Y., Crawford, M. M. & Ghosh, J. Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43, 492–501 (2005).
Google Scholar
Zolfaghari Nia, M., Moradi, M., Moradi, G. & Taghizadeh-Mehrjardi, R. Machine learning models for prediction of soil properties in the riparian forests. Land (Basel) 12, 32 (2022).
Colditz, R. An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms. Remote Sens. 7, 9655–9681 (2015).
Google Scholar
Peerbhay, K. Y., Mutanga, O. & Ismail, R. Random forests unsupervised classification: The detection and mapping of solanum mauritianum infestations in plantation forestry using hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 3107–3122 (2015).
Lima, C. A. M., Coelho, A. L. V. & Von Zuben, F. Analysis of sensitivity to the kernel parameter choice: Comparing the performance profiles exhibited by standard andleast-squares svm classifiers. In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007) (IEEE, 2007).
Isah, A., Arif, M., Hassan, A., Mahmoud, M. & Iglauer, S. A systematic review of anhydrite-bearing reservoirs: Eor perspective, co2-geo-storage and future research. Fuel 320, 123942. https://doi.org/10.1016/j.fuel.2022.123942 (2022).
Google Scholar
Flori, M. Influence of temperature on the measurement of total dissolved solids (tds) in water (2024).
Santana, M. M. & Gonzalez, J. M. High temperature microbial activity in upper soil layers. FEMS Microbiology Letters 362, fnv182, https://doi.org/10.1093/femsle/fnv182 (2015).
Machado, D. V. et al. High-resolution mapping and multivariate technique (factor analysis) to support hydrogeochemical analysis and identification of surface water contamination. Journal of Geochemical Exploration 263, 107495. https://doi.org/10.1016/j.gexplo.2024.107495 (2024).
Google Scholar
Ihejirika, C. E., Njoku, J. D., Ujowundu, C., Onwudike, S. U. & Uzoka, C. N. Synergism between season, ph, conductivity and total dissolved solids (tds) of imo river quality for agricultural irrigation. Journal of Biodiversity and Environmental Sciences 1, 26–31 (2000).
Singh, P. & Yadav, B. Spatiotemporal and vertical variability of water quality in lentic small water bodies: implications of varying rainfall and land use conditions (Environ. Sci. Pollut. Res, 2024).
Li, Y. et al. Urbanization and agriculture intensification jointly enlarge the spatial inequality of river water quality. Sci. Total Environ. 878, 162559 (2023).
Google Scholar
Zhang, Q. et al. Species and spatial differences in vegetation rainfall interception capacity: A synthesis and meta-analysis in china. Catena 213, 106223 (2022).
Alnahit, A., Mishra, A. & Khan, A. Stream water quality prediction using boosted regression tree and random forest models. Stochastic Environmental Research and Risk Assessment 36, 2661–2680 (2022).
Hamidi, S. A., Hosseiny, H., Ekhtari, N. & Khazaei, B. Using modis remote sensing data for mapping the spatio-temporal variability of water quality and river turbid plume. J. Coast. Conserv. 21, 939–950 (2017).
Nobre, R. L. G. et al. Precipitation, landscape properties and land use interactively affect water quality of tropical freshwaters. Sci. Total Environ. 716, 137044 (2020).
Google Scholar
Zhang, C., Zhang, W., Huang, Y. & Gao, X. Analysing the correlations of long-term seasonal water quality parameters, suspended solids and total dissolved solids in a shallow reservoir with meteorological factors. Environ. Sci. Pollut. Res. Int. 24, 6746–6756 (2017).
Google Scholar
Räty, M., Järvenranta, K., Saarijärvi, E., Koskiaho, J. & Virkajärvi, P. Losses of phosphorus, nitrogen, dissolved organic carbon and soil from a small agricultural and forested catchment in east-central finland. Agric. Ecosyst. Environ. 302, 107075 (2020).
Badrzadeh, N., Samani, J. M. V., Mazaheri, M. & Kuriqi, A. Evaluation of management practices on agricultural nonpoint source pollution discharges into the rivers under climate change effects. Sci. Total Environ. 838, 156643 (2022).
Google Scholar
McFEETERS, S. K. The use of the normalized difference water index (ndwi) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).
OEPA. Biological and water quality study of the east fork little miami river and select tributaries 2012. Tech. Rep. Technical Report Number MAS/1999-12-3, Ohio Environmental Protection Agency, Division of Surface Water and Monitoring Assessment Section, Columbus, Ohio (2017).
Rowe, G. & Baker, N. Great and little miami river basins. Tech. Rep.4, U.S. Geological Survey, Columbus, Ohio (1997).
OEPA. Ohio water resource inventory, executive summary: summary, conclusions, and recommendations. Tech. Rep. 155, Ohio Environmental Protection Agency, Division of Surface Water and Monitoring Assessment Section, Columbus, Ohio (1996).
OEPA. Report on integrated water quality monitoring and assessment. Tech. Rep. 533, Ohio Environmental Protection Agency, Division of Surface Water and Monitoring Assessment Section, Columbus, Ohio (2018).
Feyisa, G. L., Meilby, H., Fensholt, R. & Proud, S. R. Automated water extraction index: A new technique for surface water mapping using landsat imagery. Remote Sens. Environ. 140, 23–35 (2014).
Google Scholar
Milczarek, M., Robak, A. & Gadawska, A. Sentinel water mask (swm) – new index for water detection on sentinel-2 images (2017).
Salas, E. A. L., Kumaran, S. S., Partee, E. B., Willis, L. P. & Mitchell, K. Potential of mapping dissolved oxygen in the little miami river using sentinel-2 images and machine learning algorithms. Remote Sens. Appl. Soc. Environ. 26, 100759 (2022).
Castilla, G., Hay, G. G. & Ruiz-Gallardo, J. R. Size-constrained region merging (SCRM): ): An automated delineation tool for assisted photointerpretation. Photogramm. Eng. Remote Sensing 74, 409–419 (2008).
Zhao, C.-P. & Qin, C.-Z. A detailed mangrove map of china for 2019 derived from sentinel-1 and -2 images and google earth images. Geosci. Data J. 9, 74–88 (2022).
Google Scholar
Kuhn, M. Building predictive models in r using the caret package. J. Stat. Softw. 28 (2008).
Salas, E. A. L. & Subburayalu, S. K. Correction: Modified shape index for object-based random forest image classification of agricultural systems using airborne hyperspectral datasets. PLoS One 14, e0222474 (2019).
Mantero, P., Moser, G. & Serpico, S. B. Partially supervised classification of remote sensing images through svm-based probability density estimation. IEEE Trans. Geosci. Remote Sens. 43, 559–570 (2005).
Google Scholar
Mountrakis, G., Im, J. & Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 66, 247–259 (2011).
Google Scholar
Lacaux, J., Tourre, Y., Vignolles, C., Ndione, J. & Lafaye, M. Classification of ponds from high-spatial resolution remote sensing: Application to rift valley fever epidemics in senegal. Remote Sensing of Environment) 106, 66–74 (2007).
Mukherjee, N. R. & Samuel, C. Classification of ponds from high-spatial resolution remote sensing: Application to rift valley fever epidemics in senegal. Indian Journal of Science and Technology 8, 1–7 (2016).
Yahya, A. S. A. et al. Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios. Water 11, https://doi.org/10.3390/w11061231 (2019).
Godson Ebenezer Adjovu, S. A., Haroon Stephen. Monitoring of total dissolved solids using remote rensing band reflectance and salinity indices: A case study of the imperial county section, az-ca, of the colorado river (2022). [Accessed 05-02-2025].
Resende Vieira, F. & Christofaro, C. Contributions of the vegetation index (ndvi) in water quality prediction models in a semi-arid tropical watershed. J. Arid Environ. 220, 105122 (2024).
Boyle, S. A. et al. High-resolution satellite imagery is an important yet underutilized resource in conservation biology. PLoS One 9, e86908 (2014).
Google Scholar
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
Google Scholar
Gao, B.-C. Ndwi–a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266 (1996).
Google Scholar
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. & Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 48, 119–126 (1994).
Google Scholar
Xu, H. Modification of normalised difference water index (ndwi) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing) 27, 3025–3033 (2006).
Maliki, A. A. A. et al. Estimation of total dissolved solids in water bodies by spectral indices case study: Shatt al-arab river. Water Air Soil Pollut. 231 (2020).
Tran, P. H., Nguyen, A. K., Liou, Y.-A., Hoang, P. P. & Nguyen, H. T. Estimation of salinity intrusion by using landsat 8 OLI data in the mekong delta, vietnam (2018).
Mejía Ávila, D., Torres-Bejarano, F. & Martínez Lara, Z. Spectral indices for estimating total dissolved solids in freshwater wetlands using semi-empirical models. a case study of guartinaja and momil wetlands. Int. J. Remote Sens. 43, 2156–2184 (2022).
Tamiminia, H. et al. Google earth engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152–170 (2020).
Google Scholar
Gumma, M. K., Thenkabail, P. S., Teluguntla, P. & Whitbread, A. M. Indo-ganges river basin land use/land cover (LULC) and irrigated area mapping. In Indus River Basin, 203–228 (Elsevier, 2019).
