Utilization of Machine Learning Models and Satellite Data for the Estimation of Total Dissolved Solids in the Colorado River System
Publication: World Environmental and Water Resources Congress 2023
ABSTRACT
Salinity or TDS-related issues are a major cause of economic loss due to their devastating impact on agricultural production. The Colorado River Basin (CRB) Salinity Control Act requires the delivery of water by the US to Mexico from the Colorado River with an average maximum TDS concentration of 115 ± 30 mg/L in addition to the annual average salinity of the river at the Imperial Dam. However, traditional TDS measurements are costly and labor-intensive, and there is a need to estimate changes in TDS along the river system in the CRB. This study developed models for the estimations of TDS using remote sensing data and machine learning (ML) models. The ML models used in the study are categorized as standalone and hybrid ML models. Standalone models used include linear, random forest, and extreme gradient boosting regressors. The hybrid ML models include bagging, stacking, and adaptive boosting regressors. The reflectance data from 10-m resolution Sentinel-2A/B and the ground-truth TDS data obtained from the United States Geological Survey are used for the study. The remote sensing data was processed in the Google Earth Engine (GEE) platform. The GEE platform provides an interface to use JavaScript libraries and serves as a toolbox for visualization and geospatial analysis. The model provides a cost-effective approach for the estimation of TDS. For standalone ML algorithms, the analysis produced average Wilmot’s index of agreement (WI) and mean absolute relative error (MARE) of 0.62 and 0.36, respectively. The ensemble ML algorithms produced average WI and MARE of 0.65 and 0.45, respectively. From the results and data processing methods attempted, the ensemble ML methods have not shown better performance compared to the standalone ML models using the model evaluation metrics.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Abbas, M. R., Rasib, A. W., Bin, A., Bin, B., Musa, T., Bin, A., Abbas, T. R., and Dutsenwai, H. S. (2019). Landsat data to estimate a model of water quality parameters in Tigris and Euphrates rivers – Iraq. International Journal of Advanced and Applied Sciences, 6(5), 50–58. https://doi.org/10.21833/ijaas.2019.05.009.
Adjovu, G. E. (2020). Evaluating the Performance of A GIS-Based Tool for Delineating Swales Along Two Highways in Tennessee. Published by ProQuest LLC.
Adjovu, G. E., Ahmad, S., and Stephen, H. (2021). Analysis of Suspended Material in Lake Mead Using Remote Sensing Indices. World Environmental and Water Resources Congress 2021. https://doi.org/10.1061/9780784483466.069.
Adjovu, G. E., Stephen, H., and Ahmad, S. (2022). Monitoring of Total Dissolved Solids Using Remote Sensing Band Reflectance and Salinity Indices: A Case Study of the Imperial County Section, AZ-CA, of the Colorado River. World Environmental and Water Resources Congress 2022. https://doi.org/https://doi.org/10.1061/9780784484258.106.
Amani, M., et al. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052.
Avdan, Z. Y., Kaplan, G., Goncu, S., and Avdan, U. (2019). Monitoring the water quality of small water bodies using high-resolution remote sensing data. ISPRS International Journal of Geo-Information, 8(12). https://doi.org/10.3390/ijgi8120553.
Banadkooki, F. B., Ehteram, M., Panahi, F., Sammen, S., Othman, S., and EL-Shafie, A. (2020). Estimation of total dissolved solids (TDS) using new hybrid machine learning models. Journal of Hydrology, 587(February), 124989. https://doi.org/10.1016/j.jhydrol.2020.124989.
Bureau of Reclamation. (2013). Quality of Water Progress Report No. 24. 24, 130.
Chen, J., et al. (2019). A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environment International, 130(February). https://doi.org/10.1016/j.envint.2019.104934.
Chen, S., and Luc, N. M. (2022). RRMSE Voting Regressor: A weighting function based improvement to ensemble regression. 2, 1–9.
Chen, X., Liu, L., Zhang, X., Li, J., Wang, S., Liu, D., Duan, H., and Song, K. (2021). An Assessment of Water Color for Inland Water in China Using a Landsat 8-Derived Forel-Ule Index and the Google Earth Engine Platform. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5773–5785. https://doi.org/10.1109/JSTARS.2021.3085411.
Colorado River Basin Salinity Control Forum. (2020). 2020 Review: Water Quality Standards for Salinity, Colorado River System. October, 99.
da Silva, M. G., de Aguiar Netto, A. D. O., de Jesus Neves, R. J., do Vasco, A. N., Almeida, C., and Faccioli, G. G. (2015). Sensitivity Analysis and Calibration of Hydrological Modeling of the Watershed Northeast Brazil. Journal of Environmental Protection, 06(08), 837–850. https://doi.org/10.4236/jep.2015.68076.
Dekker, A. G., and Hestir, E. L. (2012). Evaluating the feasibility of systematic inland water quality monitoring with satellite remote sensing; Water for a Healthy Country WIRADA., May, 105.
Dekker, A. G., Zamurovic, Ž., Hoogenboom, H. J., and Peters, S. W. M. (1996). Remote sensing, ecological water quality modelling and in situ measurements: a case study in shallow lakes. Hydrological Sciences Journal, 41(4), 531–547. https://doi.org/10.1080/02626669609491524.
Dewidar, K., Ezaby, K., El Daym, H. A., and Ibrahim, M. (2008). Mapping some water quality parameters by using Landsat-7 ETM + for Manzala lagoon, Egypt. Environmental Science and Technology, April.
Dube, T., Mutanga, O., Seutloali, K., Adelabu, S., and Shoko, C. (2015). Water quality monitoring in sub-Saharan African lakes: a review of remote sensing applications. African Journal of Aquatic Science, 40(1), 1–7. https://doi.org/10.2989/16085914.2015.1014994.
European Space Agency. (2015). Sentinel-2 User Handbook. Industrial & Engineering Chemistry.
Gallagher, L. C. (2004). Dissolved Organic Matter in Coastal and Inland Waters, British Columbia, Canada A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of in the School of Earth and Ocean Sciences to the required standard.
Gholizadeh, M. H., Melesse, A. M., and Reddi, L. (2016a). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors (Switzerland), 16(8). https://doi.org/10.3390/s16081298.
Gholizadeh, M. H., Melesse, A. M., and Reddi, L. (2016b). Spaceborne and airborne sensors in water quality assessment. International Journal of Remote Sensing, 37(14), 3143–3180. https://doi.org/10.1080/01431161.2016.1190477.
Guo, H., Huang, J. J., Chen, B., Guo, X., and Singh, V. P. (2021). A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery. International Journal of Remote Sensing, 42(5), 1841–1866. https://doi.org/10.1080/01431161.2020.1846222.
Hajigholizadeh, M., Moncada, A., Kent, S., and Melesse, A. M. (2021). Land–lake linkage and remote sensing application in water quality monitoring in lake okeechobee, Florida, USA. Land, 10(2), 1–17. https://doi.org/10.3390/land10020147.
Hoerling, M., Barsugli, J., Livneh, B., Eischeid, J., Quan, X., and Badger, A. (2019). Causes for the century-long decline in Colorado river flow. Journal of Climate, 32(23), 8181–8203. https://doi.org/10.1175/JCLI-D-19-0207.1.
Karagiannis, I. C., and Soldatos, P. G. (2008). Water desalination cost literature: review and assessment. Desalination, 223(1–3), 448–456. https://doi.org/10.1016/j.desal.2007.02.071.
Kaur, H., Malhi, A. K., and Pannu, H. S. (2020). Machine learning ensemble for neurological disorders. Neural Computing and Applications, 32(16), 12697–12714. https://doi.org/10.1007/s00521-020-04720-1.
Li, S., et al. (2021). 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. https://doi.org/10.1016/j.scitotenv.2021.146271.
Livingston, F. (2005). Implementation of Breiman’s Random Forest Machine Learning Algorithm. Machine Learning Journal Paper, 1–13.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153.
Page, B. P., Olmanson, L. G., and Mishra, D. R. (2019). A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems. Remote Sensing of Environment, 231(September 2018), 111284. https://doi.org/10.1016/j.rse.2019.111284.
Pahlevan, N., Sarkar, S., Franz, B. A., Balasubramanian, S. V., and He, J. (2017). Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and validations. Remote Sensing of Environment, 201(May), 47–56. https://doi.org/10.1016/j.rse.2017.08.033.
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12(14). https://doi.org/10.3390/rs12142291.
Phyo, P. P., Byun, Y. C., and Park, N. (2022). Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression. Symmetry, 14(1), 1–13. https://doi.org/10.3390/sym14010160.
Pizani, F. M. C., Maillard, P., Ferreira, A. F. F., and De Amorim, C. C. (2020a). Estimation of water quality in a reservoir from sentinel-2 MSI and Landsat-8 OLI sensors. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(3), 401–408. https://doi.org/10.5194/isprs-Annals-V-3-2020-401-2020.
Pizani, F. M. C., Maillard, P., Ferreira, A. F. F., and De Amorim, C. C. (2020b). Estimation of Water Quality in a Reservoir from Sentinel-2 MSI and Landsat-8 OLI Sensors. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3–2020, 401–408. https://doi.org/https://doi.org/10.5194.
Potes, M., Costa, M. J., and Salgado, R. (2012). Satellite remote sensing of water turbidity in Alqueva reservoir and implications on lake modelling. Hydrology and Earth System Sciences, 16(6), 1623–1633. https://doi.org/10.5194/hess-16-1623-2012.
Rahaman, M. M., Thakur, B., Kalra, A., and Ahmad, S. (2019). Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin. Hydrology, 6(19). https://doi.org/http://dx.doi.org/10.3390/hydrology6010019.
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., and Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804–818. https://doi.org/10.1016/j.oregeorev.2015.01.001.
Rong, S., and Bao-Wen, Z. (2018). The research of regression model in machine learning field. MATEC Web of Conferences, 176, 8–11. https://doi.org/10.1051/matecconf/201817601033.
Rumsey, C. A., Miller, O., Hirsch, R. M., Marston, T. M., and Susong, D. D. (2021). Substantial Declines in Salinity Observed Across the Upper Colorado River Basin During the 20th Century, 1929–2019. Water Resources Research, 57(5), 1–21. https://doi.org/10.1029/2020WR028581.
Saher, R., Shaikh, T. A., Ahmad, S., and Stephen, H. (2020). Analysis of Changes in Runoff Due to Land Cover Change. In Watershed Management 2020, American Society of Civil Engineers, 2020, pp. 245–56. DOI.org (Crossref), https://doi.org/10.1061/9780784483060.022.
Schaeffer, B. A., Schaeffer, K. G., Keith, D., Lunetta, R. S., Conmy, R., and Gould, R. W. (2013). Barriers to adopting satellite remote sensing for water quality management. International Journal of Remote Sensing, 34(21), 7534–7544. https://doi.org/10.1080/01431161.2013.823524.
Scikit Learn. (n.d.). Supervised learning-Scikit learn documentation. https://scikit-learn.org/0.23/supervised_learning.html#supervised-learning.
Serafy, G. Y. H., et al. (2021). Integrating Inland and Coastal Water Quality Data for Actionable Knowledge. 1–24.
Shaikh, T. A., Ahmad, S., and Stephen, H. (2021). Assessing Spatiotemporal Change in Land Cover and Total Dissolved Solids Concentration Using Remote Sensing Data. In World Environmental and Water Resources Congress 2021, American Society of Civil Engineers, 2021, pp. 384–96. DOI.org (Crossref), https://doi.org/10.1061/9780784483466.036.
Shaikh, T. A., Saher, R., Ahmad, S., Gerrity, D., and Stephen, H. (2020). Impacts of Urban Development on Flooding: A Case Study of Flamingo and Tropicana Watershed, Clark County. Impacts of Urban Development on Flooding: A Case Study of Flamingo and Tropicana Watershed, Clark County. In Watershed Management 2020, American Society of Civil Engineers, 2020, pp. 233–44. DOI.org (Crossref), https://doi.org/10.1061/9780784483060.021.
Shope, C. L., and Gerner, S. J. (2016). Assessment of Dissolved-Solids Loading to the Colorado River in the Paradox Basin between the Dolores River and Gypsum Canyon, Utah., 28. https://doi.org/http://dx.doi.org/10.3133/sir20135229.
Singh, A. K. (2021). Impact of the Coronavirus Pandemic on Las Vegas Strip Gaming Revenue. In The Journal of Gambling Business and Economics (Vol. 14).
Sun, K., Rajabtabar, M., Samadi, S. Z., Rezaie-Balf, M., Ghaemi, A., Band, S. S., and Mosavi, A. (2021). An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction. Engineering Applications of Computational Fluid Mechanics, 15(1), 251–271. https://doi.org/10.1080/19942060.2020.1861987.
USGS. (n.d.). Why are negative values observed over water in some Landsat Surface Reflectance products? https://www.usgs.gov/faqs/why-are-negative-values-observed-over-water-some-landsat-surface-reflectance-products.
VanderPlas, J. (2019). Python Data Science Handbook. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9).
Venkatesan, A. K., Ahmad, S., Batista, J. R., and Johnson, W. S. (2010). Total dissolved solids contribution to the Colorado river associated with the growth of Las Vegas valley. World Environmental and Water Resources Congress 2010: Challenges of Change - Proceedings of the World Environmental and Water Resources Congress 2010, 3376–3385. https://doi.org/10.1061/41114(371)348.
Wang, Z., Lei, Y., Cui, H., Miao, H., Zhang, D., Wu, Z., and Liu, G. (2022). Enhanced RBF neural network metamodelling approach assisted by sliced splitting-based K-fold cross-validation and its application for the stiffened cylindrical shells. Aerospace Science and Technology, 124, 107534. https://doi.org/10.1016/j.ast.2022.107534.
Wolff, S., O’Donncha, F., and Chen, B. (2020). Statistical and machine learning ensemble modelling to forecast sea surface temperature. Journal of Marine Systems, 208(May), 103347. https://doi.org/10.1016/j.jmarsys.2020.103347.
Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., and Chen, F. (2022). A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sensing, 14(8), 1770. https://doi.org/10.3390/rs14081770.
Zounemat-Kermani, M., Batelaan, O., Fadaee, M., and Hinkelmann, R. (2021). Ensemble machine learning paradigms in hydrology: A review. Journal of Hydrology, 598(December 2020), 126266. https://doi.org/10.1016/j.jhydrol.2021.126266.
Information & Authors
Information
Published In
World Environmental and Water Resources Congress 2023
Pages: 1147 - 1160
History
Published online: May 18, 2023
ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Buildings
- Computer programming
- Computing in civil engineering
- Dissolved solids
- Engineering fundamentals
- Environmental engineering
- High-rise buildings
- Hybrid methods
- Mathematics
- Measurement (by type)
- Methodology (by type)
- River engineering
- Rivers and streams
- Salt water
- Sensors and sensing
- Structural engineering
- Structures (by type)
- Water (by type)
- Water and water resources
- Water management
- Water treatment
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.