Application of Machine Learning Algorithms for the Estimation of the Concentration of Total Suspended Solids in the Colorado River Using Landsat 8 Operational Land Imager Data
Publication: World Environmental and Water Resources Congress 2024
ABSTRACT
The Colorado River housing several dams is a source of water to about 40 million people and 5.5 million acres of farmlands in the western United States of America and the Republic of Mexico. The quality of water in the river system is impacted by several factors, including river modifications, anthropogenic activities, and drought events. Total suspended solids (TSS) are one of the water quality parameters (WQPs) that impair the quality of the river’s water. Increased concentration of TSS increases its turbidity, thereby reducing light penetrability into the water for aquatic photosynthetic processes. Assessing and managing the concentrations of TSS in waterbodies is important to understand the changes they undergo to take pragmatic steps in protecting and restoring the quality of the water and to ensure the sustenance of the limited water resources. Monitoring of TSS has been performed using traditional approaches involving laboratory analysis and field sampling. Conducting WQP assessment on a waterbody like the Colorado River is however labor and capital-intensive due to the length and the extent of the river. Remote sensing, however, provides an alternative to WQP monitoring due to its potential benefits in offering spatiotemporal coverage as well as their cost-effectiveness. This study utilized satellite images from the 30 m resolution Landsat 8 Operational Land Imager applied machine learning (ML) regression models including gradient boosting machines or regressor, bagging, random forest, and eXtreme gradient boosting for the estimation of the concentration of optically active TSS. Results produced showed varying performance of the ML for the TSS estimations with R2 > 0.40. The study found the RF model to be the optimal model for TSS estimations with the Landsat 8 OLI with reported R2, RMSE, and MAE of 0.90, 32.80 mg/L, and 22.91 mg/L, respectively.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Adjovu, G. E. (2020). Evaluating the Performance of A GIS-Based Tool for Delineating Swales Along Two Highways in Tennessee. In Published by ProQuest LLC. Published by ProQuest LLC, Ann Arbor, MI, USA.
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/https://doi.org/10.1061/9780784483466.069.
Adjovu, G. E., Stephen, H., and Ahmad, S. (2023a). Spatiotemporal Variability in Total Dissolved Solids and Total Suspended Solids along the Colorado River. Hydrology, 10(6), 1–27. https://doi.org/10.3390/hydrology10060125.
Adjovu, G. E., Stephen, H., and Ahmad, S. (2023b). A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature. Water, 15(13). https://doi.org/10.3390/w15132439.
Adjovu, G. E., Stephen, H., James, D., and Ahmad, S. (2023a). Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems : A Review of the Issues, Conventional, and Remote Sensing Techniques.
Adjovu, G. E., Stephen, H., James, D., and Ahmad, S. (2023b). Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters. Remote Sensing, 15(7), 1938. https://doi.org/10.3390/rs15071938.
Ahmad, S., Kalra, A., and Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69–80. https://doi.org/10.1016/j.advwatres.2009.10.008.
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.
Arisanty, D., and Nur Saputra, A. (2017). Remote Sensing Studies of Suspended Sediment Concentration Variation in Barito Delta. IOP Conference Series: Earth and Environmental Science, 98(1), 1–6. https://doi.org/10.1088/1755-1315/98/1/012058.
Avijeet Biswal. (2023). Bagging in Machine Learning: Step to Perform and Its Advantages. Www.Simplilearn.Com. https://www.simplilearn.com/tutorials/machine-learning-tutorial/bagging-in-machine-learning.
Banadkooki, F. B., Ehteram, M., Panahi, F. S., Sammen, S., Othman, F. B., 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.
Bedi, S., Samal, A., Ray, C., and Snow, D. (2020). Comparative evaluation of machine learning models for groundwater quality assessment. In Environmental Monitoring and Assessment (Vol. 192, 12). https://doi.org/10.1007/s10661-020-08695-3.
Bigaignon, L., Fieuzal, R., Delon, C., and Tallec, T. (2020). Combination of two methodologies, artificial neural network and linear interpolation, to gap-fill daily nitrous oxide flux measurements. Agricultural and Forest Meteorology, 291(January), 108037. https://doi.org/10.1016/j.agrformet.2020.108037.
Brajer, N., et al. (2020). Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission. JAMA Network Open, 3(2). https://doi.org/10.1001/jamanetworkopen.2019.20733.
Bureau of Reclamation. (2013). Quality of Water Progress Report No. 24. https://www.usbr.gov/uc/progact/salinity/pdfs/PR24final.pdf.
Butler, B. A., and Ford, R. G. (2018). Evaluating relationships between total dissolved solids (TDS) and total suspended solids (TSS) in a mining-influenced watershed. Mine Water Environ., 31(37(1)), 18–30. https://doi.org/doi:10.1007/s10230-017-0484-y.
Chen, F. (2013). Missing No More: Using the MCMC Procedure to Model Missing Data. In SAS Global Forum.
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.
Chicco, D., Warrens, M. J., and Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623.
Claytex Services Ltd. (2020). How to avoid Divide by Zero errors Dymola Refactor the problem. https://www.claytex.com/tech-blog/how-to-avoid-divide-by-zero-errors/.
Dey, J., and Vijay, R. (2021). A critical and intensive review on assessment of water quality parameters through geospatial techniques. Environmental Science and Pollution Research, 28(31), 41612–41626. https://doi.org/10.1007/s11356-021-14726-4.
Di Napoli, M., Carotenuto, F., Cevasco, A., Confuorto, P., Di Martire, D., Firpo, M., Pepe, G., Raso, E., and Calcaterra, D. (2020). Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides, 17(8), 1897–1914. https://doi.org/10.1007/s10346-020-01392-9.
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.
Edalat, M. M., and Stephen, H. (2019). Socio-economic drought assessment in Lake Mead, USA, based on a multivariate standardized water-scarcity index. Hydrological Sciences Journal, 64(5), 555–569. https://doi.org/10.1080/02626667.2019.1593988.
Eertink, J. J., Heymans, M. W., Zwezerijnen, G. J. C., Zijlstra, J. M., de Vet, H. C. W., and Boellaard, R. (2022). External validation: a simulation study to compare cross-validation versus holdout or external testing to assess the performance of clinical prediction models using PET data from DLBCL patients. EJNMMI Research, 12(1), 4–11. https://doi.org/10.1186/s13550-022-00931-w.
Elmahdy, S. I., and Mohamed, M. M. (2016). Land use/land cover change impact on groundwater quantity and quality: a case study of Ajman Emirate, the United Arab Emirates, using remote sensing and GIS. Arabian Journal of Geosciences, 9(19). https://doi.org/10.1007/s12517-016-2725-y.
Ghasemi, A., and Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505.
Gholizadeh, M. H., Melesse, A. M., and Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors (Switzerland), 16(8). https://doi.org/10.3390/s16081298.
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.
Hossain, A. K. M. A., Chao, X., and Jia, Y. (2010). Development of Remote Sensing Based Index for Estimating/Mapping Suspended Sediment Concentration in River and Lake Environments. 8th International Symposium on Ecohydraulics (ISE), September, 578–585.
Imen, S., Chang, N. B., and Yang, Y. J. (2015). Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead. Journal of Environmental Management, 160, 73–89. https://doi.org/10.1016/j.jenvman.2015.06.003.
Japitana, M. V., and Burce, M. E. C. (2019). A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation. Engineering, Technology & Applied Science Research, 9(2), 3965–3970. https://doi.org/10.48084/etasr.2664.
Jones, J. R., and Knowlton, M. F. (2005). Suspended solids in Missouri reservoirs in relation to catchment features and internal processes. Water Research, 39(15), 3629–3635. https://doi.org/10.1016/j.watres.2005.06.007.
Jung, K., Bae, D. H., Um, M. J., Kim, S., Jeon, S., and Park, D. (2020). Evaluation of nitrate load estimations using neural networks and canonical correlation analysis with K-fold cross-validation. Sustainability (Switzerland), 12(1). https://doi.org/10.3390/SU12010400.
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.
Kontopoulou, E., Kolokoussis, P., and Karantzalos, K. (2017). Water quality estimation in Greek lakes from Landsat 8 multispectral satellite data. European Water, 58, 191–196.
Kupssinskü, L. S., Guimarães, T. T., De Souza, E. M., Zanotta, D. C., Veronez, M. R., Gonzaga, L., and Mauad, F. F. (2020). A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors (Switzerland), 20(7). https://doi.org/10.3390/s20072125.
Kurra, S. S., Naidu, S. G., Chowdala, S., Yellanki, S. C., and Sunanda, E. (2022). Water Quality Prediction Using Machine Learning. International Research Journal of Modernization in Engineering Technology and Science.
Kutty, A. A., Wakjira, T. G., Kucukvar, M., Abdella, G. M., and Onat, N. C. (2022). Urban resilience and livability performance of European smart cities: A novel machine learning approach. Journal of Cleaner Production, 378(September), 134203. https://doi.org/10.1016/j.jclepro.2022.134203.
Lantz, B. (2013). Machine Learning with R Learn. Packt Publishing Ltd.
Leggesse, E. S., Zimale, F. A., Sultan, D., Enku, T., Srinivasan, R., and Tilahun, S. A. (2023). Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia. Hydrology, 10(5), 110. https://doi.org/10.3390/hydrology10050110.
Leigh, C., Kandanaarachchi, S., McGree, J. M., Hyndman, R. J., Alsibai, O., Mengersen, K., and Peterson, E. E. (2019). Predicting sediment and nutrient concentrations from high-frequency water-quality data. PLoS ONE, 14(8), 1–22. https://doi.org/10.1371/journal.pone.0215503.
Lin, S., Zheng, H., Han, B., Li, Y., Han, C., and Li, W. (2022). Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotechnica, 17(4), 1477–1502. https://doi.org/10.1007/s11440-021-01440-1.
Livingston, F. (2005). Implementation of Breiman’s Random Forest Machine Learning Algorithm. Machine Learning Journal Paper, 1–13.
Lubbe, S., Filzmoser, P., and Templ, M. (2021). Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems, 210(October 2020). https://doi.org/10.1016/j.chemolab.2021.104248.
Mahanty, B., Lhamo, P., and Sahoo, N. K. (2023). Inconsistency of PCA-based water quality index – Does it reflect the quality? Science of the Total Environment, 866(December 2022), 161353. https://doi.org/10.1016/j.scitotenv.2022.161353.
Malahlela, O. E. (2019). Spatio-temporal assessment of inland surface water quality using remote sensing data in the wake of changing climate. IOP Conference Series: Earth and Environmental Science, 227(6). https://doi.org/10.1088/1755-1315/227/6/062012.
Mamat, N., Hamzah, M. F., and Jaafar, O. (2021). Hybrid Support Vector Regression Model and K-Fold Cross Validation for Water Quality Index Prediction in Langat River, Malaysia. Cv.
Montalvo, L. G. (2010). Spectral analysis of suspended material in coastal waters : A comparison between band math equations. 1–6.
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.
Mueller, J. S., Grabowski, T. B., Brewer, S. K., and Worthington, T. A. (2017). Effects of temperature, total dissolved solids, and total suspended solids on survival and development rate of larval Arkansas River shiner. Journal of Fish and Wildlife Management, 8(1), 79–88. https://doi.org/10.3996/112015-JFWM-111.
Nanglia, S., Ahmad, M., Ali Khan, F., and Jhanjhi, N. Z. (2022). An enhanced Predictive heterogeneous ensemble model for breast cancer prediction. Biomedical Signal Processing and Control, 72(PA), 103279. https://doi.org/10.1016/j.bspc.2021.103279.
Nguyen, P. T. B., Koedsin, W., McNeil, D., and Van, T. P. D. (2018). Remote sensing techniques to predict salinity intrusion: application for a data-poor area of the coastal Mekong Delta, Vietnam. International Journal of Remote Sensing, 39(20), 6676–6691. https://doi.org/10.1080/01431161.2018.1466071.
Normawati, D., and Ismi, D. P. (2019). K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining. Signal and Image Processing Letters, 1(2), 23–35. https://doi.org/10.31763/simple.v1i2.3.
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.
Peterson, K. T., Sagan, V., Sidike, P., Cox, A. L., and Martinez, M. (2018). Suspended sediment concentration estimation from landsat imagery along the lower missouri and middle Mississippi Rivers using an extreme learning machine. Remote Sensing, 10(10). https://doi.org/10.3390/rs10101503.
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.
Poudel, U., Stephen, H., and Ahmad, S. (2021). Evaluating Irrigation Performance and Water Productivity Using EEFlux ET and NDVI. Sustainability (Switzerland), 13(14). https://doi.org/10.3390/su13147967.
Rahaman, M. M., Thakur, B., Kalra, A., and Ahmad, S. (2019). Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin. Hydrology, 6(1), 19. https://doi.org/10.3390/hydrology6010019.
Robinson, A. P., and Hamann, J. D. (2011). Forest Analytics with R. In Forest Analytics with R. https://doi.org/10.1007/978-1-4419-7762-5.
Rocca, J. (2019). Ensemble methods: bagging, boosting and stacking. https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205.
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.
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.
Sa’ad, F. N. A., Tahir, M. S., Jemily, N. H. B., Ahmad, A., and Amin, A. R. M. (2021). Monitoring Total Suspended Sediment Concentration in Spatiotemporal Domain over Teluk Lipat Utilizing Landsat 8 (OLI). Applied Sciences (Switzerland), 11(15). https://doi.org/10.3390/app11157082.
Saberioon, M., Brom, J., Nedbal, V., Souc̆ek, P., and Císar̆, P. (2020). Chlorophyll-a and total suspended solids retrieval and mapping using Sentinel-2A and machine learning for inland waters. Ecological Indicators, 113(June). https://doi.org/10.1016/j.ecolind.2020.106236.
Sagan, V., Peterson, K. T., Maimaitijiang, M., Sidike, P., Sloan, J., Greeling, B. A., Maalouf, S., and Adams, C. (2020). Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Science Reviews, 205(August 2019), 103187. https://doi.org/10.1016/j.earscirev.2020.103187.
Scikit Learn. (2022). Hyperparameter Tuning. https://inria.github.io/scikit-learn-mooc/python_scripts/ensemble_hyperparameters.html.
Shaikh, T. A., Adjovu, G. E., Stephen, H., and Ahmad, S. (2023). Impacts of Urbanization on Watershed Hydrology and Runoff Water Quality of a Watershed: A Review. World Environmental and Water Resources Congress 2023, 1(i), 1271–1283. https://doi.org/10.1061/9780784484852.116.
Shrestha, B., Stephen, H., and Ahmad, S. (2021). Impervious surfaces mapping at city scale by fusion of radar and optical data through a random forest classifier. Remote Sensing, 13(15). https://doi.org/10.3390/rs13153040.
Singh, A. K. (2021). Impact of the Coronavirus Pandemic on Las Vegas Strip Gaming Revenue. The Journal of Gambling Business and Economics, 14(2), 49–62. https://doi.org/10.5750/jgbe.v14i2.1965.
Slonecker, E. T., Jennings, D. B., and Garofalo, D. (2001). Remote sensing of impervious surfaces: A review. Remote Sensing Reviews, 20(3), 227–255. https://doi.org/10.1080/02757250109532436.
Tillman, F. D., and Anning, D. W. (2014). A data reconnaissance on the effect of suspended-sediment concentrations on dissolved-solids concentrations in rivers and tributaries in the Upper Colorado River Basin. Journal of Hydrology, 519(PA), 1020–1030. https://doi.org/10.1016/j.jhydrol.2014.08.020.
Tillman, F. D., Anning, D. W., Heilman, J. A., Buto, S. G., and Miller, M. P. (2018). Managing salinity in upper Colorado River Basin streams: Selecting catchments for sediment control efforts using watershed characteristics and random forests models. Water (Switzerland), 10(6), 1–17. https://doi.org/10.3390/w10060676.
Topp, S. N., Pavelsky, T. M., Jensen, D., Simard, M., and Ross, M. R. V. (2020). Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications. Water (Switzerland), 12(1), 1–34. https://doi.org/10.3390/w12010169.
US Department of the Interior, and USGS. (n.d.). National Water Information System: Web Interface. Retrieved April 22, 2023, from https://waterdata.usgs.gov/nwis/qw.
US Department of the Interior, and USGS. (2016). River Basins of the United States: The Colorado. https://pubs.usgs.gov/gip/70039371/report.pdf.
US EPA. (2017). NPDES Compliance Inspection Manual - Chapter 5 - Sampling. January. https://www.epa.gov/quality/agency-wide-quality-system-.
USGS. (n.d.-a). What are the band designations for the Landsat satellites? Retrieved July 3, 2023, from https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites.
USGS. (n.d.-b). Why are negative values observed over water in some Landsat Surface Reflectance products?
Usali, N., and Ismail, M. H. (2010). Use of Remote Sensing and GIS in Monitoring Water Quality. Journal of Sustainable Development, 3(3), 228–238. https://doi.org/10.5539/jsd.v3n3p228.
Wakjira, T. G., Abushanab, A., Ebead, U., and Alnahhal, W. (2022). FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model. Materials Today Communications, 33(September), 104461. https://doi.org/10.1016/j.mtcomm.2022.104461.
Wakjira, T. G., Ibrahim, M., Ebead, U., and Alam, M. S. (2022). Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM. Engineering Structures, 255(August 2021), 113903. https://doi.org/10.1016/j.engstruct.2022.113903.
Wakjira, T. G., Rahmzadeh, A., Alam, M. S., and Tremblay, R. (2022). Explainable machine learning based efficient prediction tool for lateral cyclic response of post-tensioned base rocking steel bridge piers. Structures, 44(August), 947–964. https://doi.org/10.1016/j.istruc.2022.08.023.
Walch, H., von der Kammer, F., and Hofmann, T. (2022). Freshwater suspended particulate matter—Key components and processes in floc formation and dynamics. Water Research, 220(May). https://doi.org/10.1016/j.watres.2022.118655.
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.
Zhang, C., Liu, Y., Chen, X., and Gao, Y. (2022). Estimation of Suspended Sediment Concentration in the Yangtze Main Stream Based on Sentinel-2 MSI Data. Remote Sensing, 14(18). https://doi.org/10.3390/rs14184446.
Zhang, C., and Ma, Y., eds. (2012). Ensemble Machine Learning. In C. Zhang & Y. Ma (Eds.), Ensemble Machine Learning. Springer US. https://doi.org/10.1007/978-1-4419-9326-7.
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 2024
Pages: 1424 - 1442
History
Published online: May 16, 2024
ASCE Technical Topics:
- Architectural engineering
- Artificial intelligence and machine learning
- Bodies of water (by type)
- Buildings
- Coasts, oceans, ports, and waterways engineering
- Computer programming
- Computing in civil engineering
- Environmental engineering
- High-rise buildings
- Hydraulic engineering
- Hydraulic structures
- River engineering
- Rivers and streams
- Sediment
- Structural engineering
- Structures (by type)
- Turbidity
- Water and water resources
- Water management
- Water policy
- Water quality
- Water resources
- Water shortage
- Water supply
- Water treatment
- Waterways
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.