152
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Model identification and accuracy for estimation of suspended sediment load

, , , ORCID Icon & ORCID Icon
Pages 18520-18545 | Received 08 May 2022, Accepted 28 Oct 2022, Published online: 20 Nov 2022

References

  • Abbaspour K. 2007. User manual for SWAT-CUP, SWAT calibration and uncertainty analysis programs. Duebendorf, Switzerland: Swiss Federal Institute of Aquatic Science and Technology, Eawag.
  • Abbaspour KC. 2011. SWAT-CUP4: SWAT calibration and uncertainty programs – A user manual. Duebendorf, Switzerland: Swiss Federal Institute of Aquatic Science and Technology, Eawag.
  • Abbaspour K, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R. 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol. 333(2–4):413–430.
  • Abdelwahab OMM, Ricci GF, De Girolamo AM, Gentile F. 2018. Modelling soil erosion in a Mediterranean watershed: comparison between SWAT and AnnAGNPS models. Environ Res. 166:363–376.
  • Aertsen W, Kint V, van Orshoven J, Özkan K, Muys B. 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model. 221(8):1119–1130.
  • Ahmad S, Simonovic SP. 2006. An intelligent decision support system for management of floods. Water Resour Manag. 20:391–410.
  • Ali G, Abbas S. 2013. Exploring CO2 sources and sinks nexus through integrated approach: insight from Pakistan. J Environ Inform. 22:112–122.
  • Aoulmi Y, Marouf N, Amireche M, Kisi O, Shubair RM, Keshtegar B. 2021. Highly accurate prediction model for daily runoff in semi-arid basin exploiting metaheuristic learning algorithms. IEEE Access. 9:92500–92515.
  • Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, van Griensven A, Van Liew MW, et al. 2012. SWAT: model use, calibration, and validation. Trans ASABE. 55(4):1491–1508.
  • Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assessment Part 1: model development. J Am Water Resources Assoc. 34(1):73–89.
  • Azari M, Moradi HR, Sagha Fi An B, Faramarzi M. 2016. Climate change impacts on stream flow and sediment yield in the North of Iran. Hydrol Sci J. 61(1):123e133.
  • Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B. 2017. Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ. 575:119–134.
  • Blettler MCM, Amsler ML, Ezcurra de Drago I, Espinola LA, Eberle E, Paira A, Best JL, Parsons DR, Drago EE. 2015. The impact of significant input of fine sediment on benthic fauna at tributary junctions: a case study of the Bermejo-Paraguay River confluence, Argentina. Ecohydrol. 8(2):340–352.
  • Breiman L. 2001. Random Forests. Mach Learn. 45(1):5–32.
  • Bressiani DD, Gassman PW, Fernandes JG, Garbossa LHP, Srinivasan R, Bonuma NB, Mendiondo EM. 2015. Review of soil and water assessment tool (SWAT) applications in Brazil: challenges and prospects. Int J Agric Biol Eng. 8:9–35.
  • Cigizoglu HK. 2004. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour. 27(2):185–195.
  • Daramola J, Ekhwan TM, Mokhtar J, Lam KC, Adeogun GA. 2019. Estimating sediment yield at Kaduna watershed, Nigeria using soil and water assessment tool (SWAT) model. Heliyon. 5(7):e02106.
  • De Cesare G, Schleiss A, Hermann F. 2001. Impact of turbidity currents on reservoir sedimentation. J Hydraul Eng. 127(1):6–16.
  • Efthimiou N. 2019. The role of sediment rating curve development methodology on river load modeling. Environ Monit Assess. 191(2):108.
  • Emamgholizadeh S, Demneh RK. 2019. A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran. Water Supply. 19(1):165–178.
  • Ezugwu CN. 2013. Sediment deposition in Nigeria reservoirs: impacts and control measures. Innov Syst Des Eng. 4(15):54–62.
  • Ferguson RI. 1986. River loads underestimated by rating curves. Water Resour Res. 22(1):74–76.
  • Gupta G, Hazarika BB, Berlin M, Sharma UM, Mishra K. 2021. Artificial intelligence for suspended sediment load prediction: a review. Environ Earth Sci. 80:346.
  • Gupta HV, Sorooshian S, Yapo PO. 1999. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng. 4(2):135–143.
  • Hastie T, Tibshirani R, Friedman J. 2009. Random forests. In: The elements of statistical learning. New York, NY: Springer; p. 587–604.
  • Hazarika BB, Gupta D. 2022. MODWT—Random vector functional link for river-suspended sediment load prediction. Arab J Geosci. 15(10):966.
  • Ho TK. 1995. Random decision forests (PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC. pp. 278–282.
  • Jha SK, Bombardelli FA. 2011. Theoretical/numerical model for the transport of non-uniform suspended sediment in open channels. Adv Water Resour. 34(5):577–591.
  • Jie LC, Yu, ST. 2011. Suspended sediment load estimate using support vector machines in Kaoping River Basin. 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet). Xianning, China: IEEE Xplore Conferences; pp. 1750–1753.
  • Kavian A, Mohammadi M, Gholami L, Rodrigo-Comino J. 2018. Assessment of the spatiotemporal effects of land use changes on runoff and nitrate loads in the Talar River. Water. 10(4):445.
  • Khosravi K, Golkarian A, Melesse AM, Deo RC. 2022. Suspended sediment load modeling using advanced hybrid Rotation Forest based Elastic Network approach. J Hydrol. 610:127963.
  • Khosravi K, Mao L, Kisi O, Yaseen ZM, Shahid S. 2018a. Quantifying hourly suspended sediment load using data mining models: case study of a glacierized Andean catchment in Chile. J Hydrol. 567:165–179.
  • Khosravi K, Panahi M, Bui DT. 2018b. Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrol Earth Syst Sci. 22(9):4771–4792.
  • Kia N. 2014. Evaluating of the SWAT model in predicting runoff and sediment yield at Mazandaran Province [M.Sc. thesis]. Iran: Isfahan University of Technology.
  • Kisi O. 2004. Daily suspended sediment modelling using a fuzzy differential evolution approach. Hydrol Sci J. 49(1):183–197.
  • Kisi O, Yaseen ZM. 2019. The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction. CATENA. 174:11–23.
  • Legates DR, Mccabe GJ. 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res. 35(1):233–241.
  • Li J, Huang T. 2018. Predicting and analyzing early wake-up associated gene expressions by integrating GWAS and eQTL studies. Biochim Biophys Acta Mol Basis Dis. 1864(6 Pt B):2241–2246.
  • Ligaray M, Kim M, Baek S, Ra JS, Chun JA, Park Y, Boithias L, Ribolzi O, Chon K, Cho KH. 2017. Modeling the fate and transport of Malathion in the Pagsanjan-Lumban basin, Philippines. Water. 9(7):451.
  • Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH. 2011. Suspended sediment load prediction of river systems: an artificial neural network approach. Agric Water Manage. 98(5):855–866.
  • Meshram SG, Safari MJS, Khosravi K, Meshram C. 2021. Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction. Environ Sci Pollut Res Int. 28(9):11637–11649.
  • Mohammadi M. 2016. The effects of land use change evaluation on water quantity and quality of Talar river using remote sensing and hydrological modeling. Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Iran. 116 p.
  • Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD, Veith TL. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE. 50:885–900.
  • Mosquera-Machado S, Ahmad S. 2007. Flood hazard assessment of Atrato River in Colombia. Water Resour Manage. 21(3):591–609.
  • Naseri F, Azari M, Dastorani MT. 2018. Optimizing coefficients of sediment rating curve equation using genetic algorithm (Case study: GHAZAGHLI and BAGH ABBASI stations). Irrig Water Eng. 9(3):82–98. (In Persian).
  • Neitsch SL, Arnold JG, Kiniry, JR. 2005. Soil and water assessment tool documentation (User’s manual). Texas Water Resources Institute, College Station, Texas TWRI Report TR-192; 494 p.
  • Nhu V-H, Khosravi K, Cooper JR, Karimi M, Kisi O, Pham BT, Lyu Z. 2020. Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method. Hydrol Sci J. 65(12):2116–2127.
  • Phan D, Wu C, Hsieh S. 2011. Impact of climate change on stream discharge and sediment yield in Northern Viet Nam. Water Resour. 38(6):e827–e836.
  • Rostamian R, Jaleh A, Afyuni M, Mousavi SF, Heidarpour M, Jalalian A, Abbaspour K. 2008. Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrol Sci J. 53(5):977–988.
  • Sahin S. 2012. An aridity index defined by precipitation and specific humidity. J Hydrol. 444–445:199–208.
  • Sandy R. 1990. Statistics for business and economics. New York: McGraw-Hill Publishing; p. 1117.
  • Shrestha B, Babel MS, Maskey S, van Griensven A, Uhlenbrook S, Green A, Akkharath I. 2013. Impact of climate change on sediment yield in the Mekong river basin: a case study of the Namou basin. Lao PDR. Hydrol Earth Syst Sci. 17(1):1e20.
  • Tan ML, Gassman PW, Srinivasan R, Arnold JG, Yang X. 2019. A review of SWAT studies in Southeast Asia: applications, challenges and future directions. Water. 11(5):914.
  • Tao H, Diop L, Bodian A, Djaman K, Ndiaye PM, Yaseen ZM. 2018. Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: regional case study in Burkina Faso. Agric Water Manage. 208(30):140–151.
  • Ting KM, Witten IH. 1997. Stacking bagged and dagged models. (Working paper 97/09). University of Waikato, Department of Computer Science, Hamilton, New Zealand
  • Tolson BA, Shoemaker CA. 2007. Cannonsville reservoir watershed SWAT2000 model development, calibration and validation. J Hydrol. 337(1–2):68–86.
  • Van Griensven A, Ndomba P, Yalew S, Kilonzo F. 2012. Critical review of SWAT applications in the Upper Nile basin countries. Hydrol Earth Syst Sci. 16(9):3371–3381.
  • Walling DE. 1977. Limitations of the rating curve technique for estimating suspended sediment loads, with particular reference to British rivers. Erosion and solid matter transport in inland waters. Vol. 122. IAHS Publication; p. 34–48.
  • Wang J, Ishidaira H, Sun W, Ning S. 2013. Development and interpretation of new sediment rating curve considering the effect of vegetation cover for Asian Basins. ScientificWorldJournal. 2013:154375.
  • Williams JR. 1975. Sediment routing for agricultural watersheds. J Am Water Resources Assoc. 11(5):965–974.
  • Williams JR. 1995. Chapter 25: the EPIC model. In: Singh VP, editor. Computer models of watershed hydrology. Highlands Ranch: Water Resources Publications; p. 909–1000.
  • Wischmeier WH, Smith DD. 1978. Predicting rainfall erosion losses–a guide for conservation planning. U.S. Department of Agriculture, Agriculture Handbook; p. 537.
  • Yang J, Reichert P, Abbaspour KC, Xia J, Yang H. 2008. Comparing uncertainty analysis techniques for a SWAT application to Chaohe Basin in China. J Hydrol. 358(1–2):1–23.
  • Yang X-S. 2008. Nature-inspired metaheuristic algorithms. UK: Luniver Press, University of Cambridge.
  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Mm A-K. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides. 13:839–856.
  • Zhang W, Jia Q, Chen X. 2014. Numerical simulation of flow and suspended sediment transport in the distributary channel networks. Journal of applied mathematics (special issue entitled: Modeling of Water Quality, Quantity, and Sustainability). 2014:1–9. https://doi.org/10.1155/2014/948731.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.