SciELO - Scientific Electronic Library Online

 
vol.47 issue2The use of Radon (Rn222) isotopes to detect groundwater discharge in streams draining Table Mountain Group (TMG) aquifersIncreasing nutrient influx trends and remediation options at Hartbeespoort Dam, South Africa: a mass-balance approach author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Article

Indicators

Related links

  • On index processCited by Google
  • On index processSimilars in Google

Share


Water SA

On-line version ISSN 1816-7950
Print version ISSN 0378-4738

Abstract

DANG, Nguyen Mai  and  ANH, Duong Tran. Integration of ANFIS with PCA and DWT for daily suspended sediment concentration prediction. Water SA [online]. 2021, vol.47, n.2, pp.200-209. ISSN 1816-7950.  http://dx.doi.org/10.17159/wsa/2021.v47.i2.10916.

Quantifying sediment load is vital for aquatic and riverine biota and has been the subject of various environmental studies since sediment plays a key role in maintaining ecological integrity, river morphology and agricultural productivity. However, predicting sediment concentration in rivers is difficult because of the non-linear relationships of flow rates, geophysical characteristics and sediment loads. It is thus very important to propose suitable statistical methods which can provide fast, accurate and robust prediction of suspended sediment concentration (SSC) for management guidance. In this study, we developed coupled models of discrete wavelet transform (DWT) with adaptive neuro-fuzzy inference system (ANFIS), named DWT-ANFIS, and principal component analysis (PCA) with ANFIS, named PCA-ANFIS, for SSC time-series modeling. The coupled models and single ANFIS model were trained and tested using long-term daily SSC and river discharge which were measured on the Schuylkill and Iowa Rivers in the United States. The findings showed that the PCA-ANFIS performed better than the single ANFIS and the coupled DWT-ANFIS. Further applications of the PCA-ANFIS should be considered for simulation and prediction of other indicators relating to weather, water resources, and the environment.

Keywords : machine learning; Schuylkill River; Iowa River; suspended sediment.

        · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License