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- W2900233077 abstract "Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability." @default.
- W2900233077 created "2018-11-16" @default.
- W2900233077 creator A5016315589 @default.
- W2900233077 creator A5037953109 @default.
- W2900233077 date "2019-03-01" @default.
- W2900233077 modified "2023-10-14" @default.
- W2900233077 title "The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction" @default.
- W2900233077 cites W1577071438 @default.
- W2900233077 cites W1966057689 @default.
- W2900233077 cites W1969680722 @default.
- W2900233077 cites W1975220059 @default.
- W2900233077 cites W1979653734 @default.
- W2900233077 cites W1985479415 @default.
- W2900233077 cites W1987557628 @default.
- W2900233077 cites W1988523040 @default.
- W2900233077 cites W1989172739 @default.
- W2900233077 cites W1989832870 @default.
- W2900233077 cites W1991200370 @default.
- W2900233077 cites W1995065637 @default.
- W2900233077 cites W1995277704 @default.
- W2900233077 cites W2002328979 @default.
- W2900233077 cites W2002351729 @default.
- W2900233077 cites W2003102560 @default.
- W2900233077 cites W2004588747 @default.
- W2900233077 cites W2007959087 @default.
- W2900233077 cites W2008661373 @default.
- W2900233077 cites W2014243623 @default.
- W2900233077 cites W2018755600 @default.
- W2900233077 cites W2019200652 @default.
- W2900233077 cites W2019385594 @default.
- W2900233077 cites W2020967943 @default.
- W2900233077 cites W2023984489 @default.
- W2900233077 cites W2027667462 @default.
- W2900233077 cites W2029724692 @default.
- W2900233077 cites W2033904036 @default.
- W2900233077 cites W2035659642 @default.
- W2900233077 cites W2037563058 @default.
- W2900233077 cites W2042561097 @default.
- W2900233077 cites W2042985051 @default.
- W2900233077 cites W2050545426 @default.
- W2900233077 cites W2050929484 @default.
- W2900233077 cites W2052075521 @default.
- W2900233077 cites W2056907263 @default.
- W2900233077 cites W2058998445 @default.
- W2900233077 cites W2061516089 @default.
- W2900233077 cites W2072618914 @default.
- W2900233077 cites W2074346324 @default.
- W2900233077 cites W2077407939 @default.
- W2900233077 cites W2077596759 @default.
- W2900233077 cites W2082484980 @default.
- W2900233077 cites W2085974256 @default.
- W2900233077 cites W2089422456 @default.
- W2900233077 cites W2093002477 @default.
- W2900233077 cites W2094635587 @default.
- W2900233077 cites W2102148524 @default.
- W2900233077 cites W2109247873 @default.
- W2900233077 cites W2116992828 @default.
- W2900233077 cites W2117886773 @default.
- W2900233077 cites W2138869959 @default.
- W2900233077 cites W2146101124 @default.
- W2900233077 cites W2151571212 @default.
- W2900233077 cites W2158404871 @default.
- W2900233077 cites W2177959459 @default.
- W2900233077 cites W2179759512 @default.
- W2900233077 cites W2189059205 @default.
- W2900233077 cites W2247666031 @default.
- W2900233077 cites W2270752644 @default.
- W2900233077 cites W2284731747 @default.
- W2900233077 cites W2346981143 @default.
- W2900233077 cites W2490619389 @default.
- W2900233077 cites W2512041146 @default.
- W2900233077 cites W2518406752 @default.
- W2900233077 cites W2518675717 @default.
- W2900233077 cites W2586766860 @default.
- W2900233077 cites W2598217115 @default.
- W2900233077 cites W2613928134 @default.
- W2900233077 cites W2725749356 @default.
- W2900233077 cites W2753179649 @default.
- W2900233077 cites W2754332904 @default.
- W2900233077 cites W2756130328 @default.
- W2900233077 cites W2763641192 @default.
- W2900233077 cites W2767028917 @default.
- W2900233077 cites W2791552866 @default.
- W2900233077 cites W2792034138 @default.
- W2900233077 cites W4211007335 @default.
- W2900233077 doi "https://doi.org/10.1016/j.catena.2018.10.047" @default.
- W2900233077 hasPublicationYear "2019" @default.
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