A performance evaluation of neuro‑fuzzy and regression methods in estimation of sediment load of selective rivers

• Autorzy: Varvani J. , Khaleghi M. R.

Identyfikatory

DOI

10.1007/s11600-018-0228-9

• Języki publikacji: EN

Abstrakty

EN

Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.

Słowa kluczowe

EN

accuracy index; precision; suspended load; sediment rating curves; tree regression model; neuro-fuzzy

PL

wskaźnik dokładnościowy; precyzja; rumowisko unoszone; krzywe natężenia przepływu zawiesiny

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 1
• Strony: 205--214
• Opis fizyczny: Bibliogr. 55 poz.

Twórcy

autor

Varvani J.

• Department of Range and Watershed Management, Arak Branch, Islamic Azad University, Arak, Iran

autor

Khaleghi M. R.

• Department of Range and Watershed Management, Torbat-e-Jam Branch, Islamic Azad University, Torbat‑e‑Jam, Iran

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).