Quantitative retrieval of suspended solid concentration in Lake Taihu based on BP neural net

Heng Lü; Xinguo Li; Kai Cao

Quantitative retrieval of suspended solid concentration in Lake Taihu based on BP neural net

Heng Lü^*
, Xinguo Li
, Kai Cao

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

A two-layer BP neural net model is constructed with four input nodes of TM1, 2, 3, 4 band reflectances, and one output node of suspended solid concentration (SSC) to retrieve SSC of Lake Taihu. The results demonstrated that BP neural net is very fit to quantitatively retrieve water quality of case II water with complex optic characteristic, and has much higher accuracy than the common linear model. A test was made and the results suggest that 13 had relative error (RE)RE of less than 30%, accounting for 81.25% of the total samples.

Original language	English
Pages (from-to)	683-686+735
Journal	Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University
Volume	31
Issue number	8
State	Published - 5 Aug 2006
Externally published	Yes

Keywords

BP neural net
Lake Taihu
Quantitative retrieval
Suspended solid concentration

Cite this

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title = "Quantitative retrieval of suspended solid concentration in Lake Taihu based on BP neural net",

abstract = "A two-layer BP neural net model is constructed with four input nodes of TM1, 2, 3, 4 band reflectances, and one output node of suspended solid concentration (SSC) to retrieve SSC of Lake Taihu. The results demonstrated that BP neural net is very fit to quantitatively retrieve water quality of case II water with complex optic characteristic, and has much higher accuracy than the common linear model. A test was made and the results suggest that 13 had relative error (RE)RE of less than 30\%, accounting for 81.25\% of the total samples.",

keywords = "BP neural net, Lake Taihu, Quantitative retrieval, Suspended solid concentration",

author = "Heng L{\"u} and Xinguo Li and Kai Cao",

year = "2006",

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journal = "Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University",

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TY - JOUR

T1 - Quantitative retrieval of suspended solid concentration in Lake Taihu based on BP neural net

AU - Lü, Heng

AU - Li, Xinguo

AU - Cao, Kai

PY - 2006/8/5

Y1 - 2006/8/5

N2 - A two-layer BP neural net model is constructed with four input nodes of TM1, 2, 3, 4 band reflectances, and one output node of suspended solid concentration (SSC) to retrieve SSC of Lake Taihu. The results demonstrated that BP neural net is very fit to quantitatively retrieve water quality of case II water with complex optic characteristic, and has much higher accuracy than the common linear model. A test was made and the results suggest that 13 had relative error (RE)RE of less than 30%, accounting for 81.25% of the total samples.

AB - A two-layer BP neural net model is constructed with four input nodes of TM1, 2, 3, 4 band reflectances, and one output node of suspended solid concentration (SSC) to retrieve SSC of Lake Taihu. The results demonstrated that BP neural net is very fit to quantitatively retrieve water quality of case II water with complex optic characteristic, and has much higher accuracy than the common linear model. A test was made and the results suggest that 13 had relative error (RE)RE of less than 30%, accounting for 81.25% of the total samples.

KW - BP neural net

KW - Lake Taihu

KW - Quantitative retrieval

KW - Suspended solid concentration

UR - https://www.scopus.com/pages/publications/33748681124

M3 - 文章

AN - SCOPUS:33748681124

SN - 1671-8860

VL - 31

SP - 683-686+735

JO - Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University

JF - Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University

IS - 8

ER -

Quantitative retrieval of suspended solid concentration in Lake Taihu based on BP neural net

Abstract

Keywords

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